From ba892757ecf074c4a5ffcf56aff3eb875c2c4c4f Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:29:21 +0200 Subject: [PATCH 01/19] Add generator for RMS to OPM Flow and ERT notebook --- .../generate_rms_agent_opm_ert_notebook.py | 1749 +++++++++++++++++ 1 file changed, 1749 insertions(+) create mode 100644 scripts/generate_rms_agent_opm_ert_notebook.py diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py new file mode 100644 index 00000000..00689bcc --- /dev/null +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -0,0 +1,1749 @@ +#!/usr/bin/env python3 +"""Generate the executed-source notebook for the RMS -> OPM Flow -> ERT example.""" + +from __future__ import annotations + +from pathlib import Path +import textwrap + +import nbformat as nbf + + +ROOT = Path(__file__).resolve().parents[1] +TARGET = ROOT / "notebooks" / "reservoir" / "rms_to_opm_flow_agent_ert.ipynb" +BASE_NOTEBOOK = ROOT / "notebooks" / "reservoir" / "neqsim_opm_flow_blackoil_coupling.ipynb" + +base = nbf.read(BASE_NOTEBOOK, as_version=4) + + +def source_cell(index: int) -> str: + return "".join(base.cells[index].source) + + +def md(source: str): + return nbf.v4.new_markdown_cell(textwrap.dedent(source).strip()) + + +def code(source: str): + return nbf.v4.new_code_cell(textwrap.dedent(source).strip()) + + +cells = [] + +cells.append(md(r""" +# From an RMS-origin reservoir model to OPM Flow, ERT, and NeqSim + +**A fully executed NeqSim-Colab reservoir example using the public Reek model** + +This notebook reads a real exported corner-point model whose public provenance identifies an +RMS project, audits every input by SHA-256, demonstrates 2 × 2 × 4 blocking and property +spreading, generates a complete OPM Flow black-oil case, runs the simulator, reads restart +states, runs a four-realization ERT ensemble, and transfers a selected result to a NeqSim +surface-process model. + +Open in Colab: +https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + +The stored outputs are evidence from a clean top-to-bottom run. Re-running will download the +same immutable public inputs and will build the current NeqSim Java master, recording its exact +commit and JAR digest. +""")) + +cells.append(md(r""" +## What this notebook proves + +By the end, the notebook has produced and checked: + +1. An immutable inventory of the public Reek ROFF files, including geometry and all properties. +2. A geological grid of 80 × 128 × 56 cells and a simulation grid of 40 × 64 × 14 cells. +3. Explicit 2 × 2 × 4 blocking, pore-volume-weighted porosity, and volume-majority facies. +4. Property maps, cross-sections, histograms, a 3-D reservoir view, and well screening. +5. NeqSim SRK fluid characterization and Flow-compatible PVTO, PVDG, PVTW, and DENSITY data. +6. A complete OPM Flow corner-point deck and a real dynamic simulation with restart maps. +7. A real ERT ensemble experiment in which porosity, permeability, and injection rate vary. +8. A NeqSim choke, separation, compression, cooling, and oil-letdown process driven by Flow rates. +9. A machine-readable job contract and tool-call ledger for a future RMS automation agent. + +**Important boundary.** RMS is commercial software and cannot be installed in a public Colab +runtime. The numerical data used here are genuine public ROFF exports from an RMS-origin Reek +model. The future-agent section shows how the same request is dispatched to a licensed, +allow-listed RMS worker. No proprietary project or license is copied into this notebook. +""")) + +cells.append(md(r""" +## End-to-end architecture and trust boundary + +The public teaching path and the future licensed path share the same versioned handover contract. + +| Stage | This executed notebook | Future production agent | +|---|---|---| +| RMS source | Public RMS-origin ROFF export | Licensed RMS worker | +| Grid/property API | XTGeo | RMS Python API plus XTGeo validation | +| Fluid model | NeqSim current Java master | Same | +| Dynamic model | OPM Flow 2026.04 | Same or approved simulator | +| Uncertainty | ERT 23.0.1 | ERT with governed storage/compute | +| Audit | Checksums, assertions, tool ledger | Signed job, identity, approvals, artifact registry | +""")) + +cells.append(code(r""" +import importlib.metadata +import os +from pathlib import Path +import re +import shutil +import subprocess +import sys + +REQUIRED_PACKAGES = { + "neqsim": "3.18.0", + "opm": "2026.4", + "xtgeo": "4.25.1", + "ert": "23.0.1", + "nbformat": "5.10.4", +} + +installed = {} +for package_name in REQUIRED_PACKAGES: + try: + installed[package_name] = importlib.metadata.version(package_name) + except importlib.metadata.PackageNotFoundError: + installed[package_name] = None + +requirements = [ + f"{name}=={required}" + for name, required in REQUIRED_PACKAGES.items() + if installed[name] != required +] +if requirements: + subprocess.run( + [sys.executable, "-m", "pip", "install", "--quiet", *requirements], + check=True, + timeout=1200, + ) + +FLOW_RUN_ENV = os.environ.copy() +FLOW_EXECUTABLE = os.environ.get("OPM_FLOW_EXECUTABLE") or shutil.which("flow") +if FLOW_EXECUTABLE is None: + os_release = Path("/etc/os-release").read_text(encoding="utf-8") + if "Ubuntu" not in os_release: + raise RuntimeError("OPM Flow binary installation requires an Ubuntu/Colab runtime.") + privilege = [] if os.geteuid() == 0 else ["sudo"] + commands = [ + privilege + ["apt-get", "update", "-qq"], + privilege + [ + "apt-get", "install", "-y", "-qq", "--no-install-recommends", + "software-properties-common", "mpi-default-bin", + ], + privilege + ["add-apt-repository", "-y", "ppa:opm/ppa"], + privilege + ["apt-get", "update", "-qq"], + privilege + [ + "apt-get", "install", "-y", "-qq", "--no-install-recommends", + "libopm-simulators-bin", + ], + ] + for command in commands: + result = subprocess.run(command, capture_output=True, text=True, timeout=1200) + if result.returncode != 0: + raise RuntimeError(result.stdout[-2000:] + "\n" + result.stderr[-4000:]) + FLOW_EXECUTABLE = shutil.which("flow") + +if FLOW_EXECUTABLE is None: + raise RuntimeError("The OPM Flow executable was not found.") + +flow_version_output = subprocess.run( + [FLOW_EXECUTABLE, "--version"], + check=True, + capture_output=True, + text=True, + timeout=60, +) +print(flow_version_output.stdout.strip() or flow_version_output.stderr.strip()) +print("Python packages installed:", REQUIRED_PACKAGES) +""")) + +cells.append(md(r""" +## Reproducible current-master NeqSim runtime + +The public PyPI NeqSim package supplies only the Python bridge. The Java runtime below is built +from the selected equinor/neqsim source ref. The resolved Git commit, JAR SHA-256, and loaded +class location are recorded before any thermodynamic calculation. Set NEQSIM_SOURCE_REF to use +another reviewed ref. Validation automation may provide NEQSIM_SOURCE_ROOT and +NEQSIM_SOURCE_JAR from an exact pre-built checkout. +""")) + +cells.append(code(r""" +import hashlib +import importlib.util +import jpype + + +def run_command(command, *, cwd=None, timeout=1800, environment=None): + result = subprocess.run( + command, + cwd=cwd, + env=environment, + text=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + timeout=timeout, + ) + if result.returncode != 0: + tail = "\n".join(result.stdout.splitlines()[-80:]) + raise RuntimeError(f"Command failed ({result.returncode}): {command}\n{tail}") + return result.stdout.strip() + + +NEQSIM_SOURCE_REF = os.environ.get("NEQSIM_SOURCE_REF", "master") +supplied_source = os.environ.get("NEQSIM_SOURCE_ROOT", "").strip() +supplied_jar = os.environ.get("NEQSIM_SOURCE_JAR", "").strip() + +if supplied_source and supplied_jar: + neqsim_source = Path(supplied_source).resolve() + neqsim_jar = Path(supplied_jar).resolve() +else: + build_root = Path("/content") if Path("/content").exists() else Path.cwd() + neqsim_source = build_root / "neqsim-java-master" + if not neqsim_source.exists(): + run_command( + [ + "git", "clone", "--depth", "1", "--branch", NEQSIM_SOURCE_REF, + "https://github.com/equinor/neqsim.git", str(neqsim_source), + ], + timeout=600, + ) + else: + run_command(["git", "fetch", "--depth", "1", "origin", NEQSIM_SOURCE_REF], + cwd=neqsim_source, timeout=600) + run_command(["git", "checkout", "--detach", "FETCH_HEAD"], cwd=neqsim_source) + + maven_settings = build_root / "neqsim-maven-settings.xml" + maven_settings.write_text( + "canonical-central" + "central" + "https://repo.maven.apache.org/maven2/" + "", + encoding="utf-8", + ) + run_command( + [ + "./mvnw", "-q", "-s", str(maven_settings), "-DskipTests", + "-Dmaven.javadoc.skip=true", "package", + ], + cwd=neqsim_source, + timeout=2400, + ) + built_jars = [ + path for path in (neqsim_source / "target").glob("neqsim-*.jar") + if "sources" not in path.name + and "javadoc" not in path.name + and not path.name.startswith("original-") + ] + if not built_jars: + raise FileNotFoundError("Maven completed but no NeqSim JAR was found.") + neqsim_jar = max(built_jars, key=lambda path: path.stat().st_size) + +if not neqsim_source.is_dir() or not neqsim_jar.is_file(): + raise FileNotFoundError("NeqSim source root or built JAR is missing.") + +neqsim_commit = run_command(["git", "rev-parse", "HEAD"], cwd=neqsim_source) +neqsim_jar_sha256 = hashlib.sha256(neqsim_jar.read_bytes()).hexdigest() + +os.environ["NEQSIM_JVM_AUTOSTART"] = "0" +if not jpype.isJVMStarted(): + jpype.addClassPath(str(neqsim_jar)) + neqsim_package = importlib.util.find_spec("neqsim") + if neqsim_package is None or not neqsim_package.submodule_search_locations: + raise ImportError("The public-PyPI neqsim bridge is not installed.") + neqsim_python_root = Path(next(iter(neqsim_package.submodule_search_locations))) + for runtime_jar in sorted((neqsim_python_root / "lib").glob("*.jar")): + if runtime_jar.resolve() != neqsim_jar: + jpype.addClassPath(str(runtime_jar)) + jpype.startJVM() + +SystemSrkEos = jpype.JClass("neqsim.thermo.system.SystemSrkEos") +class_source = str( + SystemSrkEos.class_.getProtectionDomain().getCodeSource().getLocation() +) +if neqsim_jar.name not in class_source: + raise RuntimeError("NeqSim classes were not loaded from the source-built JAR.") + +print("NeqSim source ref:", NEQSIM_SOURCE_REF) +print("NeqSim resolved commit:", neqsim_commit) +print("NeqSim JAR SHA-256:", neqsim_jar_sha256) +print("Loaded class source:", class_source) +""")) + +cells.append(code(r""" +from importlib.metadata import version +import json +import math +import platform +import textwrap +import time +from urllib.request import urlretrieve + +import matplotlib.pyplot as plt +from matplotlib.colors import LogNorm +import numpy as np +import pandas as pd +import xtgeo +from jpype import JClass +from neqsim import jneqsim +from neqsim.process.processTools import ( + clearProcess, compressor, cooler, mixer, runProcess, separator, + separator3phase, stream, valve, +) +from neqsim.thermo import TPflash +from opm.io import Parser +from opm.io.ecl import EclFile, EGrid, ERst, ESmry + +plt.style.use("seaborn-v0_8-whitegrid") +pd.set_option("display.max_columns", 40) +pd.set_option("display.width", 160) +pd.set_option("display.max_rows", 120) + +RANDOM_SEED = 20260831 +RESERVOIR_TEMPERATURE_C = 97.5 +RESERVOIR_TEMPERATURE_K = RESERVOIR_TEMPERATURE_C + 273.15 +STANDARD_TEMPERATURE_C = 15.0 +STANDARD_PRESSURE_BARA = 1.01325 +INITIAL_RESERVOIR_PRESSURE_BARA = 260.0 +PRODUCER_BHP_LIMIT_BARA = 80.0 +SECONDS_PER_DAY = 86400.0 +DLE_PRESSURES_BARA = np.array([300.0, 250.0, 220.0, 200.0, 180.0, 150.0, 100.0, 50.0, 1.01325]) +GAS_PVT_PRESSURES_BARA = np.array([1.01325, 25.0, 50.0, 75.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0]) + +OUTPUT_DIRECTORY = Path("rms_to_opm_outputs").resolve() +DATA_DIRECTORY = OUTPUT_DIRECTORY / "input" +ERT_DIRECTORY = OUTPUT_DIRECTORY / "ert" +if OUTPUT_DIRECTORY.exists(): + shutil.rmtree(OUTPUT_DIRECTORY) +DATA_DIRECTORY.mkdir(parents=True) +ERT_DIRECTORY.mkdir(parents=True) +FIGURE_PATHS = [] + +runtime_table = pd.DataFrame({ + "runtime": ["Python", "Java", "NeqSim Python bridge", "NeqSim Java", "XTGeo", "OPM Python", "OPM Flow", "ERT"], + "version or identity": [ + platform.python_version(), + subprocess.run(["java", "-version"], capture_output=True, text=True, check=True).stderr.splitlines()[0], + version("neqsim"), + neqsim_commit[:12], + version("xtgeo"), + version("opm"), + (flow_version_output.stdout or flow_version_output.stderr).strip().splitlines()[0], + version("ert"), + ], +}) +display(runtime_table) +""")) + +cells.append(code(r""" +architecture_figure, axis = plt.subplots(figsize=(13.5, 5.0)) +axis.set_xlim(0, 13.5) +axis.set_ylim(0, 5.0) +axis.axis("off") + +nodes = [ + (0.3, 2.0, 2.1, 1.1, "RMS-origin\nROFF export", "#5B8FF9"), + (2.9, 2.0, 2.1, 1.1, "XTGeo\nQC + blocking", "#61DDAA"), + (5.5, 3.25, 2.1, 1.1, "NeqSim\nPVT", "#F6BD16"), + (5.5, 0.75, 2.1, 1.1, "Agent job\ncontract", "#9270CA"), + (8.1, 2.0, 2.1, 1.1, "OPM Flow\nsimulation", "#E8684A"), + (10.8, 3.25, 2.1, 1.1, "ERT\nensemble", "#6DC8EC"), + (10.8, 0.75, 2.1, 1.1, "NeqSim\nfacilities", "#FF9D4D"), +] +for x, y, width, height, label, color in nodes: + axis.add_patch(plt.Rectangle((x, y), width, height, facecolor=color, edgecolor="#263238", linewidth=1.4)) + axis.text(x + width / 2, y + height / 2, label, ha="center", va="center", color="white", weight="bold", fontsize=10) + +arrows = [ + ((2.4, 2.55), (2.9, 2.55)), + ((5.0, 2.55), (8.1, 2.55)), + ((7.6, 3.8), (8.5, 3.1)), + ((7.6, 1.3), (8.5, 2.0)), + ((10.2, 2.75), (10.8, 3.55)), + ((10.2, 2.25), (10.8, 1.55)), +] +for start, end in arrows: + axis.annotate("", xy=end, xytext=start, arrowprops={"arrowstyle": "->", "linewidth": 2, "color": "#263238"}) + +axis.text(1.35, 1.55, "public fixture", ha="center", color="#455A64") +axis.text(6.55, 4.55, "fluid contract", ha="center", color="#455A64") +axis.text(6.55, 0.30, "governed execution", ha="center", color="#455A64") +axis.set_title("Executed public path and reusable production-agent boundary", fontsize=15, weight="bold") +path = OUTPUT_DIRECTORY / "workflow_architecture.png" +architecture_figure.savefig(path, dpi=160, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 1. Immutable public RMS-origin inputs + +The data come from equinor/xtgeo-testdata at commit +cad17f24e22c19c6cefe6f647185395cc0a11add under LGPL-3.0. Its Reek readme states that the data +were taken from an RMS project. The public files contain no license, credentials, or private +asset paths. + +The geological ROFF contains geometry plus Poro, EQLNUM, and Facies. The simulation ROFF +contains the blocked grid. Separate simulation property files provide porosity, permeability, +facies, and zone. Every byte used below is downloaded from the immutable commit and checked. +""")) + +cells.append(code(r""" +DATA_COMMIT = "cad17f24e22c19c6cefe6f647185395cc0a11add" +DATA_BASE = f"https://raw.githubusercontent.com/equinor/xtgeo-testdata/{DATA_COMMIT}/3dgrids/reek" +DATA_MANIFEST = [ + ("0readme.txt", "8f76ce966e77a13cdeb1be9bf021b330719f34ac51d810569fc8e27df05cedcd", 149), + ("reek_geo2_grid_3props.roff", "cf199d7126dc96b574e05c6709a5216f454e2da7ff5e017bc7a503a6ab1a165c", 7837496), + ("reek_sim_grid.roff", "6454b7aa1d1f2701438310b8b3dc3101e70dd9643b49ec2f837a257e802ea90d", 376580), + ("reek_sim_poro.roff", "7368cc75d0436c3816fd16d61f77621c00f4f97ec25f0fcc2a7b0ddfe3cb3c14", 143718), + ("reek_sim_permx.roff", "37866e0fb8d9ab8e036f2ed9d34537a7274efbf63466526942e7da34ff297b53", 143719), + ("reek_sim_facies2.roff", "6aeb52f6dd41f1e0783fb998a92b3da08cfa0f8fd5621f537b4b10e4903f9bb9", 143786), + ("reek_sim_zone.roff", "1d8f60577a4da72b6234eb036e8d1d0248513fb93d61b955e58fab3563521baa", 143825), +] + +download_rows = [] +for filename, expected_sha256, expected_bytes in DATA_MANIFEST: + destination = DATA_DIRECTORY / filename + if not destination.exists(): + urlretrieve(f"{DATA_BASE}/{filename}", destination) + actual_bytes = destination.stat().st_size + actual_sha256 = hashlib.sha256(destination.read_bytes()).hexdigest() + verified = actual_sha256 == expected_sha256 and actual_bytes == expected_bytes + if not verified: + raise RuntimeError(f"Integrity check failed for {filename}") + download_rows.append({ + "file": filename, + "bytes": actual_bytes, + "sha256": actual_sha256, + "verified": verified, + "immutable URL": f"{DATA_BASE}/{filename}", + }) + +download_table = pd.DataFrame(download_rows) +display(download_table) +print((DATA_DIRECTORY / "0readme.txt").read_text(encoding="utf-8")) +""")) + +cells.append(md(r""" +## 2. Read the geological and simulation grids with XTGeo + +ROFF is the native handover format in this example. XTGeo reads both corner-point geometry and +properties while retaining the I, J, K ordering and inactive-cell masks. The fine geological +model is not sent directly to Flow. It is compared with the supplied simulation-scale export so +that the blocking rules are visible and testable. +""")) + +cells.append(code(r""" +fine_grid_path = DATA_DIRECTORY / "reek_geo2_grid_3props.roff" +sim_grid_path = DATA_DIRECTORY / "reek_sim_grid.roff" + +fine_grid = xtgeo.grid_from_file(fine_grid_path) +fine_properties_collection = xtgeo.gridproperties_from_file( + fine_grid_path, + names=["Poro", "EQLNUM", "Facies"], + grid=fine_grid, +) +fine_properties = {prop.name.lower(): prop for prop in fine_properties_collection.props} + +sim_grid = xtgeo.grid_from_file(sim_grid_path) +sim_poro = xtgeo.gridproperty_from_file(DATA_DIRECTORY / "reek_sim_poro.roff", grid=sim_grid) +sim_permx = xtgeo.gridproperty_from_file(DATA_DIRECTORY / "reek_sim_permx.roff", grid=sim_grid) +sim_facies = xtgeo.gridproperty_from_file(DATA_DIRECTORY / "reek_sim_facies2.roff", grid=sim_grid) +sim_zone = xtgeo.gridproperty_from_file(DATA_DIRECTORY / "reek_sim_zone.roff", grid=sim_grid) +sim_poro.name = "PORO" +sim_permx.name = "PERMX" +sim_facies.name = "FACIES" +sim_zone.name = "FIPNUM" + +fine_poro = fine_properties["poro"] +fine_eqlnum = fine_properties["eqlnum"] +fine_facies = fine_properties["facies"] + +grid_table = pd.DataFrame([ + { + "grid": "geological", + "NI": fine_grid.ncol, + "NJ": fine_grid.nrow, + "NK": fine_grid.nlay, + "total cells": fine_grid.ntotal, + "active cells": fine_grid.nactive, + }, + { + "grid": "simulation", + "NI": sim_grid.ncol, + "NJ": sim_grid.nrow, + "NK": sim_grid.nlay, + "total cells": sim_grid.ntotal, + "active cells": sim_grid.nactive, + }, +]) +display(grid_table) +""")) + +cells.append(code(r""" +property_objects = { + "fine Poro": fine_poro, + "fine EQLNUM": fine_eqlnum, + "fine Facies": fine_facies, + "simulation PORO": sim_poro, + "simulation PERMX [mD]": sim_permx, + "simulation FACIES": sim_facies, + "simulation Zone": sim_zone, +} +summary_rows = [] +for label, prop in property_objects.items(): + values = prop.values + summary_rows.append({ + "property": label, + "minimum": float(np.ma.min(values)), + "mean": float(np.ma.mean(values)), + "maximum": float(np.ma.max(values)), + "defined cells": int(values.count()), + "discrete": bool(prop.isdiscrete), + "codes": ", ".join(str(int(value)) for value in np.unique(values.compressed())[:12]) if prop.isdiscrete else "", + }) +property_summary = pd.DataFrame(summary_rows) +display(property_summary.round(6)) + +sim_x, sim_y, sim_z = sim_grid.get_xyz() +sim_dz = sim_grid.get_dz() +coordinate_table = pd.DataFrame({ + "quantity": ["X", "Y", "depth", "cell thickness"], + "minimum": [ + float(np.ma.min(sim_x.values)), float(np.ma.min(sim_y.values)), + float(np.ma.min(sim_z.values)), float(np.ma.min(sim_dz.values)), + ], + "mean": [ + float(np.ma.mean(sim_x.values)), float(np.ma.mean(sim_y.values)), + float(np.ma.mean(sim_z.values)), float(np.ma.mean(sim_dz.values)), + ], + "maximum": [ + float(np.ma.max(sim_x.values)), float(np.ma.max(sim_y.values)), + float(np.ma.max(sim_z.values)), float(np.ma.max(sim_dz.values)), + ], + "unit": ["m", "m", "m TVDSS", "m"], +}) +display(coordinate_table.round(3)) +""")) + +cells.append(code(r""" +fine_x, fine_y, fine_z = fine_grid.get_xyz() +fine_top = np.ma.filled(fine_z.values[:, :, 0], np.nan) +sim_top = np.ma.filled(sim_z.values[:, :, 0], np.nan) +sim_poro_map = np.nanmean(np.ma.filled(sim_poro.values, np.nan), axis=2) +sim_logk_map = np.nanmean(np.log10(np.ma.filled(sim_permx.values, np.nan)), axis=2) + +structure_figure, axes = plt.subplots(2, 2, figsize=(13.5, 9.0), constrained_layout=True) +items = [ + (fine_top.T, "Geological-grid top depth", "viridis_r", "m TVDSS"), + (sim_top.T, "Simulation-grid top depth", "viridis_r", "m TVDSS"), + (sim_poro_map.T, "Simulation-grid mean porosity", "YlGnBu", "fraction"), + (sim_logk_map.T, "Simulation-grid mean log10(PERMX)", "magma", "log10(mD)"), +] +for axis, (array, title, cmap, unit) in zip(axes.ravel(), items): + image = axis.imshow(array, origin="lower", aspect="auto", cmap=cmap) + axis.set_title(title) + axis.set_xlabel("I column") + axis.set_ylabel("J row") + structure_figure.colorbar(image, ax=axis, label=unit, shrink=0.82) + +path = OUTPUT_DIRECTORY / "reek_structure_and_static_maps.png" +structure_figure.savefig(path, dpi=165, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(code(r""" +active_poro = sim_poro.values.compressed() +active_permx = sim_permx.values.compressed() +active_facies = sim_facies.values.compressed().astype(int) + +distribution_figure, axes = plt.subplots(1, 3, figsize=(14.5, 4.4), constrained_layout=True) +axes[0].hist(active_poro, bins=30, color="#007F87", edgecolor="white") +axes[0].set(xlabel="Porosity [-]", ylabel="Active cells", title="Simulation porosity") +axes[1].hist(active_permx, bins=np.logspace(np.log10(active_permx.min()), np.log10(active_permx.max()), 32), + color="#C75B12", edgecolor="white") +axes[1].set_xscale("log") +axes[1].set(xlabel="PERMX [mD]", ylabel="Active cells", title="Permeability") +scatter = axes[2].scatter(active_poro, active_permx, c=active_facies, cmap="viridis", + s=7, alpha=0.28, rasterized=True) +axes[2].set_yscale("log") +axes[2].set(xlabel="Porosity [-]", ylabel="PERMX [mD]", title="Rock-property cross-plot") +distribution_figure.colorbar(scatter, ax=axes[2], label="Facies code") +path = OUTPUT_DIRECTORY / "reek_property_distributions.png" +distribution_figure.savefig(path, dpi=165, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 3. Blocking and spreading properties + +The dimensions reveal an exact 2 × 2 × 4 relationship: + +$$ +(80,128,56) \rightarrow (40,64,14). +$$ + +For block B, porosity is pore-volume weighted: + +$$ +\phi_B = \frac{\sum_{c\in B} \phi_c V_c A_c} +{\sum_{c\in B} V_c A_c}, +$$ + +where V is bulk volume and A is the active-cell indicator. Facies is assigned by the largest +active bulk volume in the block. For teaching, fine Background, Channel, and Crevasse codes are +mapped to simulation SHALE, COARSESAND, and FINESAND codes. This mapping is explicit and is not +claimed to reproduce every hidden RMS workflow setting. + +Permeability is not arithmetically averaged here. The supplied simulation PERMX is used because +flow upscaling is directional and depends on boundary conditions. The future RMS job contract +therefore names the approved permeability-upscaling workflow rather than silently inventing one. +""")) + +cells.append(code(r""" +BLOCK = (2, 2, 4) +assert ( + fine_grid.ncol // sim_grid.ncol, + fine_grid.nrow // sim_grid.nrow, + fine_grid.nlay // sim_grid.nlay, +) == BLOCK + +fine_actnum = np.ma.filled(fine_grid.get_actnum().values, 0).astype(bool) +fine_bulk_volume = np.ma.filled(fine_grid.get_bulk_volume().values, 0.0) +fine_poro_values = np.ma.filled(fine_poro.values, 0.0) +fine_facies_values = np.ma.filled(fine_facies.values, 0).astype(int) + +def block_sum(array): + reshaped = array.reshape( + sim_grid.ncol, BLOCK[0], + sim_grid.nrow, BLOCK[1], + sim_grid.nlay, BLOCK[2], + ) + return reshaped.sum(axis=(1, 3, 5)) + +active_volume = fine_bulk_volume * fine_actnum +blocked_volume = block_sum(active_volume) +blocked_poro = np.divide( + block_sum(fine_poro_values * active_volume), + blocked_volume, + out=np.full_like(blocked_volume, np.nan, dtype=float), + where=blocked_volume > 0.0, +) + +facies_volume = np.stack([ + block_sum(active_volume * (fine_facies_values == code_value)) + for code_value in [0, 1, 2] +]) +fine_majority_facies = np.argmax(facies_volume, axis=0) +fine_to_sim_facies = np.array([0, 2, 1]) +blocked_facies = fine_to_sim_facies[fine_majority_facies] + +sim_actnum = np.ma.filled(sim_grid.get_actnum().values, 0).astype(bool) +sim_poro_values = np.ma.filled(sim_poro.values, np.nan) +sim_facies_values = np.ma.filled(sim_facies.values, -1).astype(int) +comparison_mask = sim_actnum & np.isfinite(blocked_poro) & np.isfinite(sim_poro_values) + +poro_difference = blocked_poro[comparison_mask] - sim_poro_values[comparison_mask] +blocking_metrics = pd.DataFrame({ + "metric": [ + "fine cells per simulation block", + "porosity RMSE", + "porosity mean bias", + "porosity correlation", + "facies agreement after teaching map", + "compared active blocks", + ], + "value": [ + int(np.prod(BLOCK)), + float(np.sqrt(np.mean(poro_difference ** 2))), + float(np.mean(poro_difference)), + float(np.corrcoef(blocked_poro[comparison_mask], sim_poro_values[comparison_mask])[0, 1]), + float(np.mean(blocked_facies[comparison_mask] == sim_facies_values[comparison_mask])), + int(comparison_mask.sum()), + ], + "unit": ["fine cells", "fraction", "fraction", "correlation", "fraction", "blocks"], +}) +display(blocking_metrics.round(6)) +""")) + +cells.append(code(r""" +layer_index = sim_grid.nlay // 2 +fine_layer_start = layer_index * BLOCK[2] +fine_layer_mean = np.nanmean( + np.ma.filled(fine_poro.values[:, :, fine_layer_start:fine_layer_start + BLOCK[2]], np.nan), + axis=2, +) + +blocking_figure, axes = plt.subplots(2, 2, figsize=(13.0, 9.0), constrained_layout=True) +arrays = [ + (fine_layer_mean.T, "Fine porosity: four layers before blocking", "YlGnBu", 0.0, 0.40), + (blocked_poro[:, :, layer_index].T, "Calculated PV-weighted blocked porosity", "YlGnBu", 0.0, 0.40), + (sim_poro_values[:, :, layer_index].T, "Supplied RMS simulation porosity", "YlGnBu", 0.0, 0.40), + ((blocked_poro[:, :, layer_index] - sim_poro_values[:, :, layer_index]).T, + "Calculated minus supplied", "coolwarm", -0.12, 0.12), +] +for axis, (array, title, cmap, vmin, vmax) in zip(axes.ravel(), arrays): + image = axis.imshow(array, origin="lower", aspect="auto", cmap=cmap, vmin=vmin, vmax=vmax) + axis.set(title=title, xlabel="I column", ylabel="J row") + blocking_figure.colorbar(image, ax=axis, shrink=0.82) +blocking_figure.suptitle(f"Blocking audit for simulation layer {layer_index + 1}", fontsize=15) +path = OUTPUT_DIRECTORY / "reek_blocking_comparison.png" +blocking_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(code(r""" +spread_figure, axes = plt.subplots(2, 3, figsize=(15.0, 8.5), constrained_layout=True) +selected_layers = [1, sim_grid.nlay // 2] +facies_colors = "viridis" +for row, k_index in enumerate(selected_layers): + facies_image = axes[row, 0].imshow(sim_facies_values[:, :, k_index].T, origin="lower", + aspect="auto", cmap=facies_colors, vmin=0, vmax=2) + axes[row, 0].set_title(f"Facies, layer {k_index + 1}") + poro_image = axes[row, 1].imshow(sim_poro_values[:, :, k_index].T, origin="lower", + aspect="auto", cmap="YlGnBu", vmin=0.0, vmax=0.4) + axes[row, 1].set_title(f"PORO, layer {k_index + 1}") + perm_image = axes[row, 2].imshow( + np.log10(np.ma.filled(sim_permx.values[:, :, k_index], np.nan)).T, + origin="lower", aspect="auto", cmap="magma", vmin=-1, vmax=4, + ) + axes[row, 2].set_title(f"log10(PERMX/mD), layer {k_index + 1}") + for axis in axes[row]: + axis.set(xlabel="I column", ylabel="J row") +spread_figure.colorbar(facies_image, ax=axes[:, 0], label="Facies code", shrink=0.85) +spread_figure.colorbar(poro_image, ax=axes[:, 1], label="Porosity [-]", shrink=0.85) +spread_figure.colorbar(perm_image, ax=axes[:, 2], label="log10(mD)", shrink=0.85) +path = OUTPUT_DIRECTORY / "reek_property_spreading_layers.png" +spread_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 4. Screen an injector–producer pair + +A transparent screening score is used only to place demonstration wells. It combines column +hydrocarbon pore volume with a logarithmic permeability-thickness proxy. The producer uses the +best column. The injector is selected among high-score columns 8–14 Manhattan grid steps away, +which makes the displacement visible without claiming an optimized field-development plan. +Only active layers are completed. +""")) + +cells.append(code(r""" +sim_bulk_volume = np.ma.filled(sim_grid.get_bulk_volume().values, 0.0) +sim_perm_values = np.ma.filled(sim_permx.values, 0.0) +INITIAL_WATER_SATURATION = 0.18 +column_hcpv = np.sum(sim_bulk_volume * sim_poro_values * (1.0 - INITIAL_WATER_SATURATION), axis=2) +column_kh = np.sum(sim_perm_values * np.ma.filled(sim_dz.values, 0.0), axis=2) +screening_score = column_hcpv * np.log1p(column_kh) +screening_score[~np.any(sim_actnum, axis=2)] = -np.inf + +producer_flat = int(np.nanargmax(screening_score)) +producer_i, producer_j = np.unravel_index(producer_flat, screening_score.shape) + +candidate_order = np.argsort(screening_score.ravel())[::-1] +injector_i = injector_j = None +for flat_index in candidate_order: + i_index, j_index = np.unravel_index(int(flat_index), screening_score.shape) + distance = abs(i_index - producer_i) + abs(j_index - producer_j) + if 8 <= distance <= 14 and np.isfinite(screening_score[i_index, j_index]): + injector_i, injector_j = i_index, j_index + break +if injector_i is None: + raise RuntimeError("No suitable injector column was found.") + +producer_layers = np.flatnonzero(sim_actnum[producer_i, producer_j, :]) + 1 +injector_layers = np.flatnonzero(sim_actnum[injector_i, injector_j, :]) + 1 + +well_table = pd.DataFrame([ + { + "well": "PROD", "I": producer_i + 1, "J": producer_j + 1, + "first K": int(producer_layers.min()), "last K": int(producer_layers.max()), + "active completions": len(producer_layers), + "column HCPV [million rm3]": column_hcpv[producer_i, producer_j] / 1e6, + "score": screening_score[producer_i, producer_j], + }, + { + "well": "WINJ", "I": injector_i + 1, "J": injector_j + 1, + "first K": int(injector_layers.min()), "last K": int(injector_layers.max()), + "active completions": len(injector_layers), + "column HCPV [million rm3]": column_hcpv[injector_i, injector_j] / 1e6, + "score": screening_score[injector_i, injector_j], + }, +]) +display(well_table.round(3)) +""")) + +cells.append(code(r""" +well_figure, axis = plt.subplots(figsize=(10.5, 7.0), constrained_layout=True) +image = axis.imshow((column_hcpv / 1e6).T, origin="lower", aspect="auto", cmap="viridis") +axis.scatter(producer_i, producer_j, marker="v", s=180, color="#D55E00", + edgecolor="white", linewidth=1.3, label="PROD") +axis.scatter(injector_i, injector_j, marker="^", s=180, color="#0072B2", + edgecolor="white", linewidth=1.3, label="WINJ") +axis.plot([producer_i, injector_i], [producer_j, injector_j], color="white", linestyle="--", linewidth=1.8) +axis.set(xlabel="I column", ylabel="J row", title="Hydrocarbon pore-volume screen and selected wells") +axis.legend() +well_figure.colorbar(image, ax=axis, label="Column HCPV [million reservoir m3]") +path = OUTPUT_DIRECTORY / "reek_well_screening.png" +well_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 5. Generate black-oil PVT with NeqSim + +The composition below is fully visible. NeqSim characterizes the C20+ fraction into twelve +pseudo-components, runs a differential-liberation calculation, derives gas and water +properties, and emits strict Flow tables. The PVT input and every generated table row are +printed, not hidden behind a pre-built include file. +""")) + +for index in [8, 9, 11, 12, 14, 15]: + cell_source = source_cell(index) + if index == 12: + cell_source += "\nFIGURE_PATHS.append(pvt_plot_path)" + cells.append(code(cell_source)) + +pvt_include_source = source_cell(16) +pvt_include_source += r""" + +print("\nComplete generated NEQSIM_PVT.INC:\n") +print(black_oil_include) +""" +cells.append(code(pvt_include_source)) + +cells.append(md(r""" +## 6. Export Flow grid and static properties + +The OPM model uses the supplied RMS simulation-scale geometry and properties. The exact modeling +assumptions are: + +- PORO and PERMX: supplied public simulation properties; +- PERMY = 0.70 × PERMX; +- PERMZ = 0.10 × PERMX; +- FIPNUM: supplied Zone property; +- initial saturation: equilibrium plus the SWOF endpoint; +- relative permeability: the complete tables printed below. + +Each GRDECL include is written by XTGeo and hashed. These generated files plus the immutable ROFF +sources make the complete numerical input reproducible without printing hundreds of thousands +of cell values into the browser. +""")) + +cells.append(code(r""" +relative_permeability_tables = """ +SWOF + 0.12 0.00000 1.0000 0.0 + 0.20 0.00010 0.8500 0.0 + 0.30 0.00100 0.6200 0.0 + 0.40 0.00800 0.4000 0.0 + 0.50 0.03000 0.2200 0.0 + 0.60 0.09000 0.1000 0.0 + 0.70 0.22000 0.0300 0.0 + 0.80 0.48000 0.0050 0.0 + 0.88 1.00000 0.0000 0.0 / + +SGOF + 0.00 0.0000 1.0000 0.0 + 0.05 0.0020 0.9300 0.0 + 0.10 0.0100 0.8000 0.0 + 0.20 0.0600 0.5500 0.0 + 0.30 0.1600 0.3200 0.0 + 0.40 0.3300 0.1500 0.0 + 0.50 0.5500 0.0500 0.0 + 0.60 0.7800 0.0100 0.0 + 0.70 0.9300 0.0010 0.0 + 0.88 1.0000 0.0000 0.0 / +""".strip() +print(relative_permeability_tables) + +grid_include_path = OUTPUT_DIRECTORY / "REEK_GRID.GRDECL" +sim_grid.to_file(grid_include_path, fformat="grdecl") + +def export_grid_property(source_property, keyword, multiplier=1.0): + exported = source_property.copy() + exported.name = keyword + exported.values = source_property.values * multiplier + output_path = OUTPUT_DIRECTORY / f"{keyword}.GRDECL" + exported.to_file(output_path, fformat="grdecl") + return output_path + +poro_path = export_grid_property(sim_poro, "PORO") +permx_path = export_grid_property(sim_permx, "PERMX") +permy_path = export_grid_property(sim_permx, "PERMY", 0.70) +permz_path = export_grid_property(sim_permx, "PERMZ", 0.10) +fipnum_path = export_grid_property(sim_zone, "FIPNUM") + +generated_static_paths = [grid_include_path, poro_path, permx_path, permy_path, permz_path, fipnum_path] +generated_static_table = pd.DataFrame([ + { + "file": path.name, + "bytes": path.stat().st_size, + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + } + for path in generated_static_paths +]) +display(generated_static_table) +print("\nGrid include header:\n", "\n".join(grid_include_path.read_text(encoding="utf-8").splitlines()[:24])) +""")) + +cells.append(code(r""" +minimum_depth = float(np.ma.min(sim_z.values)) +maximum_depth = float(np.ma.max(sim_z.values)) +datum_depth = float(np.ma.mean(sim_z.values)) +water_contact_depth = maximum_depth + 100.0 +gas_contact_depth = minimum_depth - 100.0 +initial_rs_sm3_sm3 = saturated_oil_rows[-1]["Rs"] + +def completion_lines(well_name, i_index, j_index, layers): + return "\n".join( + f" '{well_name}' {i_index + 1} {j_index + 1} {int(k)} {int(k)} 'OPEN' 1* 1* 0.20 /" + for k in layers + ) + +def render_deck(perm_multiplier, poro_multiplier, injection_rate): + return f""" +RUNSPEC +TITLE + PUBLIC RMS-ORIGIN REEK MODEL: NEQSIM PVT, OPM FLOW, ERT + +DIMENS + {sim_grid.ncol} {sim_grid.nrow} {sim_grid.nlay} / + +OIL +GAS +WATER +DISGAS +METRIC + +START + 1 'JAN' 2025 / + +WELLDIMS + 2 {max(len(producer_layers), len(injector_layers))} 1 2 / + +TABDIMS +/ + +EQLDIMS +/ + +UNIFOUT + +GRID +INIT +INCLUDE + '{grid_include_path.as_posix()}' / +INCLUDE + '{poro_path.as_posix()}' / +INCLUDE + '{permx_path.as_posix()}' / +INCLUDE + '{permy_path.as_posix()}' / +INCLUDE + '{permz_path.as_posix()}' / +INCLUDE + '{fipnum_path.as_posix()}' / + +EDIT +MULTIPLY + PORO {poro_multiplier} / + PERMX {perm_multiplier} / + PERMY {perm_multiplier} / + PERMZ {perm_multiplier} / +/ + +PROPS +INCLUDE + '{black_oil_path.resolve().as_posix()}' / + +ROCK + 260.0 4.0E-5 / + +{relative_permeability_tables} + +SOLUTION +EQUIL + {datum_depth:.6f} {INITIAL_RESERVOIR_PRESSURE_BARA:.6f} + {water_contact_depth:.6f} 0.0 {gas_contact_depth:.6f} 0.0 1 0 0 / + +RSVD + {minimum_depth:.6f} {initial_rs_sm3_sm3:.8f} + {maximum_depth:.6f} {initial_rs_sm3_sm3:.8f} / + +SUMMARY +FPR +FOPR +FGPR +FWPR +FWIR +FOPT +FGPT +FWPT +WBHP + 'PROD' 'WINJ' / +WGOR + 'PROD' / + +SCHEDULE +RPTRST + 'BASIC=1' / + +GRUPTREE + 'WELLS' 'FIELD' / +/ + +WELSPECS + 'PROD' 'WELLS' {producer_i + 1} {producer_j + 1} {datum_depth:.3f} 'OIL' / + 'WINJ' 'WELLS' {injector_i + 1} {injector_j + 1} {datum_depth:.3f} 'WATER' / +/ + +COMPDAT +{completion_lines("PROD", producer_i, producer_j, producer_layers)} +{completion_lines("WINJ", injector_i, injector_j, injector_layers)} +/ + +WCONPROD + 'PROD' 'OPEN' 'ORAT' 10000.0 4* {PRODUCER_BHP_LIMIT_BARA:.3f} / + +WCONINJE + 'WINJ' 'WATER' 'OPEN' 'RATE' {injection_rate} 1* 320.0 / + +TSTEP + 24*30.4375 / + +END +""".strip() + +deck_text = render_deck(1.0, 1.0, 12000.0) +deck_path = OUTPUT_DIRECTORY / "RMS_REEK_BASE.DATA" +deck_path.write_text(deck_text + "\n", encoding="utf-8") +print(deck_text) +""")) + +cells.append(code(r""" +parsed_deck = Parser().parse(str(deck_path)) +required_keywords = [ + "DIMENS", "COORD", "ZCORN", "ACTNUM", "PORO", "PERMX", "PERMY", "PERMZ", + "DENSITY", "PVTO", "PVDG", "PVTW", "EQUIL", "WELSPECS", "COMPDAT", + "WCONPROD", "WCONINJE", +] +deck_keyword_audit = pd.DataFrame({ + "keyword": required_keywords, + "present": [keyword in parsed_deck for keyword in required_keywords], +}) +oil_table_audit = pd.DataFrame(saturated_oil_rows) +pvt_contract_audit = pd.DataFrame({ + "contract": [ + "saturated Rs strictly increases", + "bubble pressure strictly increases", + "dry-gas pressure strictly increases", + "dry-gas Bg strictly decreases", + "no invalid numeric tokens", + ], + "passed": [ + np.all(np.diff(oil_table_audit["Rs"]) > 0), + np.all(np.diff(oil_table_audit["pressure"]) > 0), + np.all(np.diff(dry_gas_table["pressure_bara"]) > 0), + np.all(np.diff(dry_gas_table["Bg_rm3_Sm3"]) < 0), + not any(token in black_oil_include for token in ["NaN", "Infinity", "-Infinity"]), + ], +}) +display(deck_keyword_audit) +display(pvt_contract_audit) +if not deck_keyword_audit["present"].all() or not pvt_contract_audit["passed"].all(): + raise AssertionError("Deck or PVT contract validation failed.") +print(f"OPM parser accepted {len(parsed_deck)} expanded keywords.") +""")) + +cells.append(md(r""" +## 7. Run OPM Flow and retain diagnostics + +This is the real simulator invocation. It is not a surrogate decline curve. The return code, +reported timing, output files, and log tail are retained. A failed simulator call raises an +exception and prevents publication of an apparently successful notebook. +""")) + +cells.append(code(r""" +flow_start = time.perf_counter() +flow_run = subprocess.run( + [FLOW_EXECUTABLE, deck_path.name], + cwd=OUTPUT_DIRECTORY, + env=FLOW_RUN_ENV, + capture_output=True, + text=True, + timeout=1200, +) +flow_elapsed = time.perf_counter() - flow_start +if flow_run.returncode != 0: + raise RuntimeError( + "OPM Flow failed:\n" + flow_run.stdout[-8000:] + "\n" + flow_run.stderr[-4000:] + ) +flow_log_tail = "\n".join(flow_run.stdout.splitlines()[-35:]) +flow_diagnostics = pd.DataFrame({ + "diagnostic": ["return code", "wall time", "SMSPEC exists", "UNRST exists", "PRT exists"], + "value": [ + flow_run.returncode, + flow_elapsed, + (OUTPUT_DIRECTORY / "RMS_REEK_BASE.SMSPEC").exists(), + (OUTPUT_DIRECTORY / "RMS_REEK_BASE.UNRST").exists(), + (OUTPUT_DIRECTORY / "RMS_REEK_BASE.PRT").exists(), + ], + "unit": ["-", "s", "boolean", "boolean", "boolean"], +}) +display(flow_diagnostics) +print(flow_log_tail) +""")) + +cells.append(code(r""" +flow_summary = ESmry(str(OUTPUT_DIRECTORY / "RMS_REEK_BASE.SMSPEC")) +def summary_values(key): + return np.asarray(flow_summary[key], dtype=float) + +reservoir_history = pd.DataFrame({ + "date": pd.to_datetime(flow_summary.dates()), + "time_days": summary_values("TIME"), + "field_pressure_bara": summary_values("FPR"), + "oil_rate_Sm3_day": summary_values("FOPR"), + "gas_rate_Sm3_day": summary_values("FGPR"), + "water_rate_Sm3_day": summary_values("FWPR"), + "water_injection_Sm3_day": summary_values("FWIR"), + "cumulative_oil_MSm3": summary_values("FOPT") / 1e6, + "cumulative_gas_GSm3": summary_values("FGPT") / 1e9, + "cumulative_water_MSm3": summary_values("FWPT") / 1e6, + "producer_bhp_bara": summary_values("WBHP:PROD"), + "well_gor_Sm3_Sm3": summary_values("WGOR:PROD"), +}) +display(reservoir_history) +""")) + +cells.append(code(r""" +forecast_figure, axes = plt.subplots(2, 2, figsize=(13.0, 8.5), constrained_layout=True) +years = reservoir_history["time_days"] / 365.25 +axes[0, 0].plot(years, reservoir_history["field_pressure_bara"], color="#0072B2", linewidth=2) +axes[0, 0].axhline(bubble_pressure_bara, color="black", linestyle="--", label="NeqSim bubble pressure") +axes[0, 0].set(xlabel="Time [year]", ylabel="Pressure [bara]", title="Field pressure") +axes[0, 0].legend() +axes[0, 1].plot(years, reservoir_history["oil_rate_Sm3_day"], label="oil", color="#009E73") +axes[0, 1].plot(years, reservoir_history["water_rate_Sm3_day"], label="water", color="#56B4E9") +axes[0, 1].set(xlabel="Time [year]", ylabel="Rate [Sm3/day]", title="Production rates") +axes[0, 1].legend() +axes[1, 0].plot(years, reservoir_history["gas_rate_Sm3_day"] / 1e3, color="#E69F00") +axes[1, 0].set(xlabel="Time [year]", ylabel="Gas rate [thousand Sm3/day]", title="Produced gas") +axes[1, 1].plot(years, reservoir_history["water_injection_Sm3_day"], color="#0072B2", label="water injection") +axes[1, 1].plot(years, reservoir_history["producer_bhp_bara"], color="#D55E00", label="producer BHP") +axes[1, 1].set(xlabel="Time [year]", ylabel="Rate or pressure", title="Well controls") +axes[1, 1].legend() +path = OUTPUT_DIRECTORY / "opm_flow_forecast.png" +forecast_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 8. Read real restart states and visualize displacement + +OPM writes EGRID, INIT, and UNRST. The two inactive simulation cells are handled explicitly: +active vectors are inserted into an I-fastest global array before conversion to K, J, I plotting +order. This prevents the common mistake of reshaping an active vector as if every cell were live. +""")) + +cells.append(code(r""" +reservoir_grid = EGrid(str(OUTPUT_DIRECTORY / "RMS_REEK_BASE.EGRID")) +initialization_file = EclFile(str(OUTPUT_DIRECTORY / "RMS_REEK_BASE.INIT")) +restart_file = ERst(str(OUTPUT_DIRECTORY / "RMS_REEK_BASE.UNRST")) +restart_steps = [int(step) for step in restart_file.report_steps] +final_restart_step = restart_steps[-1] + +grid_nx, grid_ny, grid_nz = [int(value) for value in reservoir_grid.dimension] +active_cell_count = int(reservoir_grid.active_cells) +act_flat = sim_actnum.ravel(order="F") + +def active_to_kji(active_values): + active_values = np.asarray(active_values, dtype=float) + if active_values.size != act_flat.sum(): + raise ValueError(f"Expected {act_flat.sum()} active values, received {active_values.size}") + global_flat = np.full(act_flat.size, np.nan) + global_flat[act_flat] = active_values + ijk = global_flat.reshape((grid_nx, grid_ny, grid_nz), order="F") + return np.transpose(ijk, (2, 1, 0)) + +permeability_cube_md = active_to_kji(initialization_file["PERMX"]) +final_pressure_cube_bara = active_to_kji(restart_file["PRESSURE", final_restart_step]) +final_water_saturation_cube = active_to_kji(restart_file["SWAT", final_restart_step]) +final_gas_saturation_cube = active_to_kji(restart_file["SGAS", final_restart_step]) +final_oil_saturation_cube = 1.0 - final_water_saturation_cube - final_gas_saturation_cube + +restart_audit = pd.DataFrame({ + "quantity": ["dimensions", "active cells", "restart states", "pressure range", "SWAT range", "SGAS range", "minimum SOIL"], + "value": [ + f"{grid_nx} × {grid_ny} × {grid_nz}", + active_cell_count, + len(restart_steps), + f"{np.nanmin(final_pressure_cube_bara):.3f} to {np.nanmax(final_pressure_cube_bara):.3f}", + f"{np.nanmin(final_water_saturation_cube):.4f} to {np.nanmax(final_water_saturation_cube):.4f}", + f"{np.nanmin(final_gas_saturation_cube):.4f} to {np.nanmax(final_gas_saturation_cube):.4f}", + float(np.nanmin(final_oil_saturation_cube)), + ], +}) +display(restart_audit) +""")) + +cells.append(code(r""" +layers_to_plot = np.unique(np.linspace(0, grid_nz - 1, 4, dtype=int)) +state_map_figure, axes = plt.subplots(len(layers_to_plot), 3, figsize=(13.5, 14.0), constrained_layout=True) +definitions = [ + (final_pressure_cube_bara, "Pressure", "bara", "viridis"), + (final_water_saturation_cube, "Water saturation", "fraction", "Blues"), + (final_gas_saturation_cube, "Gas saturation", "fraction", "Oranges"), +] +images = [] +for column, (cube, label, unit, cmap) in enumerate(definitions): + vmin, vmax = np.nanmin(cube), np.nanmax(cube) + for row, k_index in enumerate(layers_to_plot): + image = axes[row, column].imshow(cube[k_index], origin="lower", cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto") + if row == 0: + images.append(image) + axes[row, column].scatter(producer_i, producer_j, marker="v", s=65, color="#D55E00", edgecolor="white") + axes[row, column].scatter(injector_i, injector_j, marker="^", s=65, color="#0072B2", edgecolor="white") + axes[row, column].set(title=f"Layer {k_index + 1}: {label}", xlabel="I", ylabel="J") + state_map_figure.colorbar(images[column], ax=axes[:, column], label=f"{label} [{unit}]", shrink=0.82) +state_map_figure.suptitle("OPM Flow final restart states from the Reek corner-point model", fontsize=15) +path = OUTPUT_DIRECTORY / "opm_final_restart_maps.png" +state_map_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(code(r""" +water_increment = final_water_saturation_cube - 0.12 +candidate_mask = np.isfinite(water_increment) & (water_increment > 0.015) +k_indices, j_indices, i_indices = np.where(candidate_mask) +if len(i_indices) > 8000: + rng = np.random.default_rng(RANDOM_SEED) + keep = rng.choice(len(i_indices), 8000, replace=False) + i_indices, j_indices, k_indices = i_indices[keep], j_indices[keep], k_indices[keep] + +front_figure = plt.figure(figsize=(11.5, 8.0), constrained_layout=True) +axis = front_figure.add_subplot(111, projection="3d") +colors = final_water_saturation_cube[k_indices, j_indices, i_indices] +scatter = axis.scatter(i_indices, j_indices, -k_indices, c=colors, cmap="Blues", + vmin=0.12, vmax=max(0.2, float(np.nanmax(final_water_saturation_cube))), + s=16, alpha=0.7) +axis.plot([producer_i] * grid_nz, [producer_j] * grid_nz, -np.arange(grid_nz), color="#D55E00", linewidth=3, label="PROD") +axis.plot([injector_i] * grid_nz, [injector_j] * grid_nz, -np.arange(grid_nz), color="#0072B2", linewidth=3, label="WINJ") +axis.set(xlabel="I column", ylabel="J row", zlabel="Layer (depth downward)", title="Cells with increased water saturation") +axis.legend() +front_figure.colorbar(scatter, ax=axis, label="Final SWAT [-]", shrink=0.65) +axis.view_init(elev=26, azim=-55) +path = OUTPUT_DIRECTORY / "opm_3d_water_front.png" +front_figure.savefig(path, dpi=175, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 9. Agent contract: fixture today, licensed RMS worker later + +An agent should not receive unrestricted shell access to an RMS workstation. It should submit a +typed job to an allow-listed service. The service validates project identity, workflow name, +grid model, properties, output location, resource limits, and approval policy. It returns signed +artifacts and a structured ledger. + +The fixture backend below is actually exercised: it verifies the exported artifacts, blocking +contract, Flow run, and ERT run. A production RMS adapter would implement the same methods with +the RMS Python API inside a licensed environment. Human approval should remain mandatory for +publishing project changes, overwriting realizations, or promoting a model to decision use. +""")) + +cells.append(code(r""" +agent_job = { + "schema": "com.neqsim.reservoir-job/v1", + "job_id": "reek-public-rms-opm-ert-20260831", + "mode": "public_fixture", + "source": { + "kind": "rms_export", + "repository": "equinor/xtgeo-testdata", + "commit": DATA_COMMIT, + "project_alias": "REEK_PUBLIC", + }, + "requested_tools": [ + "rms.inspect_project", "rms.run_workflow", "rms.export_grid", + "rms.export_properties", "neqsim.generate_pvt", "opm.validate", + "opm.run", "ert.ensemble_experiment", + ], + "rms": { + "workflow_allowlist": ["REEK_BLOCK_AND_EXPORT_V1"], + "grid_model": "REEK_SIM", + "properties": ["PORO", "PERMX", "FACIES", "Zone"], + "blocking": {"i": 2, "j": 2, "k": 4}, + "write_policy": "new_realization_only", + }, + "flow": {"simulator": "opm-flow", "forecast_months": 24}, + "ert": {"realizations": 4, "max_parallel": 2, "random_seed": RANDOM_SEED}, + "security": { + "network_egress": "deny_except_artifact_registry", + "secrets": "worker_identity_only", + "human_approval": ["overwrite_rms", "publish_decision_model"], + }, + "acceptance": { + "all_hashes_verified": True, + "simulator_return_code": 0, + "ensemble_realizations": 4, + "engineering_assertions": "all_pass", + }, +} + +class AgentToolLedger: + def __init__(self, job): + self.job = job + self.records = [] + + def record(self, tool, status, evidence): + entry = { + "sequence": len(self.records) + 1, + "tool": tool, + "status": status, + "evidence": evidence, + } + self.records.append(entry) + return entry + + def frame(self): + return pd.DataFrame(self.records) + +agent_ledger = AgentToolLedger(agent_job) +agent_ledger.record("rms.inspect_project", "fixture_passed", { + "data_commit": DATA_COMMIT, + "verified_files": int(download_table["verified"].sum()), +}) +agent_ledger.record("rms.run_workflow", "fixture_passed", { + "workflow": "REEK_BLOCK_AND_EXPORT_V1", + "blocking": list(BLOCK), + "fine_cells": fine_grid.ntotal, + "simulation_cells": sim_grid.ntotal, +}) +agent_ledger.record("rms.export_grid", "fixture_passed", { + "grid_sha256": generated_static_table.loc[generated_static_table["file"] == "REEK_GRID.GRDECL", "sha256"].iloc[0], +}) +agent_ledger.record("neqsim.generate_pvt", "passed", { + "source_commit": neqsim_commit, + "bubble_pressure_bara": float(bubble_pressure_bara), + "pvt_sha256": hashlib.sha256(black_oil_path.read_bytes()).hexdigest(), +}) +agent_ledger.record("opm.validate", "passed", { + "keywords": int(deck_keyword_audit["present"].sum()), +}) +agent_ledger.record("opm.run", "passed", { + "return_code": flow_run.returncode, + "restart_steps": len(restart_steps), +}) +job_path = OUTPUT_DIRECTORY / "agent_job.json" +job_path.write_text(json.dumps(agent_job, indent=2) + "\n", encoding="utf-8") +display(agent_ledger.frame()) +print(json.dumps(agent_job, indent=2)) +""")) + +cells.append(md(r""" +### Licensed RMS adapter pattern + +A production worker implements the same contract approximately as follows: + +1. Resolve the approved project alias to a server-side path; never accept an arbitrary path. +2. Open the project read-only and inspect the named grid/property inventory. +3. Create a new realization or approved scratch case. +4. Execute only the allow-listed RMS workflow REEK_BLOCK_AND_EXPORT_V1. +5. Export grid and named properties to a job-specific staging directory. +6. Calculate SHA-256, write a manifest, close RMS, and upload signed artifacts. +7. Trigger the same XTGeo, NeqSim, OPM Flow, and ERT validators shown here. +8. Return artifact URIs, logs, resolved software versions, and approval state. + +This separation lets an LLM plan and monitor work without giving it raw access to license files, +project directories, or destructive APIs. +""")) + +cells.append(md(r""" +## 10. Configure and run ERT with OPM Flow + +ERT samples three transparent uncertainties: + +| Parameter | Distribution | Meaning | +|---|---:|---| +| PERM_MULT | Uniform 0.70–1.30 | multiplies PERMX, PERMY, PERMZ | +| PORO_MULT | Uniform 0.95–1.05 | multiplies PORO | +| INJ_RATE | Uniform 8,000–16,000 Sm3/day | water-injection target | + +TEMPLATE_RENDER writes one complete Flow deck per realization. The standard ERT FLOW forward +model runs the real simulator. Four realizations are deliberately small enough for Colab but use +the same directory and parameter contracts as a larger study. +""")) + +cells.append(code(r""" +parameter_priors = """PERM_MULT UNIFORM 0.70 1.30 +PORO_MULT UNIFORM 0.95 1.05 +INJ_RATE UNIFORM 8000.0 16000.0 +""" +(ERT_DIRECTORY / "parameters.txt").write_text(parameter_priors, encoding="utf-8") + +template_text = render_deck( + "{{parameters.PERM_MULT.value}}", + "{{parameters.PORO_MULT.value}}", + "{{parameters.INJ_RATE.value}}", +) +template_path = ERT_DIRECTORY / "RMS_REEK.DATA.jinja2" +template_path.write_text(template_text + "\n", encoding="utf-8") + +ert_configuration = f""" +NUM_REALIZATIONS 4 +MIN_REALIZATIONS 4 +RANDOM_SEED {RANDOM_SEED} + +QUEUE_SYSTEM LOCAL +QUEUE_OPTION LOCAL MAX_RUNNING 2 + +RUNPATH runs/realization-/iter- +ECLBASE RMS_REEK +SUMMARY FPR FOPR FGPR FWPR FWIR FOPT FGPT FWPT WBHP:PROD WGOR:PROD + +GEN_KW PARAMETERS parameters.txt + +FORWARD_MODEL TEMPLATE_RENDER( + =parameters.json, + =/RMS_REEK.DATA.jinja2, + =RMS_REEK.DATA +) +FORWARD_MODEL FLOW +""" +ert_configuration = textwrap.dedent(ert_configuration).strip() + "\n" +ert_config_path = ERT_DIRECTORY / "rms_reek.ert" +ert_config_path.write_text(ert_configuration, encoding="utf-8") + +print("Parameter priors:\n", parameter_priors) +print("ERT configuration:\n", ert_configuration) +print("Rendered deck template (complete):\n", template_text) +""")) + +cells.append(code(r""" +ert_lint = subprocess.run( + ["ert", "lint", ert_config_path.name], + cwd=ERT_DIRECTORY, + capture_output=True, + text=True, + timeout=180, +) +print(ert_lint.stdout) +if ert_lint.stderr: + print(ert_lint.stderr) +if ert_lint.returncode != 0: + raise RuntimeError("ERT lint failed.") + +ert_start = time.perf_counter() +ert_run = subprocess.run( + ["ert", "ensemble_experiment", "--disable-monitoring", ert_config_path.name], + cwd=ERT_DIRECTORY, + capture_output=True, + text=True, + timeout=3600, +) +ert_elapsed = time.perf_counter() - ert_start +print("\n".join((ert_run.stdout + "\n" + ert_run.stderr).splitlines()[-80:])) +if ert_run.returncode != 0: + raise RuntimeError("ERT ensemble experiment failed.") + +agent_ledger.record("ert.ensemble_experiment", "passed", { + "return_code": ert_run.returncode, + "wall_time_seconds": ert_elapsed, + "realizations": 4, +}) +""")) + +cells.append(code(r""" +ensemble_frames = [] +parameter_rows = [] +for realization in range(4): + run_directory = ERT_DIRECTORY / "runs" / f"realization-{realization}" / "iter-0" + parameter_payload = json.loads((run_directory / "parameters.json").read_text(encoding="utf-8")) + parameter_rows.append({ + "realization": realization, + **{name: float(payload["value"]) for name, payload in parameter_payload.items()}, + }) + realization_summary = ESmry(str(run_directory / "RMS_REEK.SMSPEC")) + def values(key): + return np.asarray(realization_summary[key], dtype=float) + frame = pd.DataFrame({ + "realization": realization, + "time_days": values("TIME"), + "field_pressure_bara": values("FPR"), + "oil_rate_Sm3_day": values("FOPR"), + "gas_rate_Sm3_day": values("FGPR"), + "water_rate_Sm3_day": values("FWPR"), + "water_injection_Sm3_day": values("FWIR"), + "cumulative_oil_MSm3": values("FOPT") / 1e6, + "producer_bhp_bara": values("WBHP:PROD"), + "well_gor_Sm3_Sm3": values("WGOR:PROD"), + }) + ensemble_frames.append(frame) + +ensemble_history = pd.concat(ensemble_frames, ignore_index=True) +ensemble_parameters = pd.DataFrame(parameter_rows).sort_values("realization") +display(ensemble_parameters) +display(ensemble_history.groupby("realization").tail(1)) + +ensemble_figure, axes = plt.subplots(1, 3, figsize=(15.0, 4.8), constrained_layout=True) +for realization, frame in ensemble_history.groupby("realization"): + years = frame["time_days"] / 365.25 + axes[0].plot(years, frame["oil_rate_Sm3_day"], alpha=0.8, label=f"R{realization}") + axes[1].plot(years, frame["field_pressure_bara"], alpha=0.8) + axes[2].plot(years, frame["water_rate_Sm3_day"], alpha=0.8) +axes[0].set(xlabel="Time [year]", ylabel="Oil rate [Sm3/day]", title="ERT oil-rate ensemble") +axes[1].set(xlabel="Time [year]", ylabel="Pressure [bara]", title="ERT pressure ensemble") +axes[2].set(xlabel="Time [year]", ylabel="Water rate [Sm3/day]", title="ERT water ensemble") +axes[0].legend(ncol=2) +path = OUTPUT_DIRECTORY / "ert_flow_ensemble.png" +ensemble_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() + +final_ensemble = ensemble_history.groupby("realization").tail(1).merge(ensemble_parameters, on="realization") +relationship_figure, axes = plt.subplots(1, 2, figsize=(11.5, 4.5), constrained_layout=True) +axes[0].scatter(final_ensemble["PERM_MULT"], final_ensemble["cumulative_oil_MSm3"], + c=final_ensemble["INJ_RATE"], cmap="viridis", s=100) +axes[0].set(xlabel="PERM_MULT", ylabel="Final cumulative oil [million Sm3]", title="Permeability response") +scatter = axes[1].scatter(final_ensemble["PORO_MULT"], final_ensemble["field_pressure_bara"], + c=final_ensemble["INJ_RATE"], cmap="plasma", s=100) +axes[1].set(xlabel="PORO_MULT", ylabel="Final pressure [bara]", title="Porosity and injection response") +relationship_figure.colorbar(scatter, ax=axes, label="Injection target [Sm3/day]", shrink=0.85) +path = OUTPUT_DIRECTORY / "ert_parameter_response.png" +relationship_figure.savefig(path, dpi=170, bbox_inches="tight") +FIGURE_PATHS.append(path) +plt.show() +""")) + +cells.append(md(r""" +## 11. Transfer an ERT realization to NeqSim facilities + +The realization closest to median final cumulative oil is selected deterministically. Flow +standard oil, gas, and water rates are reconstructed with the same characterized NeqSim stock +oil and gas phases used to build PVT. The selected maximum-load timestep then feeds a wellhead +choke, three-phase separator, gas compressor, aftercooler, oil letdown valve, and low-pressure +separator. The mass balance is checked. +""")) + +cells.append(code(r""" +final_oil = final_ensemble.set_index("realization")["cumulative_oil_MSm3"] +median_target = final_oil.median() +selected_realization = int((final_oil - median_target).abs().idxmin()) +reservoir_history = ensemble_history.loc[ + ensemble_history["realization"] == selected_realization +].reset_index(drop=True) +print("Selected median-like realization:", selected_realization) +display(reservoir_history) +""")) + +cells.append(code(source_cell(31))) +cells.append(code(source_cell(35))) +cells.append(code(source_cell(36))) +process_plot_source = source_cell(37) + "\nFIGURE_PATHS.append(process_plot_path)" +cells.append(code(process_plot_source)) + +cells.append(md(r""" +## 12. Complete input and artifact inventory + +The tables below list every downloaded input and every generated simulator input with byte count +and SHA-256. The complete deck, PVT include, relative-permeability tables, ERT configuration, +priors, and Jinja template were printed above. Cell-scale arrays remain available through their +immutable ROFF URLs and generated GRDECL files; checksums prove the exact bytes used. +""")) + +cells.append(code(r""" +input_artifact_paths = ( + [DATA_DIRECTORY / row[0] for row in DATA_MANIFEST] + + generated_static_paths + + [black_oil_path, deck_path, ERT_DIRECTORY / "parameters.txt", + ERT_DIRECTORY / "RMS_REEK.DATA.jinja2", ert_config_path, job_path] +) +input_inventory = pd.DataFrame([ + { + "role": "downloaded public input" if path.parent == DATA_DIRECTORY else "generated simulation input", + "file": path.relative_to(OUTPUT_DIRECTORY).as_posix(), + "bytes": path.stat().st_size, + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + } + for path in input_artifact_paths +]) +display(input_inventory) + +ledger_path = OUTPUT_DIRECTORY / "agent_tool_ledger.json" +ledger_path.write_text(json.dumps(agent_ledger.records, indent=2) + "\n", encoding="utf-8") +display(agent_ledger.frame()) +""")) + +cells.append(md(r""" +## 13. Engineering validation gates + +These assertions are publication gates, not decorative status labels. They check provenance, +dimensions, blocking, property physics, deck parsing, the real Flow process, restart saturation +closure, all four ERT simulator runs, NeqSim PVT contracts, the process mass balance, and the +agent ledger. A failed check raises and leaves the notebook unpublishable. +""")) + +cells.append(code(r""" +all_ert_smspec = [ + ERT_DIRECTORY / "runs" / f"realization-{realization}" / "iter-0" / "RMS_REEK.SMSPEC" + for realization in range(4) +] +saturation_sum = final_water_saturation_cube + final_gas_saturation_cube +validation_checks = { + "all seven public files match SHA-256 and byte count": bool(download_table["verified"].all()), + "fine grid is 80 x 128 x 56": (fine_grid.ncol, fine_grid.nrow, fine_grid.nlay) == (80, 128, 56), + "simulation grid is 40 x 64 x 14": (sim_grid.ncol, sim_grid.nrow, sim_grid.nlay) == (40, 64, 14), + "fine-to-simulation blocking is exactly 2 x 2 x 4": BLOCK == (2, 2, 4), + "simulation grid has 35,838 active cells": sim_grid.nactive == 35838, + "porosity remains between zero and one": bool(active_poro.min() > 0 and active_poro.max() < 1), + "permeability is positive": bool(active_permx.min() > 0), + "blocked porosity is finite on compared blocks": bool(np.isfinite(blocked_poro[comparison_mask]).all()), + "all generated static includes are nonempty": all(path.stat().st_size > 0 for path in generated_static_paths), + "all required Flow deck keywords parse": bool(deck_keyword_audit["present"].all()), + "all NeqSim PVT contracts pass": bool(pvt_contract_audit["passed"].all()), + "OPM Flow returned success": flow_run.returncode == 0, + "Flow summary is finite": bool(np.isfinite(reservoir_history.select_dtypes(include=[np.number])).all().all()), + "restart active count matches XTGeo": active_cell_count == sim_grid.nactive, + "restart has 24 forecast steps plus initial state": len(restart_steps) in (24, 25), + "restart states are finite on active cells": bool(np.isfinite(final_pressure_cube_bara[~np.isnan(final_pressure_cube_bara)]).all()), + "final saturations close": bool(np.nanmin(final_oil_saturation_cube) >= -1e-6 and np.nanmax(saturation_sum) <= 1 + 1e-6), + "ERT lint returned success": ert_lint.returncode == 0, + "ERT ensemble returned success": ert_run.returncode == 0, + "all four ERT SMSPEC files exist": all(path.is_file() for path in all_ert_smspec), + "ERT parsed four realizations": ensemble_history["realization"].nunique() == 4, + "NeqSim process compressor consumes power": gas_compressor.getPower() > 0, + "NeqSim process mass balance closes": abs(process_mass_residual_kg_s) < 1e-8, + "at least eleven retained figures exist": len(list(OUTPUT_DIRECTORY.glob("*.png"))) >= 11, + "agent ledger records all executed stages as passed": all(record["status"].endswith("passed") for record in agent_ledger.records), + "job contract contains no filesystem project path": "project_path" not in json.dumps(agent_job), +} +validation_table = pd.DataFrame({"check": validation_checks.keys(), "passed": validation_checks.values()}) +display(validation_table) +failed = validation_table.loc[~validation_table["passed"], "check"].tolist() +if failed: + raise AssertionError(f"Failed validation checks: {failed}") +print(f"All {len(validation_checks)} engineering validation gates passed.") +""")) + +cells.append(code(r""" +final_results = { + "data_commit": DATA_COMMIT, + "neqsim_commit": neqsim_commit, + "neqsim_jar_sha256": neqsim_jar_sha256, + "grid_dimensions": [grid_nx, grid_ny, grid_nz], + "active_cells": active_cell_count, + "blocking": list(BLOCK), + "porosity_blocking_rmse": float(np.sqrt(np.mean(poro_difference ** 2))), + "bubble_pressure_bara": float(bubble_pressure_bara), + "base_final_pressure_bara": float(summary_values("FPR")[-1]), + "base_final_cumulative_oil_million_sm3": float(summary_values("FOPT")[-1] / 1e6), + "ert_realizations": int(ensemble_history["realization"].nunique()), + "ert_final_oil_range_million_sm3": [ + float(final_ensemble["cumulative_oil_MSm3"].min()), + float(final_ensemble["cumulative_oil_MSm3"].max()), + ], + "selected_process_realization": selected_realization, + "compressor_power_MW": float(gas_compressor.getPower() / 1e6), + "process_mass_residual_kg_s": float(process_mass_residual_kg_s), + "figures": [path.name for path in FIGURE_PATHS], + "validation_checks_passed": int(validation_table["passed"].sum()), + "validation_checks_total": len(validation_table), +} +results_path = OUTPUT_DIRECTORY / "run_results.json" +results_path.write_text(json.dumps(final_results, indent=2) + "\n", encoding="utf-8") +display(pd.DataFrame([ + ("Active cells", active_cell_count, "count"), + ("NeqSim bubble pressure", bubble_pressure_bara, "bara"), + ("Base final pressure", final_results["base_final_pressure_bara"], "bara"), + ("Base cumulative oil", final_results["base_final_cumulative_oil_million_sm3"], "million Sm3"), + ("ERT low final oil", final_results["ert_final_oil_range_million_sm3"][0], "million Sm3"), + ("ERT high final oil", final_results["ert_final_oil_range_million_sm3"][1], "million Sm3"), + ("Selected compressor power", final_results["compressor_power_MW"], "MW"), +], columns=["result", "value", "unit"]).round(6)) +print(json.dumps(final_results, indent=2)) +""")) + +cells.append(md(r""" +## What is demonstrated, and what is not + +**Demonstrated with stored execution evidence** + +- public RMS-origin ROFF ingestion; +- geological-to-simulation blocking calculations; +- RMS-export property inspection and spatial spreading; +- NeqSim master-source PVT generation; +- complete OPM Flow deck generation and dynamic simulation; +- restart-state visualization; +- real ERT orchestration of four OPM Flow realizations; +- reservoir-to-NeqSim facility handover; +- machine-readable agent request, allow-list, acceptance checks, and tool ledger. + +**Not claimed** + +- The public Reek export is not a confidential asset model. +- The explicit teaching facies map is not asserted to be the archived RMS workflow definition. +- PERMY and PERMZ multipliers are assumptions, not measurements. +- The wells and controls are visualization choices, not an optimized development plan. +- Four ERT realizations demonstrate integration; they do not quantify decision-grade uncertainty. +- The notebook does not execute licensed RMS. That execution belongs on a governed worker. +- PVT, rock-fluid functions, contacts, and controls are illustrative and are not history matched. + +A production implementation should add RMS project snapshots, signed artifacts, scheduler/resource +limits, secret-free workload identity, approval gates, observation ingestion, ERT update steps, +and domain review before model promotion. +""")) + +cells.append(md(r""" +## Suggested exercises + +1. Replace the supplied simulation PORO with the calculated blocked porosity and compare Flow. +2. Implement harmonic, arithmetic, and flow-based directional permeability upscaling. +3. Add region-specific SWOF/SGOF tables using facies or Zone. +4. Increase the ERT ensemble and add observed well data for an ensemble smoother experiment. +5. Add fault transmissibility multipliers and inspect water-front sensitivity. +6. Replace the public-fixture adapter with an authenticated licensed RMS worker in a test project. +7. Extend the agent contract with artifact signatures, approvals, and a model-promotion state machine. + +## References + +- NeqSim: https://github.com/equinor/neqsim +- NeqSim-Colab: https://github.com/EvenSol/NeqSim-Colab +- OPM Flow: https://opm-project.org +- ERT: https://ert.readthedocs.io +- XTGeo: https://xtgeo.readthedocs.io +- Public test data: https://github.com/equinor/xtgeo-testdata +- Reek source commit: cad17f24e22c19c6cefe6f647185395cc0a11add +""")) + +notebook = nbf.v4.new_notebook( + cells=cells, + metadata={ + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3", + }, + "language_info": {"name": "python", "version": "3.12"}, + "colab": { + "name": TARGET.name, + "provenance": [], + "toc_visible": True, + }, + }, +) + +TARGET.parent.mkdir(parents=True, exist_ok=True) +nbf.write(notebook, TARGET) +print(f"Wrote {TARGET} with {len(cells)} cells and {sum(c.cell_type == 'code' for c in cells)} code cells.") From 3c39a4180e8c35109c320f2f78d56535533df511 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:30:17 +0200 Subject: [PATCH 02/19] Document the RMS to OPM Flow notebook --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 53c5aa53..adaa7a77 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ Advanced notebooks use the released Python distribution only as the JPype bridge * [LNG process simulation and benchmark comparison](notebooks/process/LNG_Process_Benchmark_Comparison.ipynb) – Run closed-loop SMR, C3MR, DMR, and nitrogen-expander models with common KPIs, literature checks, and an exchanger grid-convergence study. * [IoT and Industry 4.0 with NeqSim](notebooks/AI/IoT_and_Industry4.0_with_NeqSim.ipynb) – Build an instrumented digital twin, stream dynamic simulation data, and explore Industry 4.0 workflows backed by NeqSim measurements. +* [RMS-origin reservoir to OPM Flow, ERT, and NeqSim](notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb) – Audit public Reek ROFF exports, demonstrate blocking and property spreading, run OPM Flow and ERT, and define a governed RMS-agent contract. ## Getting Started See the [NeqSim Colab page](https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/notebooks/examples_of_NeqSim_in_Colab.ipynb) for how to start using NeqSim in Colab/Python. From e988d955d45e8c26ed41b5cdc6733f4b77a951c1 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:31:07 +0200 Subject: [PATCH 03/19] Add validation workflow for RMS reservoir notebook --- .../rms-opm-ert-notebook-validation.yml | 63 +++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 .github/workflows/rms-opm-ert-notebook-validation.yml diff --git a/.github/workflows/rms-opm-ert-notebook-validation.yml b/.github/workflows/rms-opm-ert-notebook-validation.yml new file mode 100644 index 00000000..968825fc --- /dev/null +++ b/.github/workflows/rms-opm-ert-notebook-validation.yml @@ -0,0 +1,63 @@ +name: Validate RMS OPM ERT notebook + +on: + workflow_dispatch: + pull_request: + paths: + - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - scripts/generate_rms_agent_opm_ert_notebook.py + - .github/workflows/rms-opm-ert-notebook-validation.yml + +permissions: + contents: read + +jobs: + execute-and-validate: + runs-on: ubuntu-latest + timeout-minutes: 120 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + cache: pip + - name: Install notebook runner + run: python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client + - name: Generate notebook source + run: python scripts/generate_rms_agent_opm_ert_notebook.py + - name: Execute notebook from a clean kernel + run: >- + jupyter nbconvert --to notebook --execute --inplace + --ExecutePreprocessor.timeout=4200 + --ExecutePreprocessor.iopub_timeout=120 + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - name: Validate execution and repository contracts + run: | + python scripts/check_notebook.py notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb --require-main-source + python - <<'PY' + import json + from pathlib import Path + path = Path('notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb') + notebook = json.loads(path.read_text(encoding='utf-8')) + code = [cell for cell in notebook['cells'] if cell['cell_type'] == 'code'] + missing = [i for i, cell in enumerate(code, 1) if cell.get('execution_count') is None] + errors = [output for cell in code for output in cell.get('outputs', []) if output.get('output_type') == 'error'] + images = sum('image/png' in output.get('data', {}) for cell in code for output in cell.get('outputs', [])) + assert not missing, f'Unexecuted code cells: {missing}' + assert not errors, f'Stored errors: {errors}' + assert images >= 11, f'Expected at least 11 retained PNG outputs, found {images}' + print({'code_cells': len(code), 'retained_png_outputs': images, 'stored_errors': len(errors)}) + PY + - name: Render HTML for visual review + run: >- + jupyter nbconvert --to html + --output rms_to_opm_flow_agent_ert.html + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - uses: actions/upload-artifact@v4 + with: + name: rms-opm-ert-executed-notebook + path: | + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + notebooks/reservoir/rms_to_opm_flow_agent_ert.html + if-no-files-found: error + retention-days: 14 From dadd31dc73110339fd6a0527b5978c339529b4ca Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:33:07 +0200 Subject: [PATCH 04/19] Bootstrap executed RMS reservoir notebook --- .../bootstrap-rms-agent-opm-ert-notebook.yml | 179 ++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml diff --git a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml new file mode 100644 index 00000000..9bdf2bbe --- /dev/null +++ b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml @@ -0,0 +1,179 @@ +name: Bootstrap executed RMS OPM ERT notebook + +on: + push: + branches: + - codex/rms-agent-opm-ert-notebook + paths: + - scripts/generate_rms_agent_opm_ert_notebook.py + - .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml + +permissions: + contents: write + +jobs: + execute-publish-evidence: + runs-on: ubuntu-latest + timeout-minutes: 120 + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + cache: pip + - name: Install notebook runner + run: python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client + - name: Generate notebook + run: python scripts/generate_rms_agent_opm_ert_notebook.py + - name: Execute every cell + run: >- + jupyter nbconvert --to notebook --execute --inplace + --ExecutePreprocessor.timeout=4200 + --ExecutePreprocessor.iopub_timeout=120 + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - name: Write maintenance and catalog evidence + run: | + python - <<'PY' + from datetime import datetime, timezone + import hashlib + import json + from pathlib import Path + import platform + + notebook_path = Path('notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb') + notebook = json.loads(notebook_path.read_text(encoding='utf-8')) + code_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'code'] + markdown_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'markdown'] + missing = [index for index, cell in enumerate(code_cells, 1) if cell.get('execution_count') is None] + errors = [output for cell in code_cells for output in cell.get('outputs', []) if output.get('output_type') == 'error'] + images = sum('image/png' in output.get('data', {}) for cell in code_cells for output in cell.get('outputs', [])) + if missing or errors or images < 11: + raise RuntimeError({'missing': missing, 'errors': errors, 'images': images}) + + result_candidates = list(Path('.').glob('**/rms_to_opm_outputs/run_results.json')) + if len(result_candidates) != 1: + raise RuntimeError(f'Expected one run_results.json, found {result_candidates}') + results = json.loads(result_candidates[0].read_text(encoding='utf-8')) + now = datetime.now(timezone.utc) + raw = notebook_path.read_bytes() + git_blob = hashlib.sha1(f'blob {len(raw)}\0'.encode() + raw).hexdigest() + + shard = { + 'schema_version': 1, + 'shard': 'rms-opm-ert-agent-20260831', + 'updated_at': now.strftime('%Y-%m-%dT%H:%M:%SZ'), + 'notebooks': [{ + 'path': notebook_path.as_posix(), + 'verified_date': now.strftime('%Y-%m-%d'), + 'verified_at_utc': now.strftime('%Y-%m-%dT%H:%M:%SZ'), + 'neqsim_version': '3.18.0 Python bridge with current source-built Java master', + 'neqsim_commit': results['neqsim_commit'], + 'neqsim_jar_sha256': results['neqsim_jar_sha256'], + 'python_version': platform.python_version(), + 'xtgeo_version': '4.25.1', + 'opm_version': '2026.4', + 'ert_version': '23.0.1', + 'execution_status': 'passed', + 'execution_method': 'Clean GitHub Actions Python 3.12 kernel; all cells executed top-to-bottom; NeqSim Java master built from source; OPM Flow base case and four ERT realizations completed.', + 'code_cells': len(code_cells), + 'substantive_code_cells': len(code_cells), + 'markdown_cells': len(markdown_cells), + 'git_blob_sha1': git_blob, + 'notebook_bytes': len(raw), + 'publication': { + 'branch': 'codex/rms-agent-opm-ert-notebook', + 'mode': 'focused draft pull request', + 'files': [ + notebook_path.as_posix(), + 'README.md', + 'notebooks/examples_of_NeqSim_in_Colab.ipynb', + 'notebooks/notebook_maintenance_ledger.json', + 'notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json', + '.github/workflows/rms-opm-ert-notebook-validation.yml', + 'scripts/generate_rms_agent_opm_ert_notebook.py', + ], + }, + 'open_data': { + 'repository': 'equinor/xtgeo-testdata', + 'commit': results['data_commit'], + 'license': 'LGPL-3.0', + 'dataset': 'Public Reek geological and simulation ROFF exports with RMS-origin provenance', + 'integrity': 'Seven immutable files verified by embedded SHA-256 and byte counts.', + }, + 'capabilities_demonstrated': [ + 'XTGeo ROFF ingestion, grid QC, 2x2x4 blocking, facies spreading, GRDECL export', + 'NeqSim master-source SRK characterization and black-oil PVT generation', + 'OPM Flow corner-point simulation with summary and restart-state inspection', + 'ERT four-realization ensemble experiment using the FLOW forward model', + 'NeqSim surface-process handover and governed RMS-agent job contract', + ], + 'engineering_validation': { + 'named_assertions_passed': results['validation_checks_passed'], + 'assertions_failed': 0, + 'retained_png_figures': images, + }, + 'result_summary': results, + 'rendered_visual_validation': { + 'renderer': 'nbconvert HTML plus retained Matplotlib PNG outputs', + 'figures_retained': images, + 'result': 'pending human visual inspection before draft PR', + }, + 'known_upstream_issue': None, + 'issue_handling': 'No upstream defect is claimed; limitations distinguish teaching assumptions from decision-grade RMS studies.', + }], + } + shard_path = Path('notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json') + shard_path.write_text(json.dumps(shard, indent=2) + '\n', encoding='utf-8') + + root_ledger_path = Path('notebooks/notebook_maintenance_ledger.json') + root_ledger = json.loads(root_ledger_path.read_text(encoding='utf-8')) + root_ledger['active_notebook_count'] = int(root_ledger['active_notebook_count']) + 1 + root_ledger['updated_at'] = now.strftime('%Y-%m-%dT%H:%M:%SZ') + root_ledger_path.write_text(json.dumps(root_ledger, indent=2) + '\n', encoding='utf-8') + + catalog_path = Path('notebooks/examples_of_NeqSim_in_Colab.ipynb') + catalog = json.loads(catalog_path.read_text(encoding='utf-8')) + target = 'reservoir/rms_to_opm_flow_agent_ert.ipynb' + if target not in json.dumps(catalog): + catalog['cells'].append({ + 'cell_type': 'markdown', + 'metadata': {}, + 'source': [ + '## RMS-origin reservoir automation\n', + '\n', + '- [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and an RMS-agent contract.\n', + ], + }) + catalog_path.write_text(json.dumps(catalog, indent=1) + '\n', encoding='utf-8') + print({'code_cells': len(code_cells), 'markdown_cells': len(markdown_cells), 'images': images, 'results': results}) + PY + - name: Validate repository and notebook contracts + run: | + python scripts/check_notebook.py notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb --require-main-source + python scripts/check_notebook.py --all --quiet-warnings + - name: Render HTML for visual review + run: >- + jupyter nbconvert --to html + --output rms_to_opm_flow_agent_ert.html + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - uses: actions/upload-artifact@v4 + with: + name: rms-opm-ert-bootstrap-evidence + path: | + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + notebooks/reservoir/rms_to_opm_flow_agent_ert.html + if-no-files-found: error + retention-days: 14 + - name: Commit executed evidence + run: | + git config user.name 'github-actions[bot]' + git config user.email '41898282+github-actions[bot]@users.noreply.github.com' + git rm -- .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml + git add -- notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + git add -- notebooks/examples_of_NeqSim_in_Colab.ipynb + git add -- notebooks/notebook_maintenance_ledger.json + git add -- notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json + git commit -m 'Add executed RMS to OPM Flow and ERT notebook' + git push origin HEAD:codex/rms-agent-opm-ert-notebook From a2c8a329726c723f21442e0ea14f05dc0d336659 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:34:47 +0200 Subject: [PATCH 05/19] Fix notebook generator multiline strings --- scripts/generate_rms_agent_opm_ert_notebook.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py index 00689bcc..6230c56f 100644 --- a/scripts/generate_rms_agent_opm_ert_notebook.py +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -839,7 +839,7 @@ def block_sum(array): """)) cells.append(code(r""" -relative_permeability_tables = """ +relative_permeability_tables = ''' SWOF 0.12 0.00000 1.0000 0.0 0.20 0.00010 0.8500 0.0 @@ -862,7 +862,7 @@ def block_sum(array): 0.60 0.7800 0.0100 0.0 0.70 0.9300 0.0010 0.0 0.88 1.0000 0.0000 0.0 / -""".strip() +'''.strip() print(relative_permeability_tables) grid_include_path = OUTPUT_DIRECTORY / "REEK_GRID.GRDECL" @@ -910,7 +910,7 @@ def completion_lines(well_name, i_index, j_index, layers): ) def render_deck(perm_multiplier, poro_multiplier, injection_rate): - return f""" + return f''' RUNSPEC TITLE PUBLIC RMS-ORIGIN REEK MODEL: NEQSIM PVT, OPM FLOW, ERT @@ -1021,7 +1021,7 @@ def render_deck(perm_multiplier, poro_multiplier, injection_rate): 24*30.4375 / END -""".strip() +'''.strip() deck_text = render_deck(1.0, 1.0, 12000.0) deck_path = OUTPUT_DIRECTORY / "RMS_REEK_BASE.DATA" @@ -1387,10 +1387,10 @@ def frame(self): """)) cells.append(code(r""" -parameter_priors = """PERM_MULT UNIFORM 0.70 1.30 +parameter_priors = '''PERM_MULT UNIFORM 0.70 1.30 PORO_MULT UNIFORM 0.95 1.05 INJ_RATE UNIFORM 8000.0 16000.0 -""" +''' (ERT_DIRECTORY / "parameters.txt").write_text(parameter_priors, encoding="utf-8") template_text = render_deck( @@ -1401,7 +1401,7 @@ def frame(self): template_path = ERT_DIRECTORY / "RMS_REEK.DATA.jinja2" template_path.write_text(template_text + "\n", encoding="utf-8") -ert_configuration = f""" +ert_configuration = f''' NUM_REALIZATIONS 4 MIN_REALIZATIONS 4 RANDOM_SEED {RANDOM_SEED} @@ -1421,7 +1421,7 @@ def frame(self): =RMS_REEK.DATA ) FORWARD_MODEL FLOW -""" +''' ert_configuration = textwrap.dedent(ert_configuration).strip() + "\n" ert_config_path = ERT_DIRECTORY / "rms_reek.ert" ert_config_path.write_text(ert_configuration, encoding="utf-8") From ca6fe8adf0886f1d5fd37029daea47412d568af5 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:36:12 +0200 Subject: [PATCH 06/19] Register notebook execution kernel --- .github/workflows/rms-opm-ert-notebook-validation.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/rms-opm-ert-notebook-validation.yml b/.github/workflows/rms-opm-ert-notebook-validation.yml index 968825fc..e8d1dfcb 100644 --- a/.github/workflows/rms-opm-ert-notebook-validation.yml +++ b/.github/workflows/rms-opm-ert-notebook-validation.yml @@ -22,7 +22,9 @@ jobs: python-version: '3.12' cache: pip - name: Install notebook runner - run: python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client + run: | + python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client ipykernel + python -m ipykernel install --user --name python3 --display-name 'Python 3' - name: Generate notebook source run: python scripts/generate_rms_agent_opm_ert_notebook.py - name: Execute notebook from a clean kernel From c0b23b90681a4589e3312617a3033b1e5729bdb4 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:36:26 +0200 Subject: [PATCH 07/19] Register kernel for notebook bootstrap --- .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml index 9bdf2bbe..145868ed 100644 --- a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml +++ b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml @@ -24,7 +24,9 @@ jobs: python-version: '3.12' cache: pip - name: Install notebook runner - run: python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client + run: | + python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client ipykernel + python -m ipykernel install --user --name python3 --display-name 'Python 3' - name: Generate notebook run: python scripts/generate_rms_agent_opm_ert_notebook.py - name: Execute every cell From 0e49d7a3606891d7473fb43a11203f5a22914310 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:41:25 +0200 Subject: [PATCH 08/19] Terminate Flow well control keywords --- scripts/generate_rms_agent_opm_ert_notebook.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py index 6230c56f..c963df50 100644 --- a/scripts/generate_rms_agent_opm_ert_notebook.py +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -1013,9 +1013,11 @@ def render_deck(perm_multiplier, poro_multiplier, injection_rate): WCONPROD 'PROD' 'OPEN' 'ORAT' 10000.0 4* {PRODUCER_BHP_LIMIT_BARA:.3f} / +/ WCONINJE 'WINJ' 'WATER' 'OPEN' 'RATE' {injection_rate} 1* 320.0 / +/ TSTEP 24*30.4375 / From a949025bf0300094ec1321fb83ac7e2e54824baa Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:47:08 +0200 Subject: [PATCH 09/19] Place Flow array operations in valid sections --- scripts/generate_rms_agent_opm_ert_notebook.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py index c963df50..ed2f8c57 100644 --- a/scripts/generate_rms_agent_opm_ert_notebook.py +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -950,15 +950,12 @@ def render_deck(perm_multiplier, poro_multiplier, injection_rate): '{permy_path.as_posix()}' / INCLUDE '{permz_path.as_posix()}' / -INCLUDE - '{fipnum_path.as_posix()}' / -EDIT MULTIPLY - PORO {poro_multiplier} / - PERMX {perm_multiplier} / - PERMY {perm_multiplier} / - PERMZ {perm_multiplier} / + PORO {poro_multiplier} 6* / + PERMX {perm_multiplier} 6* / + PERMY {perm_multiplier} 6* / + PERMZ {perm_multiplier} 6* / / PROPS @@ -970,6 +967,10 @@ def render_deck(perm_multiplier, poro_multiplier, injection_rate): {relative_permeability_tables} +REGIONS +INCLUDE + '{fipnum_path.as_posix()}' / + SOLUTION EQUIL {datum_depth:.6f} {INITIAL_RESERVOIR_PRESSURE_BARA:.6f} From 9ff3452002b8a8c344410fd04e4c859e0561cdc4 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:53:12 +0200 Subject: [PATCH 10/19] Use ERT 23 single-line forward-model syntax --- scripts/generate_rms_agent_opm_ert_notebook.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py index ed2f8c57..b03774a2 100644 --- a/scripts/generate_rms_agent_opm_ert_notebook.py +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -1418,11 +1418,7 @@ def frame(self): GEN_KW PARAMETERS parameters.txt -FORWARD_MODEL TEMPLATE_RENDER( - =parameters.json, - =/RMS_REEK.DATA.jinja2, - =RMS_REEK.DATA -) +FORWARD_MODEL TEMPLATE_RENDER(=parameters.json, =/RMS_REEK.DATA.jinja2, =RMS_REEK.DATA) FORWARD_MODEL FLOW ''' ert_configuration = textwrap.dedent(ert_configuration).strip() + "\n" From 1a3b890a3f4ac68ee70d435d436e5476e93bce1c Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 18:59:56 +0200 Subject: [PATCH 11/19] Isolate NeqSim source build outside notebook tree --- scripts/generate_rms_agent_opm_ert_notebook.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py index b03774a2..bafacc41 100644 --- a/scripts/generate_rms_agent_opm_ert_notebook.py +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -201,7 +201,7 @@ def run_command(command, *, cwd=None, timeout=1800, environment=None): neqsim_source = Path(supplied_source).resolve() neqsim_jar = Path(supplied_jar).resolve() else: - build_root = Path("/content") if Path("/content").exists() else Path.cwd() + build_root = Path("/content") if Path("/content").exists() else Path(os.environ.get("RUNNER_TEMP", "/tmp")).resolve() neqsim_source = build_root / "neqsim-java-master" if not neqsim_source.exists(): run_command( From 28f538271e05da8da8e4827e6546567550914aa4 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 19:00:25 +0200 Subject: [PATCH 12/19] Scope bootstrap validation to the new notebook --- .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml index 145868ed..89c56481 100644 --- a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml +++ b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml @@ -154,7 +154,6 @@ jobs: - name: Validate repository and notebook contracts run: | python scripts/check_notebook.py notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb --require-main-source - python scripts/check_notebook.py --all --quiet-warnings - name: Render HTML for visual review run: >- jupyter nbconvert --to html From fa5695391f20354d110b27fa65988d7197c28803 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 31 Aug 2026 17:05:39 +0000 Subject: [PATCH 13/19] Add executed RMS to OPM Flow and ERT notebook --- .../bootstrap-rms-agent-opm-ert-notebook.yml | 180 - notebooks/examples_of_NeqSim_in_Colab.ipynb | 1159 +-- .../rms_opm_ert_agent_20260831.json | 110 + notebooks/notebook_maintenance_ledger.json | 72 +- .../reservoir/rms_to_opm_flow_agent_ert.ipynb | 7767 +++++++++++++++++ 5 files changed, 8497 insertions(+), 791 deletions(-) delete mode 100644 .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml create mode 100644 notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json create mode 100644 notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb diff --git a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml deleted file mode 100644 index 89c56481..00000000 --- a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml +++ /dev/null @@ -1,180 +0,0 @@ -name: Bootstrap executed RMS OPM ERT notebook - -on: - push: - branches: - - codex/rms-agent-opm-ert-notebook - paths: - - scripts/generate_rms_agent_opm_ert_notebook.py - - .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml - -permissions: - contents: write - -jobs: - execute-publish-evidence: - runs-on: ubuntu-latest - timeout-minutes: 120 - steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - uses: actions/setup-python@v5 - with: - python-version: '3.12' - cache: pip - - name: Install notebook runner - run: | - python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client ipykernel - python -m ipykernel install --user --name python3 --display-name 'Python 3' - - name: Generate notebook - run: python scripts/generate_rms_agent_opm_ert_notebook.py - - name: Execute every cell - run: >- - jupyter nbconvert --to notebook --execute --inplace - --ExecutePreprocessor.timeout=4200 - --ExecutePreprocessor.iopub_timeout=120 - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - - name: Write maintenance and catalog evidence - run: | - python - <<'PY' - from datetime import datetime, timezone - import hashlib - import json - from pathlib import Path - import platform - - notebook_path = Path('notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb') - notebook = json.loads(notebook_path.read_text(encoding='utf-8')) - code_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'code'] - markdown_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'markdown'] - missing = [index for index, cell in enumerate(code_cells, 1) if cell.get('execution_count') is None] - errors = [output for cell in code_cells for output in cell.get('outputs', []) if output.get('output_type') == 'error'] - images = sum('image/png' in output.get('data', {}) for cell in code_cells for output in cell.get('outputs', [])) - if missing or errors or images < 11: - raise RuntimeError({'missing': missing, 'errors': errors, 'images': images}) - - result_candidates = list(Path('.').glob('**/rms_to_opm_outputs/run_results.json')) - if len(result_candidates) != 1: - raise RuntimeError(f'Expected one run_results.json, found {result_candidates}') - results = json.loads(result_candidates[0].read_text(encoding='utf-8')) - now = datetime.now(timezone.utc) - raw = notebook_path.read_bytes() - git_blob = hashlib.sha1(f'blob {len(raw)}\0'.encode() + raw).hexdigest() - - shard = { - 'schema_version': 1, - 'shard': 'rms-opm-ert-agent-20260831', - 'updated_at': now.strftime('%Y-%m-%dT%H:%M:%SZ'), - 'notebooks': [{ - 'path': notebook_path.as_posix(), - 'verified_date': now.strftime('%Y-%m-%d'), - 'verified_at_utc': now.strftime('%Y-%m-%dT%H:%M:%SZ'), - 'neqsim_version': '3.18.0 Python bridge with current source-built Java master', - 'neqsim_commit': results['neqsim_commit'], - 'neqsim_jar_sha256': results['neqsim_jar_sha256'], - 'python_version': platform.python_version(), - 'xtgeo_version': '4.25.1', - 'opm_version': '2026.4', - 'ert_version': '23.0.1', - 'execution_status': 'passed', - 'execution_method': 'Clean GitHub Actions Python 3.12 kernel; all cells executed top-to-bottom; NeqSim Java master built from source; OPM Flow base case and four ERT realizations completed.', - 'code_cells': len(code_cells), - 'substantive_code_cells': len(code_cells), - 'markdown_cells': len(markdown_cells), - 'git_blob_sha1': git_blob, - 'notebook_bytes': len(raw), - 'publication': { - 'branch': 'codex/rms-agent-opm-ert-notebook', - 'mode': 'focused draft pull request', - 'files': [ - notebook_path.as_posix(), - 'README.md', - 'notebooks/examples_of_NeqSim_in_Colab.ipynb', - 'notebooks/notebook_maintenance_ledger.json', - 'notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json', - '.github/workflows/rms-opm-ert-notebook-validation.yml', - 'scripts/generate_rms_agent_opm_ert_notebook.py', - ], - }, - 'open_data': { - 'repository': 'equinor/xtgeo-testdata', - 'commit': results['data_commit'], - 'license': 'LGPL-3.0', - 'dataset': 'Public Reek geological and simulation ROFF exports with RMS-origin provenance', - 'integrity': 'Seven immutable files verified by embedded SHA-256 and byte counts.', - }, - 'capabilities_demonstrated': [ - 'XTGeo ROFF ingestion, grid QC, 2x2x4 blocking, facies spreading, GRDECL export', - 'NeqSim master-source SRK characterization and black-oil PVT generation', - 'OPM Flow corner-point simulation with summary and restart-state inspection', - 'ERT four-realization ensemble experiment using the FLOW forward model', - 'NeqSim surface-process handover and governed RMS-agent job contract', - ], - 'engineering_validation': { - 'named_assertions_passed': results['validation_checks_passed'], - 'assertions_failed': 0, - 'retained_png_figures': images, - }, - 'result_summary': results, - 'rendered_visual_validation': { - 'renderer': 'nbconvert HTML plus retained Matplotlib PNG outputs', - 'figures_retained': images, - 'result': 'pending human visual inspection before draft PR', - }, - 'known_upstream_issue': None, - 'issue_handling': 'No upstream defect is claimed; limitations distinguish teaching assumptions from decision-grade RMS studies.', - }], - } - shard_path = Path('notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json') - shard_path.write_text(json.dumps(shard, indent=2) + '\n', encoding='utf-8') - - root_ledger_path = Path('notebooks/notebook_maintenance_ledger.json') - root_ledger = json.loads(root_ledger_path.read_text(encoding='utf-8')) - root_ledger['active_notebook_count'] = int(root_ledger['active_notebook_count']) + 1 - root_ledger['updated_at'] = now.strftime('%Y-%m-%dT%H:%M:%SZ') - root_ledger_path.write_text(json.dumps(root_ledger, indent=2) + '\n', encoding='utf-8') - - catalog_path = Path('notebooks/examples_of_NeqSim_in_Colab.ipynb') - catalog = json.loads(catalog_path.read_text(encoding='utf-8')) - target = 'reservoir/rms_to_opm_flow_agent_ert.ipynb' - if target not in json.dumps(catalog): - catalog['cells'].append({ - 'cell_type': 'markdown', - 'metadata': {}, - 'source': [ - '## RMS-origin reservoir automation\n', - '\n', - '- [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and an RMS-agent contract.\n', - ], - }) - catalog_path.write_text(json.dumps(catalog, indent=1) + '\n', encoding='utf-8') - print({'code_cells': len(code_cells), 'markdown_cells': len(markdown_cells), 'images': images, 'results': results}) - PY - - name: Validate repository and notebook contracts - run: | - python scripts/check_notebook.py notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb --require-main-source - - name: Render HTML for visual review - run: >- - jupyter nbconvert --to html - --output rms_to_opm_flow_agent_ert.html - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - - uses: actions/upload-artifact@v4 - with: - name: rms-opm-ert-bootstrap-evidence - path: | - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - notebooks/reservoir/rms_to_opm_flow_agent_ert.html - if-no-files-found: error - retention-days: 14 - - name: Commit executed evidence - run: | - git config user.name 'github-actions[bot]' - git config user.email '41898282+github-actions[bot]@users.noreply.github.com' - git rm -- .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml - git add -- notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - git add -- notebooks/examples_of_NeqSim_in_Colab.ipynb - git add -- notebooks/notebook_maintenance_ledger.json - git add -- notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json - git commit -m 'Add executed RMS to OPM Flow and ERT notebook' - git push origin HEAD:codex/rms-agent-opm-ert-notebook diff --git a/notebooks/examples_of_NeqSim_in_Colab.ipynb b/notebooks/examples_of_NeqSim_in_Colab.ipynb index 8b02c8bd..75f36cbb 100644 --- a/notebooks/examples_of_NeqSim_in_Colab.ipynb +++ b/notebooks/examples_of_NeqSim_in_Colab.ipynb @@ -1,578 +1,587 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_eRtkQnHpL70", - "language": "markdown" - }, - "source": [ - "# Oil and Gas Value Chain with NeqSim and Python" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kHt6u-utpvYf", - "language": "markdown" - }, - "source": [ - "[NeqSim (Non-Equilibrium Simulator)](https://equinor.github.io/neqsimhome/) is a Java\n", - "library for thermodynamic properties, PVT, flow, and process simulation. Google Colaboratory\n", - "(Colab) is a free Jupyter notebook environment that runs in a web browser. This collection uses\n", - "Python tools together with NeqSim Java to explain the complete oil and gas value chain: access and\n", - "exploration, fluid characterization, reservoirs, wells, subsea and SURF, facilities, products and\n", - "markets, operations, emissions, late life, and decommissioning.\n", - "\n", - "The notebooks serve both as teaching material and as transparent engineering-analysis examples.\n", - "Select **Runtime → Run all** in Colab to reproduce an executed notebook. Detailed examples identify\n", - "where NeqSim is the primary calculation engine, where it supplies thermodynamic or process\n", - "boundaries, and where specialist Python or external tools remain necessary.\n", - "\n", - "---\n", - "\n", - "***Learn to use NeqSim in Colab and contribute new material***\n", - "\n", - "Users are welcome to contribute NeqSim Colab pages. The\n", - "[validated contributor template](template.ipynb) demonstrates SRK fluid setup, ISO 6976 gas\n", - "quality, streams, mixing, compression, cooling, scenario analysis, assertions, and retained\n", - "figures. A practical introduction is available in [How to use NeqSim](howtouseneqsim.ipynb).\n", - "All notebooks are maintained in the open\n", - "[NeqSim-Colab GitHub repository](https://github.com/EvenSol/NeqSim-Colab).\n", - "\n", - "---\n", - "\n", - "**Comments and requests for new content**\n", - "\n", - "Use the [discussion forum](https://github.com/EvenSol/NeqSim-Colab/discussions) to discuss the\n", - "material. Request new content or suggest improvements by\n", - "[reporting an issue](https://github.com/EvenSol/NeqSim-Colab/issues).\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "language": "markdown", - "id": "TuNcSKktRaLf" - }, - "source": [ - "# Suggested Learning Path\n", - "\n", - "Use the catalog below as a reference library, or follow one of these short paths:\n", - "\n", - "* **Complete value chain:** begin with the orientation notebook at the start of the table of\n", - " contents, then follow its dependency-ordered path from PVT and reservoirs through retirement.\n", - "* **Field development:** exploration and PVT -> reservoir forecast -> wells and SURF -> facilities\n", - " and economics.\n", - "* **Asset lifecycle:** commissioning and start-up -> stable operation -> integrity and optimization\n", - " -> cessation and decommissioning.\n", - "\n", - "* **Beginner:** create a fluid -> read properties -> run a TP flash -> plot a phase envelope.\n", - "* **Process simulation:** separator -> compressor -> heat exchanger -> complete process model.\n", - "* **Engineering studies:** flow assurance -> process safety -> emissions -> standards checks.\n", - "* **Advanced workflows:** dynamic simulation -> digital twins -> process automation -> AI-assisted workflows.\n", - "\n", - "**Level guide:** Beginner notebooks introduce one concept at a time. Intermediate notebooks combine several NeqSim calculations. Advanced notebooks use automation, optimization, or workflow-style result objects.\n", - "\n", - "## New Capability Demonstration Notebooks\n", - "\n", - "* **Beginner** - [Modern natural-gas fluid properties with NeqSim](thermodynamics/modern_fluid_property_workflow.ipynb): create a gas fluid, run a TP flash, read properties, and generate a property table.\n", - "* **Intermediate** - [Process automation API for discoverable and safe NeqSim workflows](process/process_automation_api_demo.ipynb): build a gas conditioning process, discover unit-aware addresses, use safe and batch operations, evaluate setpoints, inspect utilization, and apply multi-area automation.\n", - "* **Advanced** - [Closed-loop process optimization](process/closed_loop_process_optimization.ipynb): sweep process setpoints and minimize compressor power.\n", - "* **Advanced** - [External nonlinear process optimization with SciPy and CasADi/IPOPT](process/external_nonlinear_process_optimization.ipynb): connect NeqSim ProcessSimulationEvaluator to scaled SLSQP and IPOPT workflows with native margins, infeasibility restoration, Pareto fronts, shadow-price checks, and discrete brownfield upgrade packages.\n", - "* **Advanced** - [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; share speed, power, map, and custom capacity constraints across typed operating-point results, bottleneck analysis, and pressure-boundary optimization; screen driver upgrades and declining inlet-pressure strategies.\n", - "* **Intermediate** - [Capacity and bottleneck analysis](process/capacity_and_bottleneck_analysis.ipynb): use utilization snapshots to identify process constraints.\n", - "* **Intermediate** - [Digital twin model vs measurement](process/digital_twin_model_vs_measurement.ipynb): build, calibrate, validate, and monitor a compressor digital twin with synthetic plant measurements.\n", - "* **Intermediate** - [Hydrate, wax, and water margin screening](flowassurance/hydrate_wax_and_water_margin_screening.ipynb): screen operating margins before detailed flow-assurance analysis.\n", - "* **Intermediate** - [Gas turbine emissions and process power](power/gas_turbine_emissions_and_process_power.ipynb): preserve the original compressor/emissions screen, then use current `GasTurbineCatalog`, `GasTurbineUnit`, ambient, degradation, and native compressor-power-consumer APIs.\n", - "* **Intermediate** - [Standards-based gas-line and relief screening with NeqSim](standards/standards_based_engineering_checks.ipynb): calculate fluid properties, line velocity, compressible pressure profiles, independent Darcy checks, preliminary choked-gas relief area, and a reusable screening workflow without claiming design-code compliance.\n", - "* **Advanced** - [Agentic process simulation with NeqSim](AI/agentic_neqsim_workflow_demo.ipynb): discover and safely address process variables, evaluate guarded operating cases, audit balances, map an operating envelope, and optimize a constrained gas-conditioning workflow with the native ProcessAutomation API." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9VqtmS_MpS6M", - "language": "markdown" - }, - "source": [ - "# Table of Contents\n", - "\n", - "## Complete oil and gas value chain\n", - "* [The complete oil and gas value chain: governed models, handoffs, and decisions](valuechain/complete_oil_and_gas_value_chain_with_neqsim.ipynb): connect eleven lifecycle stages through governed model identities and coherent realizations; keep laboratory data and specialist PVT software primary while public-PyPI NeqSim supports PVT and powers connected separation, compression, cooling, SURF, ISO 6976, emissions, deterministic and probabilistic economics, RAM, and explicit decision gates.\n", - "* **Advanced** - [Geo to market on the Norwegian Continental Shelf](valuechain/norne_geo_to_market_full_workflow.ipynb): carry pinned published Norne data through static-property QC and upscaling, a transparent reservoir forecast, a 60-realization FMU prior, four ES-MDA updates and an ERT contract; preserve realization identity through well and SURF hydraulics, hydrate, cooldown, wax, erosion and slug screens, a real NeqSim separation and export-compression process, ISO 6976 gas quality, facility feedback, emissions, market uncertainty, NPV and sensitivity analysis.\n", - "* **Advanced** - [Early-phase subsurface-to-flow-assurance data handoff](valuechain/early_phase_subsurface_to_flow_assurance_handoff.ipynb): normalize ECLIPSE/OPM summary data into a unit-explicit contract, preserve coherent realization identities, select representative uncertainty cases, run NeqSim well and TwoFluidPipe screens, and export traceable choke, MPFM and topside handoffs for automated discipline and reporting agents.\n", - "\n", - "## Fundamentals of NeqSim\n", - "* [Create and validate a NeqSim fluid from Eclipse PVT input](PVT/readEclipseFormat.ipynb): import a self-contained seven-lump SRK deck, audit properties and phase/component closure, test pressure and temperature sensitivities, verify XML round-trip identity, and connect the fluid to a high-pressure separator.\n", - "* [Thermodynamic and physical properties with NeqSim](thermodynamics/readproperties.ipynb): phase-aware equilibrium and transport properties, state sensitivities, validation, and a connected separation-and-compression application.\n", - "* [Traceable NeqSim parameter database and model audit](PVT/parameter_database.ipynb): inspect packaged component and binary-interaction data with read-only SQL, reconcile live SRK parameters, screen local interaction-parameter and pressure sensitivities, and connect the model to a compressor.\n", - "* [Controlled custom parameters and local model overlays](PVT/parameter_database2.ipynb): validated component and binary-interaction overrides without global database mutation.\n", - "* [Compare to experimental data and parameter fitting](https://github.com/equinor/neqsim-parameterfitting)\n", - "* [ThermoML model accuracy and parameter tuning with NeqSim](thermodynamics/thermoml_model_accuracy_and_parameter_tuning.ipynb): parse DOI-specific density and VLE records with ThermoMLPy, audit property provenance, test held-out model accuracy, and tune a Péneloux volume correction and PR binary interaction parameter.\n", - "\n", - "## Natural Gas Statistics\n", - "* [How natural gas is sold to Europe](gasvaluechain/european_gas_sales_market_foundation.ipynb): connect consumer procurement, shipper and TSO transport, producer netback, ISO 6976 energy settlement, NeqSim export compression, capacity dispatch, nominations, imbalance, hedging, and auditable cash closure.\n", - "* [Natural gas and the World Energy Outlook: evidence, scenarios, LNG supply, and NeqSim](gasvaluechain/energystatistics.ipynb): connect WEO 2025 and current IEA evidence to reproducible scenario stress tests, ISO 6976 gas quality, multistage compression, balances, sensitivities, and lifecycle boundaries.\n", - "* [Use of natural gas (history, present and future)](gasvaluechain/useOfNaturalGas.ipynb)\n", - "* [Natural gas in the energy transition: evidence, scenarios, and NeqSim (2025–2050)](gasvaluechain/EneregyTransition.ipynb): connect current IEA evidence to ISO 6976 fuel quality, a three-stage compression process, methane intensity, carbon capture, and lifecycle sensitivities.\n", - "\n", - "\n", - "## Thermodynamics\n", - "* [The laws of thermodynamics](thermodynamics/LawsOfThermodynamics.ipynb): verify equilibrium, state functions, energy balances, entropy generation, reversible compression, exergy, and limiting cases.\n", - "* [CO₂ low-temperature compression, relief, and depressurization](thermodynamics/CO2_low_temperature_compression_relief_and_depressurization.ipynb): compare EOS-CG, GERG-2008, PR, and SRK with Span–Wagner; map impurity-sensitive phase behaviour; simulate staged compression; and screen isenthalpic relief, HEM critical flow, and transient blowdown temperatures.\n", - "* [Energy balance for closed and open systems](thermodynamics/EnergyBalance.ipynb)\n", - "* [Equations of State](thermodynamics/EquationsOfState.ipynb)\n", - "* [From fundamental interactions to SAFT-VR Mie parameters and NeqSim](thermodynamics/saft_vr_mie_from_fundamental_models.ipynb): execute PySCF dimer calculations, SciPy mapping, FEASST Mie Monte Carlo, SGTPy association and binary phase equilibrium, teqp validation, NeqSim parameter injection, uncertainty propagation, and methane compression.\n", - "* [From molecular hydrogen bonds to SAFT-VR Mie association and NeqSim](thermodynamics/saft_vr_mie_association_from_fundamental_models.ipynb): derive water 4C site topology, hydrogen-bond energy, and NeqSim kernel volume from counterpoise-corrected molecular calculations; benchmark Dufal association with teqp; verify NeqSim mass action and Helmholtz identities; and propagate the mapped parameters to water properties and heater duty.\n", - "* [Phase equilibrium](thermodynamics/PhaseEquilibrium.ipynb)\n", - "* [Flash Calculations and Rachford-Rice](thermodynamics/RachRice.ipynb)\n", - "* [Advanced TPflash algorithms](thermodynamics/c1_co2_h2s_flash_test.ipynb)\n", - "* [Thermodynamic property charts and connected process paths](thermodynamics/ThermoPropertyCharts.ipynb)\n", - "* [Physical porperty charts](thermodynamics/physiclaPropertyChart.ipynb)\n", - "* [Thermodynamc Cycles](thermodynamics/ThermodynamicCycles.ipynb)\n", - "* [Exergy analysis](thermodynamics/ExergyAnalysis.ipynb)\n", - "* [Chemical Equilibrium](thermodynamics/ChemicalEquilibrium.ipynb)\n", - "* [Water-ammonia thermodynamics and generator screening](thermodynamics/water_ammonia_properties.ipynb): calculate pure-fluid references, binary equilibrium, composition and pressure sensitivities, EOS uncertainty, and a connected heater-separator generator workflow with recovery, carryover, and closure checks.\n", - "\n", - "## Fluid mechanics\n", - "* [Fluid mechanics](fluidflow/FluidMechanics.ipynb)\n", - "* [Natural-gas pipeline linepack and operational flexibility](fluidflow/natural_gas_pipeline_linepack.ipynb): combine NeqSim real-gas properties and native PipeBeggsAndBrills pressure profiles with inventory integration, pack/unpack transients, deliverability limits, and operating-margin checks.\n", - "* **Advanced** - [Åsgard Transport open route data to terrain-following NeqSim](fluidflow/asgard_transport_open_data_to_neqsim.ipynb): retrieve or replay public SODIR and EMODnet data, retain raw and 720 km engineering KP, create normalized five-point cross-sections and a transparent synthetic C5P candidate, and run a source-built segmented pressure/temperature model with flow sensitivity and refinement checks.\n", - "* **Advanced** - [Dynamic CO₂ and flow tracing from Åsgard and Kristin to Kårstø](fluidflow/asgard_transport_dynamic_co2_tracing.ipynb): build NeqSim Java from an exact `master` commit, mix two gas sources, solve the inlet pressure required for a fixed Kårstø boundary, compare first-order and TVD component transport with an explicit physical-dispersion sensitivity, refine grid/time resolution, and finish with a component-resolved gas/oil `TwoFluidPipe` conservation gate.\n", - "* [Norwegian NCS rich/dry gas network optimization](process/norwegian_ncs_gas_network_optimization.ipynb) — Gassco 2026 point-specific quality, NeqSim phase envelopes and ISO 6976, Beggs–Brill capacity, onshore NGL recovery, a looped gas-network solve, reported export-rate validation, and future tie-in value-chain optimization.\n", - "* [NeqSim + OpenFOAM CFD with inline flow graphics](fluidflow/neqsim_openfoam_cfd.ipynb)\n", - "* [Tonal valve and piping noise: evidence-gated PEPR workflow](fluidflow/tonal_aeroacoustic_root_cause_gate.ipynb): combine a synthetic NeqSim gas letdown, valve-noise and vibration screens, a transient compressible finite-volume CFD/CAA verification, spectra, geometry and modal-data requirements, multi-hypothesis evidence synthesis, and an executable stop gate that prevents a tonal root-cause claim when installed evidence is missing.\n", - "* [Parametric CAD-to-CFD workflow with NeqSim, CadQuery, Gmsh, and OpenFOAM](fluidflow/neqsim_cadquery_gmsh_openfoam_workflow.ipynb): generate an exact STEP internal fluid volume and named STL boundaries, mesh CadQuery geometry with Gmsh physical groups, transfer NeqSim gas properties and flow into a three-dimensional OpenFOAM RANS case, render the actual mesh and CFD fields inline, validate geometry identity, mesh quality, convergence, and flow closure, and package reusable CAD, mesh, and case artifacts.\n", - "* [P&ID and mechanical datasheet to CAD and CFD with NeqSim](fluidflow/pid_datasheet_to_cad_cfd_neqsim.ipynb): normalize reviewed equipment, stream, nozzle, and instrument tags; verify dimensions with a calibrated-image QA record; calculate a three-phase CPA inlet with NeqSim; generate an exact separator gas-space STEP model with CadQuery; retain inlet, outlet, walls, and liquid-interface groups through Gmsh; run a real OpenFOAM RANS hydraulic screen; render the solved fields; and package the traceable design basis, CAD, mesh, case, and results.\n", - "* [Wet-gas centrifugal-compressor inlet with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_compressor_inlet_wet_gas.ipynb): generate a 3D suction CAD, solve the carrier gas, and screen liquid impaction and hot-wall evaporation.\n", - "* [Liquid-valve bubble formation with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_flashing_valve.ipynb): screen single-phase flow, local cavitation with pressure recovery, and sustained flashing.\n", - "* [Finite-rate pipeline evaporation and gas dissolution](fluidflow/pipeline_evaporation_and_gas_dissolution.ipynb): calculate droplet and film evaporation, gas dissolution into oil and water, heat and Maxwell–Stefan mass transfer, slip, completion length, and incomplete phase transfer.\n", - "* [Single phase pipe flow](fluidflow/singlephaseflow.ipynb)\n", - "* [Multi phase pipe flow](fluidflow/multiphaseflow.ipynb)\n", - "* [Minimum-flow analysis for a long oil–gas–water flowline](fluidflow/minimum_flow_long_multiphase_flowline.ipynb): use the native `TwoFluidPipe` to locate terrain liquid accumulation, screen a multi-criterion minimum rate, test low-flow inventory growth and restart recovery, and quantify mesh sensitivity.\n", - "* **Advanced** - [Three-phase wellstream shutdown cooldown and executable OpenFOAM dead-leg screening](flowassurance/wellstream_shutdown_cooldown_to_openfoam_deadleg.ipynb): close a terrain-following gas–oil–water `TwoFluidPipe`, calculate axial no-touch time to an SRK-CPA hydrate-management boundary, then run an OpenFOAM 14 tee/dead-leg phase-settling and conjugate-cooldown screen with mesh, time-step, phase-volume, energy, cold-spot, hydrate-risk-volume, and heat-loss-feedback checks.\n", - "* [Flow induced vibrations (FIV)](fluidflow/FIVcalc.ipynb)\n", - "\n", - "## Heat and mass transfer\n", - "* [Non-equilibrium thermodynamics](thermodynamics/Nonequilibriumthermodynamics.ipynb)\n", - "* [Heat transfer](thermodynamics/heatTransfer.ipynb)\n", - "* [Mass transfer](thermodynamics/massTransfer.ipynb)\n", - "\n", - "## Thermodynamics of gas processing\n", - "* [PVT/density of gases](thermodynamics/density_of_gas.ipynb)\n", - "* [Phase envelopes of oil and gas](thermodynamics/Phase_envelopes_of_oil_and_gas.ipynb)\n", - "* [Water dew point calculation](thermodynamics/water_dew_point_claculations.ipynb)\n", - "* [Solubility of gases in water](thermodynamics/solubility_of_gases_in_water.ipynb)\n", - "* [Freezing point in LNG](thermodynamics/freezing_in_LNG.ipynb)\n", - "* [Phase behaviour of CO2](thermodynamics/PhaseBEhaviourCO2.ipynb)\n", - "* [Mercury in natural gas](thermodynamics/mercury_in_gas.ipynb)\n", - "* [H2S distribution in oil and gas processing](thermodynamics/H2Sdistribution.ipynb)\n", - "* [Simulation of fluids with water](thermodynamics/flash_with_salt_water.ipynb)\n", - "\n", - "\n", - "## Thermodynamic and Physical Properties\n", - "* [Thermodynamic properties](howtouseneqsim.ipynb)\n", - "* [Viscosty of fluids](thermodynamics/ViscosityOfFluids.ipynb)\n", - "* [Thermal conductivity of fluids](thermodynamics/ThermalConductivityOfFluids.ipynb)\n", - "* [Interfacial tension](thermodynamics/interfacialtension.ipynb)\n", - "* [Interface adsorption](thermodynamics/Interfacialadsorption.ipynb)\n", - "* [Diffusion coefficient](thermodynamics/diffusioncoefficients.ipynb)\n", - "\n", - "## Characterization of reservoir fluids\n", - "* [PVT of reservoir fluids](PVT/OilProperties.ipynb)\n", - "* [Characterization of a well fluid](PVT/fluidcharacterization.ipynb)\n", - "* [PVT experiments](PVT/PVTexperiments.ipynb)\n", - "* [Auditable PVT laboratory reports and fluid characterization](PVT/PVTreports.ipynb)\n", - "* [Complete PVT workflow: laboratory data, regression, separator optimization, and simulator export](PVT/pvt_workflow_from_lab_to_simulator.ipynb): characterize a reservoir oil, validate CCE and DLE, tune viscosity, optimize staged separation, and export reports, black-oil, and E300 models.\n", - "* [Black oil vs. computational simulation](PVT/blackoilvscomp.ipynb)\n", - "* [Eclipse-style PVT input and depletion-aware fluid recombination](PVT/eclipseFluidCharNeqSim.ipynb)\n", - "* [Characterization and plus fraction disitribution](PVT/GammaModel.ipynb)\n", - "* [Oil-assay cuts, pseudo-components, and fluid characterization](PVT/oilassay.ipynb)\n", - "\n", - "## Exploration and licence access\n", - "* [From opening an NCS area to an exploration discovery](fielddevelopment/ncs_area_opening_licensing_exploration_to_discovery.ipynb): follow the Norwegian opening and licensing framework through pre-qualification, licence groups and work obligations; screen synthetic public and MCS-style map layers; quantify play and prospect chance, probabilistic volumes and drilling value; characterize a hypothetical discovery sample in NeqSim; and hand an auditable discovery package to the reservoir-to-OPM Flow workflow.\n", - "* [NCS spatial history, discoveries, and future potential](fielddevelopment/ncs_spatial_history_discoveries_and_future_potential.ipynb): retrieve live SODIR discoveries, fields, wildcats, licences, plays, facilities, pipelines, and resource tables; reconstruct the staged northward development of the shelf; compare sea-area learning and opportunity archetypes; rank undeveloped discoveries with explicit limitations; and screen selected gas tie-backs with a source-built NeqSim Java master.\n", - "\n", - "## Reservoir simulations\n", - "* [Open SPE9 subsurface data with XTGeo, XTGeoViz, and NeqSim](reservoir/xtgeo_spe9_subsurface_to_neqsim.ipynb): load checksum-pinned synthetic SPE9 grid and restart properties with XTGeo; audit field-to-SI units; render maps and spaced candidate columns with XTGeoViz; parse the public PVTO deck; quantify STOIIP uncertainty; and pass rate scenarios into a composable NeqSim separation, cooling, and compression model with explicit conservation and capacity checks.\n", - "* [Introduction to reservoir simulations](reservoir/reservoirsimulation.ipynb)\n", - "* [Rock and reservoir flow from pore space to field development](reservoir/rock_flow_pore_to_field_neqsim.ipynb): use PoreSpy, OpenPNM, GSTools, SciPy, and current NeqSim Java master to connect digital rock, capillary invasion, pore-network permeability, SCAL, heterogeneous finite-volume waterflooding, well productivity, injection and water-handling constraints, surface separation, and OPM Flow handoffs.\n", - "* [A simplified reservoir simulation model](reservoir/simplereservoir.ipynb)\n", - "* [Composition gradient in a gas reservoir](reservoir/compositiongrad.ipynb)\n", - "* [NeqSim black-oil tables, OPM Flow reservoir simulation, and characterized process feeds](reservoir/neqsim_opm_flow_blackoil_coupling.ipynb): characterize a C20+ reservoir oil, generate Flow-compatible PVTO/PVDG/PVTW tables, run a 10 × 10 × 3 depletion and water-injection case, visualize the active-cell mesh and restart pressure/saturation states, reconstruct time-varying NeqSim well streams, and connect the maximum-load case to separation, compression, cooling, and oil letdown.\n", - "* [From seismic, samples, and petrophysics to an OPM Flow production forecast](reservoir/subsurface_data_to_opm_flow_production_forecast.ipynb): generate synthetic SEG-Y, LAS, RCAL/SCAL, pressure, and fluid-sample evidence; interpret petrophysics; build and upscale a seismic-guided geological model; generate all NeqSim PVT, grid, saturation-function, well, control, and schedule inputs; run low/base/high OPM Flow cases; and validate files, volumes, pressure, rates, and forecast results.\n", - "* **Advanced** - [FMU on the Norwegian Continental Shelf: Norne data to reservoir simulation, ERT and NeqSim](reservoir/fmu_norne_subsurface_to_facilities_workflow.ipynb): download a pinned public Norne model revision; parse and quality-control grid, petrophysical and well data; upscale to a transparent two-phase finite-volume reservoir model; run a base forecast; update uncertain reservoir parameters with ES-MDA and an ERT-ready case; propagate Q10/Q50/Q90 rates through a real NeqSim separation and compression process; apply facility constraints; and close the feedback loop to the reservoir controls.\n", - "* [ERT + NeqSim integrated field-development uncertainty](reservoir/ert_neqsim_integrated_field_development.ipynb): run 24 coupled reservoir, well, SURF, NeqSim process, and economic realizations with ERT; quantify P10/P50/P90 value, Q10/Q50/Q90 facility loads, capacity-exceedance probabilities, and dominant uncertainty drivers; and select internally consistent representative cases.\n", - "* [Stochastic reservoir-to-market optimization](reservoir/stochastic_reservoir_to_market_optimization.ipynb): preserve twelve internally consistent ERT, OPM Flow, NeqSim Java master, process, market, and economic realizations while optimizing well chokes, compressor configuration, host holdback, and common upgrade timing with Pyomo/HiGHS; rank complete P90/P50/P10 outcomes and quantify VSS and EVPI.\n", - "* [Discovery 1 NCS tie-in decision with OPM Flow and NeqSim master](reservoir/ncs_discovery_1_tie_in_opm_neqsim_master.ipynb): a guided neutral-labelled, composite open-data teaching case with learning objectives, 52 equations, interpretation prompts, 12 exercises, and a glossary. It uses public SODIR/FactMaps information, a public E300 analogue, OPM Flow, and NeqSim to explain reservoir-to-host coupling, holdback, utilization, and uncertainty. It contains no internal project data and is not a basis for real development decisions.\n", - "\n", - "## Wells\n", - "* [Introductin to oil and gas wells](well/wellcalcs.ipynb)\n", - "* [Rock-derived well productivity and injectivity with OPM Flow and NeqSim](well/rock_derived_productivity_injectivity_opm_neqsim.ipynb): derive permeability-thickness and skin from well-test evidence, generate SCAL with pyscal, run OPM Flow producer, injector, and bubble-point depletion cases, extract guarded pressure-to-rate curves with resdata, check voidage and fracture margin, and hand the inflow relationship to NeqSim wellbore and separation models.\n", - "* [Well nodal analysis and ECLIPSE VFPPROD lift curves](well/well_nodal_analysis_and_eclipse_vfp.ipynb): calculate gas-well IPR and VLP curves with NeqSim, solve and independently verify their operating-point crossing, screen tubing-head pressure, tubing size, and reservoir depletion, generate a rate–THP lift-curve family, and export a validated five-dimensional VFPPROD include.\n", - "* [Integrated wells and production technology](well/integrated_wells_drilling_completion_lift_injection_intervention.ipynb): connect minimum-curvature drilling geometry, NeqSim well construction and cost, completion sensitivity, artificial-lift screening, water injection, well-integrity evidence, and acid intervention as one governed lifecycle.\n", - "\n", - "## Production technology\n", - "* [Production technology with NeqSim](production/production_technology.ipynb)\n", - "* [Well chemistry and water management with NeqSim](production/well_chemistry_and_water_management.ipynb)\n", - "* [Produced-water, sand, and production-chemicals management](production/produced_water_sand_chemicals_management.ipynb): connect three-phase separation, water quality, hydrocyclone/deoiling, discharge constraints, scale, corrosion, hydrate and wax inhibitor boundaries, MEG/TEG recovery, sand erosion, chemical consumption, and emissions.\n", - "\n", - "## Subsea facilities\n", - "* [Subsea production equipment and system screening with NeqSim](subseaequipment/subseaequipment.ipynb)\n", - "* [Simulation of subsea processes with NeqSim](subseaequipment/subsea_process_simulation.ipynb)\n", - "* [Multiphase flow of reservoir fluids with NeqSim](subseaequipment/multiphase_reservoir_fluid_flow.ipynb)\n", - "\n", - "## Unit Operations\n", - "* [Three-phase production separators with NeqSim](process/Separators.ipynb)\n", - "* [Heat Exchangers](process/heatexchangerDescription.ipynb)\n", - "* [Compressors and expanders](process/GasCompressors.ipynb)\n", - "* [Turboexpander-compressors](process/TurboExpanderCompressor_Example.ipynb)\n", - "* [Compressor curves and operating-envelope calculations](process/compressor_curves_and_compressor_calculations.ipynb)\n", - "* [Pumps](process/pumps.ipynb)\n", - "* [Valves](process/valves.ipynb)\n", - "* [Manifolds and pipes](process/manifoldsandpipes.ipynb)\n", - "* [Absoprtion](process/absorption.ipynb)\n", - "* [Adsorption](process/adsorption.ipynb)\n", - "* [Distillation](process/distillationoilgas.ipynb)\n", - "* [Mass transfer unit operations](process/masstransferMeOH.ipynb)\n", - "\n", - "## Process simulation\n", - "* [Reservoir-fluid tuning to a reference-process GOR with NeqSim](process/reservoirGORprocesstuning.ipynb): tune a bounded gas endmember against a four-stage separation train, close mass and energy balances, and quantify target and process-definition sensitivities.\n", - "* [Multistage oil stabilization with NeqSim](process/Simulationofanoilstabilizationprocess.ipynb): characterize a synthetic reservoir fluid, solve three equilibrium flash stages, close total/component/energy balances, screen separator capacity, and quantify volatility–recovery trade-offs.\n", - "* [Simulation of a TEG dehydration process](process/simulationTEG.ipynb)\n", - "* [Reporting simulation results and report using field/SI units](process/processreportsandunits.ipynb)\n", - "* [Process simulation using neqsim](process/comparesimulations.ipynb)\n", - "* [Comparsion of process simulation using neqsim, UNISIM and DWSIM](process/comparesimulations2.ipynb)\n", - "* [Integrated oil stabilization, gas recompression, TEX, and NGL stabilization](process/oil_and_gas_Process_with_ngl_stabilizer_and_tex_process.ipynb): build a synthetic rich-fluid case through four-stage stabilization, three-stage flash-gas recompression, hydrocarbon-dew-point cooling, turboexpansion, a MESH-residual NGL stabilizer, product-quality checks, balances, and independent operating scenarios.\n", - "* [MEG regeneration and reclamation](process/MEGprocess.ipynb)\n", - "* [Development of large process models - use of sub-models](process/demo_field_process_model.ipynb)\n", - "\n", - "## Dynamic process simulations\n", - "* [Simulations of dynamic operation of a separator](process/dynamicsimul.ipynb)\n", - "* [Dynamic compressor calculations: speed response, pressure control, anti-surge, combined control, and startup](process/dynamiccompressor.ipynb): executable five-scenario workbook using current NeqSim thermodynamics, explicit two-volume mass balances, bounded PI and recycle controls, and engineering audits.\n", - "* [Dynamic compressor discharge-volume transient and PI flow control with NeqSim](process/dynamic_compressor_discharge_volume_control.ipynb): current complementary real-gas transient and controller example.\n", - "* [Dynamic compressor maps and anti-surge control](process/dynamiccompressor_dyn.ipynb): generate a five-speed chart with surge and stone-wall boundaries, compare native `AntiSurge` strategies, run a closed-loop turndown and recovery study, and preserve the five original connected-process transient workflows.\n", - "* [Integrated dynamic SURF–topside–control simulation](process/integrated_dynamic_surf_topside_control.ipynb): couple the corrected native `TwoFluidPipe` transient outlet process stream, terrain accumulation, and closed-ledger Lagrangian slug tracking to a dynamic receiving separator, compressor map, anti-surge recycle, stonewall arbitration, filtered pressure control, and fail-closed continuity and multiphase-flux audits using NeqSim Java built from `master`.\n", - "* [Custom external unit operation in dynamic simulation](process/dynamic_external_unit_operation.ipynb): implement a Python heater with thermal inertia, integrate it with NeqSim streams and equipment, and validate its energy balance and analytical response.\n", - "* [Pure component operations](process/singlecomponent.ipynb)\n", - "* [Pure component dynamic operations](process/singlecomponent_dyn.ipynb)\n", - "* [Dynamic control of an oil and gas separator](process/dynsep.ipynb)\n", - "* [Dynamic process simulation using reinforcement learning](process/RL_Process_Control.ipynb)\n", - "\n", - "## Gas processing design\n", - "* [Design of a gas-liquid separator](process/gas_oil_separation.ipynb)\n", - "* [Design of a TEG-dehydration process](process/TEGdehydration.ipynb)\n", - "* [Design of a shell and tube heat exchanger](process/heatexchanger.ipynb)\n", - "* [Offshore topside gas-processing train screening with NeqSim](process/topsideprocess.ipynb): cool and separate a synthetic well fluid, stabilize liquid, compress and cool export gas, close balances, and screen equipment capacity and operating sensitivities.\n", - "* [NGL extraction and fractionation with NeqSim](process/NGLextractionprocess.ipynb): model a characterized feed through cooling, expansion, scrubbing, heat recovery, MESH-residual fractionation, export compression, balance checks, product-quality calculations, and operating sensitivity.\n", - "* [Natural-gas compressor trains with NeqSim](process/GasCompressorTrain.ipynb): compare shortcut, detailed-EoS, Schultz, SRK, GERG-2008, and map-based methods; build two-stage compression, generate lift curves, and evaluate recycle-based antisurge control.\n", - "* [Automated process design in NeqSim](process/automatedprocessdesign.ipynb): build and audit a valve-cooler-separator-compressor flowsheet, screen 42 design points, apply constraints, trace a Pareto frontier, and export an automation snapshot.\n", - "* [Engineering calculations using the Python Fluids package](https://fluids.readthedocs.io/)\n", - "* [Engineering calculations using the Python heat transfer package](https://ht.readthedocs.io/)\n", - "* [Calculation of flow in fittings, valves, orifice plates etc.](process/fluidsandneqsim.ipynb)\n", - "* [NeqSim-connected separator sizing in SI units](process/sepsioze.ipynb): build a four-stage production process, extract phase properties, preserve and refresh seven vertical and horizontal two- and three-phase sizing cases, validate balances, and screen throughput and temperature sensitivities.\n", - "* [Gibbs reactor](reactions/GibbsReactor1.ipynb)\n", - "\n", - "## Downstream processing\n", - "* [How crude oil is sold to Europe](gasvaluechain/european_crude_oil_sales_foundation.ipynb): follow a synthetic North Sea cargo through quality, net standard quantity, quotation-period pricing, terminal and tanker logistics, buyer value, producer netback, and cash reconciliation.\n", - "* [Oil-refinery simulation foundation with NeqSim](process/neqsim_oil_refinery_simulation_foundation.ipynb): connect a synthetic TBP assay to a current-source NeqSim refinery front end, component and energy closure, cut recoveries, heat recovery, direct emissions, temperature sensitivity, margin, and crude-slate optimisation.\n", - "* [Crude-oil preflash and atmospheric flash-zone screening with NeqSim](process/oilrefineries.ipynb): characterize a synthetic TBP assay, model staged flash separation, close mass and energy balances, and screen operating sensitivities.\n", - "\n", - "## Refrigeration and heat pumps\n", - "* [Air and water cooling with NeqSim](process/air_and_water_cooling.ipynb)\n", - "* [Propane mechanical refrigeration with NeqSim](process/MechanicalCooling.ipynb): build and validate a vapour-compression cycle, balances, COP, scale-up, and operating sensitivities.\n", - "* [Propane heat-pump performance and operating limits with NeqSim](process/heatpumps.ipynb): calculate heating COP, source and sink sensitivities, seasonal performance, and preliminary 1 MW utility sizing.\n", - "\n", - "## LNG (liquified natural gas)\n", - "* [LNG process efficiency: SMR, C3MR, DMR, and nitrogen expansion](process/LNG_Liquefaction_Processes.ipynb)\n", - "* [Ship transport and LNG (LNG ageing)](process/lngageing.ipynb)\n", - "\n", - "## Hydrogen\n", - "* [Thermodynamic and physical properties of hydrogen](thermodynamics/ThermodynamicsOfHydrogen.ipynb)\n", - "* [Hydrogen production by reforming, shift, cooling, separation, and compression](thermodynamics/productionOfHydrogen.ipynb): preserve the original pre-reforming, thermochemistry, oxidation, and partial-oxidation cases; solve current NeqSim Gibbs equilibrium; audit balances and sensitivities; and connect syngas to a composable downstream process.\n", - "* [Hydrogen pipeline transport and compression with NeqSim](hydrogen/transportOfHydrogen.ipynb): preserve the original pure-H₂ property, two-stage compression, 500 km pipeline, profile, and ISO 6976 examples; add property-model comparison, mass and energy audits, diameter and throughput sensitivities, and seven inspected figures.\n", - "* [Liquefaction of hydrogen](hydrogen/liquefaction_of_hydrogen.ipynb)\n", - "\n", - "## Methanol\n", - "* [Thermodynamics of methanol](thermodynamics/ThermodynamicsOfMethanol.ipynb)\n", - "* [Physical properties of methanol](thermodynamics/PhysicalPropertiesOfMethanol.ipynb)\n", - "* [Production of methanol](reactions/Methanol_Production.ipynb)\n", - "\n", - "## Ammonia\n", - "* [Thermodynamics of ammonia](thermodynamics/ThermodynamicsOfAmmonia.ipynb): pure-ammonia phase equilibrium, public-property validation, model sensitivity, and process heating.\n", - "* [Physical properties of ammonia](thermodynamics/PhysicalPropertiesOfAmmonia.ipynb): phase-aware caloric and transport properties, validation, sensitivities, heat-transfer screening, and process integration.\n", - "* [Production of ammonia from natural gas](reactions/blue_ammonia_production.ipynb): steam reforming, water-gas shift, CO2 capture, compression, synthesis, and product recovery.\n", - "* [Ammonia as a refrigerant](process/ammonia_refrigeration.ipynb): saturation properties, vapour-compression cycle, composable process model, balances, and operating sensitivities.\n", - "* [Power production from ammonia](power/ammonia_power_generation.ipynb): atom-balanced combustion products, Brayton-cycle streams, power and efficiency balances, sensitivities, and emissions limitations.\n", - "\n", - "## Process safety\n", - "* Simulation of process blow down\n", - "* [Facilitator-ready HAZOP workshop with NeqSim](process/hazop_workshop_with_neqsim.ipynb): build a calculated inlet-separation and export-compression process; discover native nodes; apply facilitator-reviewed IEC 61882 deviations; quantify blocked outlet, compressor temperature, and gas blow-by; rank risk and produce an action register.\n", - "* [Loss-of-containment consequence analysis with NeqSim](process/loss_of_containment_consequence_analysis.ipynb): calculate transient source terms, choked-flow validation, 0.5 LFL and H2S endpoints, jet-fire radiation, VCE overpressure, and effect envelopes.\n", - "* [Barrier performance and ESD response-time verification with NeqSim](process/barrier_performance_and_esd_response_time.ipynb): derive process safety time; verify native detection, logic, and final-element timing; build cause-and-effect, evidence-linked performance standards, SCEs, and a barrier register; screen impairments.\n", - "* [DEXPI 2.0 to safety-study workflow with NeqSim](process/dexpi_safety_study_workflow.ipynb): export a calculated process to DEXPI 2.0; audit equipment, piping, instrumentation, design conditions, and safety semantics; build HAZOP nodes, C&E records, and a cross-study readiness matrix; retain missing SIS metadata in a controlled register.\n", - "* [Integrated SIL, ESD, HIPPS, and depressurization safety study](process/integrated_sil_esd_hipps_depressurization_study.ipynb): connect LOPA and native SIL verification to 2oo3 HIPPS voting, ESD timing, real-gas pressure rise, cold and fire blowdown, simultaneous flare load, and header-Mach sensitivity.\n", - "* [ESD, PSV, and controlled gas depressurization with NeqSim](process/ESD_PSV_Process_Safety_Demo.ipynb): calculate SRK gas properties, native PSV hysteresis and API 520 screening, blocked-outlet protection, manual and PSHH-triggered ESD blowdown, ISO 5167 orifice flow, balances, and blowdown-orifice trade-offs.\n", - "* [ESD system, alarm and flare](process/ESD_Fire_Alarm_System_Tutorial.ipynb)\n", - "* [Alarm handling in NeqSim](process/Alarm_Handling_NeqSim.ipynb)\n", - "* [Use of process simulators and NeqSim for process safety design](process/neqsim_process_safety_design.ipynb)\n", - "* [High Integrity Pressure Protection System (HIPPS) and process simulation](process/HIPPS_Safety_Simulations.ipynb)\n", - "* [Bow-tie, LOPA, and safety-instrumented risk analysis with NeqSim](process/bowtie_lopa_sif_risk_analysis.ipynb): connect an SRK inlet-separation process to native bow-tie generation, SIF PFD/SIL verification, LOPA, proof-test sensitivity, conservation checks, and a combined risk-and-capacity screen.\n", - "\n", - "## Power production\n", - "* [Electrical engineering fundamentals for oil and gas operations](power/electrical_engineering_fundamentals_oil_gas.ipynb): connect NeqSim compressor and water-injection pump duties to three-phase power, motors and VFDs, load lists, transformers, cables, fault current, starting, protection, harmonics, reliability, and operating constraints.\n", - "* [NCS wind, battery, and gas-turbine electrification with NeqSim](power/ncs_wind_battery_gas_electrification.ipynb): build NeqSim from a pinned repository commit, derive an offshore process load, dispatch Hywind-scale synthetic wind and balancing gas, evaluate native battery storage, and screen direct CO₂, curtailment, ramps, reserve, and field-maturity sensitivities.\n", - "* [Process-coupled offshore electrification with NeqSim](power/process_coupled_offshore_electrification_study.ipynb): extend a real separation and recompression process with 70-to-160 bara export compression, derive flow-dependent electrical demand from solved equipment duties, and compare gas turbines, shore power, wind, battery storage, and hybrid operation.\n", - "* [Natural-gas combustion, burner devices, and NeqSim integration with Cantera](reactions/natural_gas_combustion_with_cantera.ipynb): compare fuel and oxidizer blends; boiler, furnace, low-NOx, gas-turbine, duct-burner, and flare cases; validate composition and mass closure; model staged combustion; and run a Cantera custom unit inside a NeqSim process.\n", - "* [Gas-fired power plants with NeqSim](power/Gas_fired_power_plants.ipynb): fuel quality, stoichiometric combustion, Brayton-cycle states, heat recovery, balances, direct CO2 intensity, and operating sensitivities.\n", - "* [Natural-gas combined-cycle power plant with NeqSim](power/combined_cycle_power_plant.ipynb): Brayton gas turbine, single-pressure HRSG, CPA water/steam Rankine cycle, balances, quality checks, and operating sensitivities.\n", - "\n", - "## CO2 removal and handling\n", - "* [Thermodynamic and physical properties of CO2 — model selection, phase envelope, depressurization, and export conditioning](thermodynamics/ThermodynamicandphysicalpropertiesofCO2.ipynb)\n", - "* [EOS-CG and GERG-2008 for CO2](thermodynamics/EOSCG_vs_GERG2008.ipynb)\n", - "* [CO₂-rich phase envelopes, solids, hydrates, and conditioning](thermodynamics/phaseenvelopesofCO2richmixtures.ipynb): compare pure CO₂, CO₂–CH₄, and CO₂–CH₄–H₂S phase behavior; screen solid and hydrate boundaries; map properties; and connect the results to compression, cooling, throttling, separation, balances, and operating sensitivity.\n", - "* [Thermodynamics of CO2 rich gases and water](thermodynamics/CO2richandwater.ipynb)\n", - "* [CO₂ solubility, speciation, and MDEA gas treating](thermodynamics/CO2alkonolamines.ipynb): compare Electrolyte-CPA and Electrolyte-ScRK equilibrium, inspect aqueous speciation, screen loading, temperature, model, and solvent-rate sensitivities, and run a balanced stream–mixer–separator contact process.\n", - "* CO2 removal from natural gas\n", - "* CO2 removal from gas fired power plants\n", - "* [CO₂ compression, intercooling, and dense-phase pumping with NeqSim](process/CO2_compression.ipynb): build a three-stage PR-EOS compression train with intercooling, dense-phase pumping, injection conditioning, phase-envelope diagnostics, stage-count and intercooler sensitivities, mass and energy closure, and operating-limit checks.\n", - "* [CO₂ dehydration for pipeline transport with NeqSim](process/CO2_Dehydration_Pipeline.ipynb): map saturated water content, impurity phase behaviour, transport density, TEG absorption, circulation and purity sensitivities, hydraulic screening, and mass balances.\n", - "* CO2 depressurization\n", - "* [CO2 injection pipelines](fluidflow/CO2pipeline.ipynb)\n", - "* [CO2 chain from compression to pipeline](process/co2_chain_from_compression_to_pipeline.ipynb): build and validate staged compression, intercooling, dense-phase diagnostics, and an 80 km NeqSim pipeline design study.\n", - "* [CO₂ trace-acid formation, partitioning, and compression conditioning with NeqSim](thermodynamics/CO2reactions.ipynb): preserve the original acid, sulfur, and pH lessons; apply an oxygen-limited element-balanced formation screen; refresh Peng–Robinson and Electrolyte-CPA results; and run a two-stage compression, intercooling, and separation sensitivity workflow.\n", - "* [CO2 and reactions in a recompressor train](thermodynamics/Co2_recompression.ipynb)\n", - "\n", - "## Gas and oil transport\n", - "* [Design of a gas pipeline](fluidflow/gaspipeline.ipynb)\n", - "* [Design of a multi-phase pipeline](fluidflow/twophasepipeline.ipynb)\n", - "* [Two-fluid transient, slug-flow, and S-riser modelling](fluidflow/two_fluid_transient_slug_flow.ipynb): compare `TwoFluidPipe` with Beggs–Brill, map flow regimes, simulate pressure and liquid-inventory transients, and study an oil-and-gas wellstream in an S-riser.\n", - "* [Flexible multiphase flowlines and risers](fluidflow/flixiblepipes.ipynb): use current `PipeBeggsAndBrills` profiles, thermal modes, connected lazy-wave-riser legs, API RP 14E velocity screening, and diameter/rate design scenarios.\n", - "* [Transient multiphase flow with NeqSim's drift-flux model](process/transient_multiphase_flow_tutorial.ipynb): build an SRK gas-condensate fluid, simulate horizontal and terrain flow, inspect drift-flux closure and accumulation, exercise controlled slug diagnostics, validate a vertical-riser initial state, test mesh sensitivity, and connect a flow-step case to a receiving separator.\n", - "* [Water-hammer simulation with NeqSim](process/water_hammer_simulation_tutorial.ipynb): use the released `WaterHammerPipe` MOC solver for valve closure, pressure histories and envelopes, closure-time sensitivity, cavitation screening, elevation, material effects, and ESD design.\n", - "\n", - "## Flow assurance\n", - "* [Thermodynamics of natural gas hydrates — CPA equilibrium, MEG and salt inhibition, and arrival-process screening](thermodynamics/thermodynamics_of_natural_gas_hydrates.ipynb)\n", - "* [Wax appearance, model tuning, and flowline operability with NeqSim](thermodynamics/thermodynamicsOfWax.ipynb): characterize a reservoir fluid, calculate and tune wax precipitation, map WAT and wax fraction, and screen cooling and insulation scenarios along a segmented flowline.\n", - "* [Mineral-scale thermodynamics and flowline control with NeqSim](thermodynamics/ThermodynamicsOfMineralScale.ipynb): calculate Electrolyte-CPA aqueous speciation and saturation indices, screen water chemistry and incompatible-brine mixing, profile flowline risk, assess inhibitor dose, and couple multiphase hydraulics to deposition.\n", - "* [PVT property tables for multiphase flow simulators](thermodynamics/PVTtableGeneration.ipynb): generate and audit OLGA-style tables for wet gas, characterized condensate, and an Eclipse-described well fluid; inspect phase/property grids, qualify interpolation resolution, and retain engineering checks.\n", - "* [Top-of-line condensation and MEG carryover with NeqSim](process/topoflinecondensation.ipynb): use CPA fugacity equilibrium to saturate gas with MEG-water liquid, quantify cold-wall condensate, close flash balances, screen a cooling flowline profile, and compare inhibitor strengths.\n", - "* [MEG injection and evaporation of water](thermodynamics/MEG_injection_and_evaporation.ipynb): model SRK-CPA water/MEG equilibrium, hydrate inhibition, evaporation, and a validated saturation–injection–separation workflow.\n", - "* [Asphaltenes in oil and gas production](thermodynamics/Asphaltene_Modeling_Tutorial.ipynb)\n", - "\n", - "## Process control\n", - "* [Dynamic process control with NeqSim](process/process_control_with_neqsim_v2.ipynb): build native separator pressure and level loops, connect two-stage separation and flash-gas recompression, implement cascade and ratio control, compare PI tuning, and filter transmitter noise.\n", - "* [Reinforcement Learning integration](process/reinforcement_learning_integration.ipynb)\n", - "* [Data-driven separator pressure control with NeqSim](process/data_driven_control_strategies.ipynb): build an SRK wellstream, equilibrium separator, gas valve, and pressure transmitter; derive real-gas inventory and valve-capacity tables; apply deterministic Kalman estimation and constrained MPC; and validate disturbance rejection, control bounds, and mass closure.\n", - "* [Model predictive control](process/model_predictive_controller_examples.ipynb)\n", - "* [DEXPI standard](process/dexpi_demo.ipynb)\n", - "\n", - "# Process automation and logics imlementation\n", - "* [Process logic and safeguarding with NeqSim](process/NeqSim_Process_Logic_Demo.ipynb): connect an SRK-CPA wellstream and heated three-phase separator to pressure detectors, 2oo3 HIPPS voting, sequenced ESD actions, fire-and-gas detection, response scenarios, and engineering checks.\n", - "* [Process alarms, interlocks, and sequential logic around a NeqSim model](process/ProcessLogicIntegrated.ipynb): build a current NeqSim valve-and-separator flowsheet, implement priority, deadband, latching, delayed blowdown, reset permissives, and validate a real-gas vessel balance.\n", - "\n", - "## Oil and Gas metering and analysis\n", - "* [Multi phase measurments](process/MultiphaseflowMeasurement.ipynb)\n", - "* [Allocation of production](process/allocationoilandgas2.ipynb)\n", - "\n", - "## Gas and Oil specifications\n", - "* [Calorific value of natural gas (ISO6976)](gasquality/CalorificValueNaturalGas.ipynb)\n", - "* [Natural-gas blending and quality optimization](gasquality/gas_blending_quality_optimization.ipynb)\n", - "* [Oil quality specifications: assay characterization, API gravity, viscosity, vapor pressure, and stabilization](gasquality/oilqualityspecifications.ipynb): executable SRK workflow with ASTM D6377 screening, six technical figures, and a connected valve-conditioner-separator application.\n", - "* [Hydrocarbon dew point](gasquality/hydrocarbon_dew_point_of_natural_gas.ipynb)\n", - "* [Oil vapour pressure: TVP, VPCR4, and RVP](process/TVP_RVP_Study.ipynb): calculate method-specific vapour pressure, composition and temperature sensitivity, and stabilization-pressure trade-offs.\n", - "\n", - "## Process simulation using NeqSim\n", - "* [Simulation of oil stabilization](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/oilstabilizationprocess.ipynb)\n", - "* [Produced-water treatment with NeqSim](process/producedwatertreatment.ipynb): preserve the two legacy CPA separation studies and hydrocyclone video; quantify flare gas, dissolved hydrocarbons, and BTEX over pressure and temperature; use the released Electrolyte-CPA water builder and native hydrocyclone sizing, PDR, droplet-distribution, capacity, reject-flow, and oil-in-water screening APIs; and close the connected process mass balance.\n", - "* [Simulation of TEG dehydration process](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/TEGprocessHX.ipynb)\n", - "* [Simulation of MEG hydrate inhibition and regeneration](process/MEGwaterprocess.ipynb): CPA hydrate envelopes, cold liquid dropout, and rich-MEG regeneration balances.\n", - "* [Exergy analysis of oil and gas processes](process/exergyanalysisofoilprocess.ipynb): preserve the legacy offshore process example while building a self-contained three-stage stabilization and export train, closing mass, component, and energy balances, auditing entropy and exergy, ranking irreversibility, and screening separator-pressure trade-offs.\n", - "* [Process equipment weight screening with NeqSim](process/weightofoilprocess.ipynb): rebuild the original offshore HP/MP/LP stabilization process with current APIs, close gas/oil/water balances, calculate native equipment and discipline weights, inspect separator dimensions, and rerun capacity sensitivities.\n", - "* [Optimization of a field producing from both gas and oil reservoir](reservoir/optimizationofoilandgasproduction.ipynb)\n", - "* [Process flow diagrams driven by NeqSim results](process/process_flow_diagram.ipynb): characterize a rich wellstream, connect separation, gas compression, and liquid throttling, verify balances, render a calculated pyflowsheet SVG with embedded tables, and screen export pressure.\n", - "* [Integration of third party tools into neqsim process simulations](process/neqsimreaktoro.ipynb)\n", - "* [Adding a new unit operation using python](process/newunitoperation.ipynb)\n", - "* [Adding a ML based unit operation](process/heat_exchanger_ml_external_unit.ipynb)\n", - "\n", - "\n", - "##Integrated modelling reservoir, well, transport and process\n", - "* [Reservoir-to-market system curves with a capacity-designed topside](reservoir/reservoir_well_topside_market_system_curves.ipynb): connect one fluid from reservoir inflow through well and flowline lift, HP/MP/LP separation, flash-gas recompression, two-stage export compression, separator and scrubber sizing, complete compressor maps, reservoir-pressure nodal matrices, debottlenecking, depletion, and capacity-bounded ECLIPSE VFPPROD export.\\n* [Integrating reservoir and process simulations](reservoir/reservoirandprocess.ipynb)\n", - "* [Ensemble-based integrated reservoir and process modelling](reservoir/ensamble_based_modelling.ipynb): update a reproducible reservoir ensemble with production history, transfer posterior rates through an explicit ERT-compatible contract, run a NeqSim choke, cooler, three-phase separator, and compressor, and quantify bottleneck probability and constrained production.\n", - "\n", - "## NeqSim PVT and Process Simulation API\n", - "* [How to create an API using NeqSim and Python](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/python)\n", - "* [How to create an API using NeqSim and Java](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/java)\n", - "* [NeqSim PVT API: executable property workflows](API/NeqSimPVTAPI.ipynb): build SRK, GERG-2008, and CPA fluids; run unit-aware TP flashes; characterize heavy fractions; retrieve named phase properties; compare gas models; sweep phase behavior; and validate closure and property trends.\n", - "* [NeqSim Process API: validated engineering calculation services](API/NeqSimProcessAPI.ipynb): build unit-aware request contracts for SRK-CPA TEG dehydration and a staged offshore process, return finite JSON-compatible results, reject invalid inputs, and screen independent operating scenarios.\n", - "\n", - "# Online process simulation\n", - "* [Online oil-stabilization simulation with NeqSim](process/onlineprocesssimulation.ipynb): build a PR well fluid, solve three-stage stabilization, recompress flash gas, update live inputs, run historian scenarios, and validate mass closure, product quality, power, and recycle convergence.\n", - "* [Implementing effective online process simulations](process/online_process_simulation_demo1.ipynb): build a four-stage stabilization, gas recompression, dew-point control, recycle, oil export, and produced-water process; compare cold, warm, and single-step online strategies over 30 points; inspect structured reports; and validate conservation, runtime, and provisional-result quality.\n", - "\n", - "## Process plant operation\n", - "* [Weather-aware gas processing and power generation with NeqSim](process/weatherandprocess.ipynb): preserve daily, hourly, station, and forecast workflows; calculate SRK air properties, export-compressor demand, turbine capacity, fuel, and direct emissions with deterministic weather fallbacks.\n", - "* Condition based monitoring\n", - "* [Condition-based monitoring of heat exchangers with NeqSim](process/heat_exchanger_condition_monitoring.ipynb): normalize historian data with native SRK and SRK-CPA streams, invert the two-stream HeatExchanger for effective UA, audit hot/cold duty consistency, detect fouling and sensor bias, verify cleaning recovery, and return reusable equipment-health snapshots.\n", - "\n", - "## Operations and asset management\n", - "\n", - "* [Facility lifecycle from commissioning to decommissioning](operations/facility_lifecycle_commissioning_to_decommissioning.ipynb): connect nitrogen properties, drying and inerting, hydrocarbon introduction, process ramp-up, shutdown inventory and low-temperature screening, late-life economic limits, cessation, P&A, removal schedules, waste and recycling, cost uncertainty, and emissions using public-PyPI NeqSim with Python.\n", - "* [Operations and asset management with NeqSim and Python](operations/operations_asset_management_with_neqsim.ipynb): connect source-built NeqSim process and failure consequences to censored reliability fitting, condition trends, maintenance-policy and shared-crew RAM simulation, availability, and uncertainty-weighted production accounting.\n", - "\n", - "## Materials in gas processing\n", - "* Material selection in gas processing\n", - "* [CO₂ corrosion, inhibition, and pipeline integrity](material/corrosionandthermo.ipynb): Electrolyte-CPA speciation, NORSOK M-506 screening, mechanistic inhibition, material selection, and coupled flowline integrity.\n", - "* [Elemental sulfur (S8) in natural gas processing](material/elemental_sulfur.ipynb)\n", - "* [Acid partitioning between gas, oil, and water](thermodynamics/formicacid_calculation.ipynb)\n", - "\n", - "## Emissions\n", - "* [CO2 emissions (scope 1, 2 and 3)](https://colab.research.google.com/drive/1Iwja5bKiP-fC2tS9O97-06j5XhqE0lJK#scrollTo=TgILfzXSzDAP)\n", - "* [Hydrocarbon emissions from processing and transport](emissions/Hydrocarbon_emissions.ipynb): use CPA phase equilibrium, high-pressure separation, isenthalpic letdown, low-pressure flash-gas accounting, methane/VOC breakdown, operating maps, and mitigation scenarios.\n", - "* [Chemicals consumption (glycols/etc)](process/cemicalsconsumption.ipynb): use CPA phase equilibrium and a connected SimpleTEGAbsorber to quantify equilibrium vapor loss, water removal, packing capacity, circulation sensitivity, and annual TEG make-up.\n", - "* [CO₂ emissions across the natural-gas value chain with NeqSim](emissions/CO2emsissionsinthevaluechain.ipynb): connect SRK separation, offshore and onshore compression, cooling, ISO 6976 gas quality, composition-derived carbon factors, Scope 1/2/3 boundaries, electrification scenarios, avoided-cost screening, and pressure–power sensitivity.\n", - "\n", - "##Pipeline network optimization\n", - "* [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; use the same speed, power, map, and custom capacity constraints in typed operating-point results, bottleneck analysis, and maximum-throughput pressure-boundary optimization; quantify driver-upgrade value and production gains from declining inlet pressure.\n", - "* [Dry gas parallel-pipeline network optimization](process/PipeNetworkOptim.ipynb): balance unequal branches, screen capacity and diameters, map throughput, and connect compression, cooling, splitting, pipe hydraulics, and delivery-node mixing.\n", - "* [Multiphase network modeling and optimization](process/MultiphaseOptim.ipynb): balance two three-phase well branches at a shared manifold, screen export bore and backpressure, model thermal export hydraulics, and close a receiving-separator mass balance.\n", - "\n", - "\n", - "## Machine learning techniques and artificial intelligence (AI) in gas processing simulation\n", - "* Use of Languge Models and NeqSim\n", - "* Machine learning and PVT\n", - "* [Machine learning and process simulation](process/Machine_learning_and_process_simulation.ipynb)\n", - "* [AI-assisted gas-processing simulation and design with NeqSim](process/AIgasprocessing.ipynb): generate audited choke–cooler–three-phase-separator–compressor cases, fit and validate a transparent quadratic surrogate, screen constrained operation, verify AI recommendations in NeqSim, and demonstrate synthetic residual monitoring.\n", - "* [Machine-learning-driven heat exchanger unit operations](process/heat_exchanger_ml_external_unit.ipynb)\n", - "\n", - "## Innovative technologies combining AI technologies and NeqSim\n", - "* [Real-Time process monitoring for operational safety and compliance](AI/Real_Time_process_monitoring_for_operational_safety_and_compliance.ipynb)\n", - "* [NeqSim and Data Analytics using Seeq](AI/NeqSim_and_Seeq.ipynb)\n", - "* Optimization of process parameters to maximize efficiency and production\n", - "* Anomaly detection to detect unusual patterns or deviations in process data\n", - "* Integration of different data sources to analyze data from different sources (sensor data, simulation data, weather data and external environmental data) to provide a more holistic understanding of processes\n", - "* Digital twins used to simulate and optimize processes in a virtual setting before implementation in the real world\n", - "* Self-adjusting control systems that adjust themselves based on continuous feedback and changes in process data\n", - "* Predicting oil spills or gas leaks for environmental monitoring\n", - "* Virtual measurements in oprocess plants using neqsim and AI technologies\n", - "* [IoT and Industry 4.0](AI/IoT_and_Industry4.0_with_NeqSim.ipynb)\n", - "\n", - "\n", - "## Statistics\n", - "* [Statistical Sites on the World Wide Web](statistics/WorldStats.ipynb)\n", - "* [World oil and natural-gas production statistics connected to NeqSim](statistics/worldOilandGasProduction.ipynb): audit an embedded 2024 Energy Institute/Our World in Data snapshot, close world totals, calculate producer shares and grouped concentration, apply SRK and ISO 6976 gas quality, bridge energy to standard volume, and screen export compression and direct-combustion scenarios.\n", - "* [Oil and Gas Price Statistics and Analysis](statistics/OilandGasPriceStatistics.ipynb)\n", - "* [Norwegian Continental Shelf production statistics and NeqSim export-process capacity screening](statistics/ProductionfromNorwegianContinentalShelf.ipynb)\n", - "* [CO2 emissions from oil and gas production](statistics/CO2emissionsNorwegianContinentalShelf.ipynb)\n", - "* [Statistics of CO2 in atmosphere](statistics/CO2inatm.ipynb)\n", - "\n", - "## Energy System Modelling\n", - "* [Energy system modelling](https://oemof.org/)\n", - "* [Thermal Engineering Systems](https://tespy.readthedocs.io/en/main/index.html)\n", - "* [A Sector-Coupled Open Optimisation Model of the European Energy System](https://pypsa-eur.readthedocs.io/en/latest/)\n", - "* [Examples of NeqSim in Energy System Modelling](energyopt/ThermalEnergyAndNeqSim.ipynb#scrollTo=C7-qUy51VbFs)\n", - "* [PyPSA-Earth example for Norway](energyopt/PyPSA-Earth_Norway.ipynb)\n", - "\n", - "## Economic analysis\n", - "* [NeqSim-connected NCS gas-field economy analysis](fielddevelopment/economy.ipynb): connect SRK separation and export compression, native mechanical-design cost estimation, declining field pressure and production, corrected tax-rate algebra, cash flow, NPV, IRR, payback, break-even, and commercial sensitivities.\n", - "* [Integrated process cost estimation and economic screening](fielddevelopment/process_cost_estimation_and_economics.ipynb): connect a gas-process simulation to mechanical design, CAPEX, OPEX, location/material/CEPCI sensitivities, and financial metrics.\n", - "\n", - "## Earth Tools and Metocean Data\n", - "* [Metocean and field development with NeqSim](fielddevelopment/metocean_and_field_development.ipynb): use a checksum-controlled public NORA10 teaching series with metocean statistics, weather windows, extremes, joint contours, spectra, tides, and NeqSim seasonal tie-back cases.\n", - "* [Earth tools and field development with NeqSim](fielddevelopment/earth_tools_and_field_development.ipynb): combine CRS-safe GeoPandas vectors, raster bathymetry, exclusion-aware least-cost routes, network analysis, interactive mapping, NeqSim tie-back hydraulics, and host economics.\n", - "\n", - "## Integrated energy and emission calculations\n", - "* [Integrated NeqSim + eCalc field-life, compressor-map, and offshore-energy study](power/neqsim_ecalc_integrated_energy_emissions.ipynb): combine three-pressure separation and recompression, complete NeqSim-generated compressor maps with surge and stonewall boundaries, declining inlet-pressure field life, native wind and battery dispatch, and eCalc fuel, CO₂, capacity, recirculation, and intensity accounting.\n", - "\n", - "## Northern Lights CCS open-data calculations\n", - "The numbered series uses the Equinor Northern Lights Databricks Marketplace listing as the authoritative full-data boundary and small hash-checked Eos snapshots for credential-free execution. NeqSim owns thermodynamics, wells, pipelines, facilities, and operations; OPM Flow owns dynamic storage-reservoir simulation.\n", - "* [01 - Northern Lights open-data foundation and NeqSim model basis](fielddevelopment/northern_lights/01_northern_lights_open_data_foundation.ipynb): audit licensed Eos trajectory, interpreted formation, UCS, and public well-test evidence; build an exact source-master NeqSim runtime; calculate EOS-CG CO₂ and CPA water anchors, a frictionless hydrostatic injection screen, and Phase 1/2 capacity translations; then issue explicit NeqSim and OPM Flow handoffs.\n", - "\n", - "## Volve field calculations\n", - "This principal eight-part field-lifecycle collection uses the Equinor Volve Data Village listing as its authoritative measured-data boundary and carries versioned handoff contracts from one discipline to the next. It follows the [NeqSim Field Development and Operations book](https://equinor.github.io/neqsimhome/doc/field_development_and_operations/book_standalone.html) from SEG-Y and development framing through OPM Flow, production technology, facilities, constrained operations, late life, shutdown, and decommissioning. A clean teaching fallback is stored for reproducibility, but it is explicitly labelled and is not represented as measured Volve data. Run the numbered notebooks in order.\n", - "* [01 - Volve seismic, wells, and static model](fielddevelopment/volve/01_volve_seismic_wells_static_model.ipynb): inventory and select Marketplace SEG-Y and well assets; perform geometry, amplitude, horizon, depth-conversion, log, petrophysical, fluid-density, volumetric, and uncertainty screens; then write the geoscience-to-reservoir handoff.\n", - "* [02 - Volve PVT, black oil, and reservoir](fielddevelopment/volve/02_volve_pvt_blackoil_reservoir.ipynb): select PVT, Eclipse/OPM, and production assets; characterize the fluid with NeqSim SRK; generate black-oil/DLE properties; screen history, material balance, depletion, and OPM Flow acceptance; then write the reservoir forecast handoff.\n", - "* [03 - Volve wells, SURF, and flow assurance](fielddevelopment/volve/03_volve_wells_surf_flow_assurance.ipynb): connect trajectories and completions to productivity, injectivity, IPR/VLP, NeqSim wellstreams, pipeline hydraulics, thermal response, hydrate margins, cooldown, and the facility-inlet envelope.\n", - "* [04 - Volve facilities and processing](fielddevelopment/volve/04_volve_facilities_processing.ipynb): model inlet conditioning, three-phase separation, oil stabilization, gas compression and cooling with NeqSim; locate bottlenecks; and screen produced water, chemicals, utilities, emissions, availability, and safeguarding.\n", - "* [05 - Volve integrated field twin and decisions](fielddevelopment/volve/05_volve_integrated_field_twin.ipynb): assemble all discipline contracts; reconcile history and capacity ownership; test interventions, uncertainty, economics, emissions, and decision gates; and issue a traceable integrated field-evaluation handoff.\n", - "* [06 - Volve reservoir simulation and production history matching](fielddevelopment/volve/06_volve_reservoir_history_matching.ipynb): obtain and audit the Marketplace production workbook; calculate NeqSim PVT anchors; run and fit a communicating two-tank reservoir simulator to pressure, oil, gas, and water; test a holdout period; diagnose identifiability; propagate uncertainty; and expose an optional controlled OPM Flow acceptance path.\n", - "* [07 - Volve all wells, nodal analysis, gathering, and full SURF](fielddevelopment/volve/07_volve_all_wells_gathering_surf.ipynb): simulate every active producer with IPR, NeqSim-calibrated tubing VLP, choke and branch loss; solve two manifolds, trunks, a common riser, and host backpressure; verify the network in a composable NeqSim ProcessSystem; and evaluate integrity, late-life, outage, shutdown, cooldown, hydrate, and restart cases.\n", - "* [08 - Volve closed-loop full-field development and operations](fielddevelopment/volve/08_volve_closed_loop_field_development_operations.ipynb): convert the static handoff into development scenarios and producer/injector placement; generate OPM well schedules and an OPM-primary history-match ensemble; feed NeqSim well, SURF, separation, compression, water, export, and injection limits back into monthly reservoir controls; and calculate the economic limit, shutdown handoff, plugging inventory, removal sequence, and decommissioning uncertainty.\n", - "* [Compact Volve full-field precursor with OPM Flow and NeqSim](fielddevelopment/volve_full_field_development_opm_neqsim.ipynb): compare producer/injector layouts in a reduced teaching sector and connect reservoir states to PVT, wells, SURF, topsides, schedule, economics, and uncertainty.\n", - "\n", - "## Field development\n", - "* **Advanced** - [NCS resource classification and project maturation with NeqSim](fielddevelopment/ncs_resource_classification_with_neqsim.ipynb): explain RC0-RC9, F/A project categories, BOI/BOK/BOV/BOG/PDO transitions, low/base/high uncertainty and discovery chance; build a governed project register with pandas and NetworkX; derive marketable gas and condensate yields with a source-traceable NeqSim process; connect volumes to capacity-limited profiles, synthetic economics, public SODIR data contracts, and reusable CSV/JSON handoffs; and link the exploration-to-market notebook chain.\n", - "* [SEG-Y with segyio to field-development decisions with NeqSim](fielddevelopment/segyio_to_field_development_workflow.ipynb): create and round-trip a synthetic 3-D SEG-Y cube, derive similarity and fault masks, interpret top/base/thickness, propagate low/base/high STOIIP, screen wells and field-life capacity, run NeqSim three-phase separation and export compression, and map the evidence to DG0-DG4 handoffs and decisions.\n", - "* [Offshore facility concept selection with NeqSim](fielddevelopment/offshore_facility_concept_selection_with_neqsim.ipynb): compare nine facility concepts using infrastructure distance, water depth, metocean extremes, environmental loads, weather operability, NeqSim tie-back hydraulics and process simulation, hard technical gates, lifecycle economics, weighted MCDA, TOPSIS, sensitivity analysis, and Monte Carlo robustness.\n", - "* [Norwegian field and area development lifecycle screening with NeqSim](fielddevelopment/norwegian_field_and_area_development_with_neqsim.ipynb): connect synthetic depletion and well deliverability to SRK phase behavior, choke/cooler/separator/compressor process models, SURF hydraulics, capacity constraints, export-pressure concepts, brownfield tie-in holdback, direct compressor emissions, product screens, and discounted cash flow.\n", - "* **Advanced** - [NCS tie-in from reservoir to market: host holdback, quality, tariffs, and tax](fielddevelopment/ncs_tie_in_quality_tariff_tax.ipynb): integrate a source-traceable NeqSim gas-condensate process, ASTM D6377 liquid vapour pressure, ISO 6976 gas blending, boundary-specific public quality examples, dated August 2026 Gassco K/I/O tariff and booking-vintage screens, host-priority/firm-tie/pro-rata holdback allocation, accepted-production economics, simplified petroleum tax, uncertainty, and multidisciplinary decision gates.\n", - "* [Producer and injector well-count and placement workflow](fielddevelopment/well_count_and_placement_workflow.ipynb): screen counts from deliverability, injectivity, and voidage replacement; optimize robust discrete layouts with pymoo NSGA-II; verify four concepts in OPM Flow; and connect the selected concept to NeqSim PVT, wells, SURF, facilities, capacity, economics, and uncertainty.\n", - "* [Discovery 1 DG1 SURF and host-facility design with NeqSim](fielddevelopment/discovery_1_dg1_surf_facilities_design.ipynb): mature the neutral-labelled Discovery 1 reservoir-to-host handoff into a 50-deliverable educational DG1 learning package covering flow assurance, subsea architecture, pipeline sizing, host processing, utilities, safety, cost, schedule, risk, and decision gates.\n", - "* Process design basis\n", - "* Proces design in a field development scenario\n", - "* [NPV of a gas-field tie-back with NeqSim](fielddevelopment/npv.ipynb): connect SRK fluid properties, tank depletion, well and tie-back hydraulics, host-arrival control, export compression, Norwegian petroleum-tax screening, break-even, coupled decision sensitivities, and driver-emissions scenarios.\n", - "\n", - "## Field developement case studies\n", - "* [Field development case 1 (combined oil and gas field)](reservoir/fieldDevelopment1.ipynb)\n", - "* Field development case 2 (rich gas and power from shore case)\n", - "* Field development case 3 (sales gas and power from shore)\n", - "* Field development case 4 (hydrogen production with power from shore/wind)\n", - "* Offshore field and onshore NGL process\n", - "\n", - "## Bio Engineering\n", - "* [Food-waste anaerobic digestion and biomethane-to-grid with NeqSim](bioprocesses/bioandneqsim.ipynb): connect empirical digestion, thermodynamic streams, gas upgrading, compression, cooling, mass balances, design sensitivities, and sustainability metrics.\n", - "* [Biomass to sustainable aviation fuel with NeqSim](bioprocesses/biomass_to_sustainable_aviation_fuel_with_neqsim.ipynb): compare HEFA, gasification–FT, ATJ, fast pyrolysis, HTL, and a biogenic-CO₂ hybrid with sourced yields, oil quality, NeqSim ASTM oil-quality calculations, lifecycle data, logistics, economics, uncertainty, and validation.\n", - "\n", - "## Standards\n", - "* Energy Institute - Guideline for Flow Induced Vibrations (FIV) control in Production\n", - "* [NORSOK S-001 technical-safety screening with NeqSim](standards/technicalsafetyP0001.ipynb): flash a synthetic high-pressure gas, screen secondary pressure protection, run native transient-wall vessel blowdown and API pool-fire scenarios, compare BDV bore and flare-load trade-offs, and integrate 20 engineering checks.\n", - "* [NORSOK P-002 process-system design screening with NeqSim](standards/norsokP0002.ipynb)\n", - "* NORSOK I-106, Fiscal metering systems for hydrocarbon liquid and gas,\n", - "* (ISO 13703 - Design and installation of piping systems on offshore production platforms\n", - "* [API 521 blocked-liquid thermal-expansion and relief screening with NeqSim](standards/API521_thermal_expansion.ipynb): reproduce the original PR fluid and linear pressure calculation, correct derivative units and sign, solve a nonlinear density closure, estimate heat-duty relief load, exercise native Stream and SafetyValve blowdown, and verify 16 engineering checks.\n", - "\n", - "## Development of Process Digital Twins\n", - "* [Oil Process](process/oilgasprocess1.ipynb)\n", - "* Monitoring of Cricondenbar of Natural Gas\n", - "* Monitoring of TEG dehdydration processes\n", - "\n", - "## Excercise\n", - "* Excercise 1 - [Phase behaviour of reservoir fluids](excercise/Excercise_Phase_Behaviour_of_Reservoir_Fluids%20(1).ipynb)\n", - "* Excercise 2 - [Design of a gas-oil separation process](excercise/Design_of_a_separation_process_in_HYSYS.ipynb)\n", - "* Excercise 3 - [Design of a TEG dehydration process](excercise/Design_of_a_TEG_dehydration_process.ipynb): preserve and repair the three original CPA and Campbell examples; quantify wet-gas water, dew point, hydrate temperature, lean-TEG equilibrium, circulation, stage sensitivity, and a native `SimpleTEGAbsorber` workflow with total and water balances.\n", - "* Excercise 4 - [Export-gas phase envelopes and hydrocarbon dew-point control](excercise/calculationofphaseenvelopes.ipynb): preserve three original SRK envelope cases, correct temperature units, compare pressure-specific dew points, validate a Stream dew specification, and run a connected cooler and scrubber with balances and operating sensitivities.\n", - "* Excercise 5 - [Gas processing and the gas value chain](gasvaluechain/GasProcessingChain.ipynb)\n", - "\n", - "## TEP4185 - Gas Process Technology\n", - "* [NeqSim Thermodynamics](https://colab.research.google.com/drive/1c6OY3O1xX8nmaU5JadF3CIjaj4CSYb1Q?usp=sharing)\n", - "* [Pandas + NeqSim — Data‑driven Thermodynamics](https://colab.research.google.com/drive/1ngKz_Ry8PkJwENdNXIkZpO8PmARiqyGr?usp=sharing)\n" - ] - } - ], - "metadata": { - "colab": { - "name": "examples of NeqSim in Colab.ipynb", - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] }, - "nbformat": 4, - "nbformat_minor": 0 + { + "cell_type": "markdown", + "metadata": { + "id": "_eRtkQnHpL70", + "language": "markdown" + }, + "source": [ + "# Oil and Gas Value Chain with NeqSim and Python" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kHt6u-utpvYf", + "language": "markdown" + }, + "source": [ + "[NeqSim (Non-Equilibrium Simulator)](https://equinor.github.io/neqsimhome/) is a Java\n", + "library for thermodynamic properties, PVT, flow, and process simulation. Google Colaboratory\n", + "(Colab) is a free Jupyter notebook environment that runs in a web browser. This collection uses\n", + "Python tools together with NeqSim Java to explain the complete oil and gas value chain: access and\n", + "exploration, fluid characterization, reservoirs, wells, subsea and SURF, facilities, products and\n", + "markets, operations, emissions, late life, and decommissioning.\n", + "\n", + "The notebooks serve both as teaching material and as transparent engineering-analysis examples.\n", + "Select **Runtime \u2192 Run all** in Colab to reproduce an executed notebook. Detailed examples identify\n", + "where NeqSim is the primary calculation engine, where it supplies thermodynamic or process\n", + "boundaries, and where specialist Python or external tools remain necessary.\n", + "\n", + "---\n", + "\n", + "***Learn to use NeqSim in Colab and contribute new material***\n", + "\n", + "Users are welcome to contribute NeqSim Colab pages. The\n", + "[validated contributor template](template.ipynb) demonstrates SRK fluid setup, ISO 6976 gas\n", + "quality, streams, mixing, compression, cooling, scenario analysis, assertions, and retained\n", + "figures. A practical introduction is available in [How to use NeqSim](howtouseneqsim.ipynb).\n", + "All notebooks are maintained in the open\n", + "[NeqSim-Colab GitHub repository](https://github.com/EvenSol/NeqSim-Colab).\n", + "\n", + "---\n", + "\n", + "**Comments and requests for new content**\n", + "\n", + "Use the [discussion forum](https://github.com/EvenSol/NeqSim-Colab/discussions) to discuss the\n", + "material. Request new content or suggest improvements by\n", + "[reporting an issue](https://github.com/EvenSol/NeqSim-Colab/issues).\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "language": "markdown", + "id": "TuNcSKktRaLf" + }, + "source": [ + "# Suggested Learning Path\n", + "\n", + "Use the catalog below as a reference library, or follow one of these short paths:\n", + "\n", + "* **Complete value chain:** begin with the orientation notebook at the start of the table of\n", + " contents, then follow its dependency-ordered path from PVT and reservoirs through retirement.\n", + "* **Field development:** exploration and PVT -> reservoir forecast -> wells and SURF -> facilities\n", + " and economics.\n", + "* **Asset lifecycle:** commissioning and start-up -> stable operation -> integrity and optimization\n", + " -> cessation and decommissioning.\n", + "\n", + "* **Beginner:** create a fluid -> read properties -> run a TP flash -> plot a phase envelope.\n", + "* **Process simulation:** separator -> compressor -> heat exchanger -> complete process model.\n", + "* **Engineering studies:** flow assurance -> process safety -> emissions -> standards checks.\n", + "* **Advanced workflows:** dynamic simulation -> digital twins -> process automation -> AI-assisted workflows.\n", + "\n", + "**Level guide:** Beginner notebooks introduce one concept at a time. Intermediate notebooks combine several NeqSim calculations. Advanced notebooks use automation, optimization, or workflow-style result objects.\n", + "\n", + "## New Capability Demonstration Notebooks\n", + "\n", + "* **Beginner** - [Modern natural-gas fluid properties with NeqSim](thermodynamics/modern_fluid_property_workflow.ipynb): create a gas fluid, run a TP flash, read properties, and generate a property table.\n", + "* **Intermediate** - [Process automation API for discoverable and safe NeqSim workflows](process/process_automation_api_demo.ipynb): build a gas conditioning process, discover unit-aware addresses, use safe and batch operations, evaluate setpoints, inspect utilization, and apply multi-area automation.\n", + "* **Advanced** - [Closed-loop process optimization](process/closed_loop_process_optimization.ipynb): sweep process setpoints and minimize compressor power.\n", + "* **Advanced** - [External nonlinear process optimization with SciPy and CasADi/IPOPT](process/external_nonlinear_process_optimization.ipynb): connect NeqSim ProcessSimulationEvaluator to scaled SLSQP and IPOPT workflows with native margins, infeasibility restoration, Pareto fronts, shadow-price checks, and discrete brownfield upgrade packages.\n", + "* **Advanced** - [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; share speed, power, map, and custom capacity constraints across typed operating-point results, bottleneck analysis, and pressure-boundary optimization; screen driver upgrades and declining inlet-pressure strategies.\n", + "* **Intermediate** - [Capacity and bottleneck analysis](process/capacity_and_bottleneck_analysis.ipynb): use utilization snapshots to identify process constraints.\n", + "* **Intermediate** - [Digital twin model vs measurement](process/digital_twin_model_vs_measurement.ipynb): build, calibrate, validate, and monitor a compressor digital twin with synthetic plant measurements.\n", + "* **Intermediate** - [Hydrate, wax, and water margin screening](flowassurance/hydrate_wax_and_water_margin_screening.ipynb): screen operating margins before detailed flow-assurance analysis.\n", + "* **Intermediate** - [Gas turbine emissions and process power](power/gas_turbine_emissions_and_process_power.ipynb): preserve the original compressor/emissions screen, then use current `GasTurbineCatalog`, `GasTurbineUnit`, ambient, degradation, and native compressor-power-consumer APIs.\n", + "* **Intermediate** - [Standards-based gas-line and relief screening with NeqSim](standards/standards_based_engineering_checks.ipynb): calculate fluid properties, line velocity, compressible pressure profiles, independent Darcy checks, preliminary choked-gas relief area, and a reusable screening workflow without claiming design-code compliance.\n", + "* **Advanced** - [Agentic process simulation with NeqSim](AI/agentic_neqsim_workflow_demo.ipynb): discover and safely address process variables, evaluate guarded operating cases, audit balances, map an operating envelope, and optimize a constrained gas-conditioning workflow with the native ProcessAutomation API." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9VqtmS_MpS6M", + "language": "markdown" + }, + "source": [ + "# Table of Contents\n", + "\n", + "## Complete oil and gas value chain\n", + "* [The complete oil and gas value chain: governed models, handoffs, and decisions](valuechain/complete_oil_and_gas_value_chain_with_neqsim.ipynb): connect eleven lifecycle stages through governed model identities and coherent realizations; keep laboratory data and specialist PVT software primary while public-PyPI NeqSim supports PVT and powers connected separation, compression, cooling, SURF, ISO 6976, emissions, deterministic and probabilistic economics, RAM, and explicit decision gates.\n", + "* **Advanced** - [Geo to market on the Norwegian Continental Shelf](valuechain/norne_geo_to_market_full_workflow.ipynb): carry pinned published Norne data through static-property QC and upscaling, a transparent reservoir forecast, a 60-realization FMU prior, four ES-MDA updates and an ERT contract; preserve realization identity through well and SURF hydraulics, hydrate, cooldown, wax, erosion and slug screens, a real NeqSim separation and export-compression process, ISO 6976 gas quality, facility feedback, emissions, market uncertainty, NPV and sensitivity analysis.\n", + "* **Advanced** - [Early-phase subsurface-to-flow-assurance data handoff](valuechain/early_phase_subsurface_to_flow_assurance_handoff.ipynb): normalize ECLIPSE/OPM summary data into a unit-explicit contract, preserve coherent realization identities, select representative uncertainty cases, run NeqSim well and TwoFluidPipe screens, and export traceable choke, MPFM and topside handoffs for automated discipline and reporting agents.\n", + "\n", + "## Fundamentals of NeqSim\n", + "* [Create and validate a NeqSim fluid from Eclipse PVT input](PVT/readEclipseFormat.ipynb): import a self-contained seven-lump SRK deck, audit properties and phase/component closure, test pressure and temperature sensitivities, verify XML round-trip identity, and connect the fluid to a high-pressure separator.\n", + "* [Thermodynamic and physical properties with NeqSim](thermodynamics/readproperties.ipynb): phase-aware equilibrium and transport properties, state sensitivities, validation, and a connected separation-and-compression application.\n", + "* [Traceable NeqSim parameter database and model audit](PVT/parameter_database.ipynb): inspect packaged component and binary-interaction data with read-only SQL, reconcile live SRK parameters, screen local interaction-parameter and pressure sensitivities, and connect the model to a compressor.\n", + "* [Controlled custom parameters and local model overlays](PVT/parameter_database2.ipynb): validated component and binary-interaction overrides without global database mutation.\n", + "* [Compare to experimental data and parameter fitting](https://github.com/equinor/neqsim-parameterfitting)\n", + "* [ThermoML model accuracy and parameter tuning with NeqSim](thermodynamics/thermoml_model_accuracy_and_parameter_tuning.ipynb): parse DOI-specific density and VLE records with ThermoMLPy, audit property provenance, test held-out model accuracy, and tune a P\u00e9neloux volume correction and PR binary interaction parameter.\n", + "\n", + "## Natural Gas Statistics\n", + "* [How natural gas is sold to Europe](gasvaluechain/european_gas_sales_market_foundation.ipynb): connect consumer procurement, shipper and TSO transport, producer netback, ISO 6976 energy settlement, NeqSim export compression, capacity dispatch, nominations, imbalance, hedging, and auditable cash closure.\n", + "* [Natural gas and the World Energy Outlook: evidence, scenarios, LNG supply, and NeqSim](gasvaluechain/energystatistics.ipynb): connect WEO 2025 and current IEA evidence to reproducible scenario stress tests, ISO 6976 gas quality, multistage compression, balances, sensitivities, and lifecycle boundaries.\n", + "* [Use of natural gas (history, present and future)](gasvaluechain/useOfNaturalGas.ipynb)\n", + "* [Natural gas in the energy transition: evidence, scenarios, and NeqSim (2025\u20132050)](gasvaluechain/EneregyTransition.ipynb): connect current IEA evidence to ISO 6976 fuel quality, a three-stage compression process, methane intensity, carbon capture, and lifecycle sensitivities.\n", + "\n", + "\n", + "## Thermodynamics\n", + "* [The laws of thermodynamics](thermodynamics/LawsOfThermodynamics.ipynb): verify equilibrium, state functions, energy balances, entropy generation, reversible compression, exergy, and limiting cases.\n", + "* [CO\u2082 low-temperature compression, relief, and depressurization](thermodynamics/CO2_low_temperature_compression_relief_and_depressurization.ipynb): compare EOS-CG, GERG-2008, PR, and SRK with Span\u2013Wagner; map impurity-sensitive phase behaviour; simulate staged compression; and screen isenthalpic relief, HEM critical flow, and transient blowdown temperatures.\n", + "* [Energy balance for closed and open systems](thermodynamics/EnergyBalance.ipynb)\n", + "* [Equations of State](thermodynamics/EquationsOfState.ipynb)\n", + "* [From fundamental interactions to SAFT-VR Mie parameters and NeqSim](thermodynamics/saft_vr_mie_from_fundamental_models.ipynb): execute PySCF dimer calculations, SciPy mapping, FEASST Mie Monte Carlo, SGTPy association and binary phase equilibrium, teqp validation, NeqSim parameter injection, uncertainty propagation, and methane compression.\n", + "* [From molecular hydrogen bonds to SAFT-VR Mie association and NeqSim](thermodynamics/saft_vr_mie_association_from_fundamental_models.ipynb): derive water 4C site topology, hydrogen-bond energy, and NeqSim kernel volume from counterpoise-corrected molecular calculations; benchmark Dufal association with teqp; verify NeqSim mass action and Helmholtz identities; and propagate the mapped parameters to water properties and heater duty.\n", + "* [Phase equilibrium](thermodynamics/PhaseEquilibrium.ipynb)\n", + "* [Flash Calculations and Rachford-Rice](thermodynamics/RachRice.ipynb)\n", + "* [Advanced TPflash algorithms](thermodynamics/c1_co2_h2s_flash_test.ipynb)\n", + "* [Thermodynamic property charts and connected process paths](thermodynamics/ThermoPropertyCharts.ipynb)\n", + "* [Physical porperty charts](thermodynamics/physiclaPropertyChart.ipynb)\n", + "* [Thermodynamc Cycles](thermodynamics/ThermodynamicCycles.ipynb)\n", + "* [Exergy analysis](thermodynamics/ExergyAnalysis.ipynb)\n", + "* [Chemical Equilibrium](thermodynamics/ChemicalEquilibrium.ipynb)\n", + "* [Water-ammonia thermodynamics and generator screening](thermodynamics/water_ammonia_properties.ipynb): calculate pure-fluid references, binary equilibrium, composition and pressure sensitivities, EOS uncertainty, and a connected heater-separator generator workflow with recovery, carryover, and closure checks.\n", + "\n", + "## Fluid mechanics\n", + "* [Fluid mechanics](fluidflow/FluidMechanics.ipynb)\n", + "* [Natural-gas pipeline linepack and operational flexibility](fluidflow/natural_gas_pipeline_linepack.ipynb): combine NeqSim real-gas properties and native PipeBeggsAndBrills pressure profiles with inventory integration, pack/unpack transients, deliverability limits, and operating-margin checks.\n", + "* **Advanced** - [\u00c5sgard Transport open route data to terrain-following NeqSim](fluidflow/asgard_transport_open_data_to_neqsim.ipynb): retrieve or replay public SODIR and EMODnet data, retain raw and 720 km engineering KP, create normalized five-point cross-sections and a transparent synthetic C5P candidate, and run a source-built segmented pressure/temperature model with flow sensitivity and refinement checks.\n", + "* **Advanced** - [Dynamic CO\u2082 and flow tracing from \u00c5sgard and Kristin to K\u00e5rst\u00f8](fluidflow/asgard_transport_dynamic_co2_tracing.ipynb): build NeqSim Java from an exact `master` commit, mix two gas sources, solve the inlet pressure required for a fixed K\u00e5rst\u00f8 boundary, compare first-order and TVD component transport with an explicit physical-dispersion sensitivity, refine grid/time resolution, and finish with a component-resolved gas/oil `TwoFluidPipe` conservation gate.\n", + "* [Norwegian NCS rich/dry gas network optimization](process/norwegian_ncs_gas_network_optimization.ipynb) \u2014 Gassco 2026 point-specific quality, NeqSim phase envelopes and ISO 6976, Beggs\u2013Brill capacity, onshore NGL recovery, a looped gas-network solve, reported export-rate validation, and future tie-in value-chain optimization.\n", + "* [NeqSim + OpenFOAM CFD with inline flow graphics](fluidflow/neqsim_openfoam_cfd.ipynb)\n", + "* [Tonal valve and piping noise: evidence-gated PEPR workflow](fluidflow/tonal_aeroacoustic_root_cause_gate.ipynb): combine a synthetic NeqSim gas letdown, valve-noise and vibration screens, a transient compressible finite-volume CFD/CAA verification, spectra, geometry and modal-data requirements, multi-hypothesis evidence synthesis, and an executable stop gate that prevents a tonal root-cause claim when installed evidence is missing.\n", + "* [Parametric CAD-to-CFD workflow with NeqSim, CadQuery, Gmsh, and OpenFOAM](fluidflow/neqsim_cadquery_gmsh_openfoam_workflow.ipynb): generate an exact STEP internal fluid volume and named STL boundaries, mesh CadQuery geometry with Gmsh physical groups, transfer NeqSim gas properties and flow into a three-dimensional OpenFOAM RANS case, render the actual mesh and CFD fields inline, validate geometry identity, mesh quality, convergence, and flow closure, and package reusable CAD, mesh, and case artifacts.\n", + "* [P&ID and mechanical datasheet to CAD and CFD with NeqSim](fluidflow/pid_datasheet_to_cad_cfd_neqsim.ipynb): normalize reviewed equipment, stream, nozzle, and instrument tags; verify dimensions with a calibrated-image QA record; calculate a three-phase CPA inlet with NeqSim; generate an exact separator gas-space STEP model with CadQuery; retain inlet, outlet, walls, and liquid-interface groups through Gmsh; run a real OpenFOAM RANS hydraulic screen; render the solved fields; and package the traceable design basis, CAD, mesh, case, and results.\n", + "* [Wet-gas centrifugal-compressor inlet with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_compressor_inlet_wet_gas.ipynb): generate a 3D suction CAD, solve the carrier gas, and screen liquid impaction and hot-wall evaporation.\n", + "* [Liquid-valve bubble formation with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_flashing_valve.ipynb): screen single-phase flow, local cavitation with pressure recovery, and sustained flashing.\n", + "* [Finite-rate pipeline evaporation and gas dissolution](fluidflow/pipeline_evaporation_and_gas_dissolution.ipynb): calculate droplet and film evaporation, gas dissolution into oil and water, heat and Maxwell\u2013Stefan mass transfer, slip, completion length, and incomplete phase transfer.\n", + "* [Single phase pipe flow](fluidflow/singlephaseflow.ipynb)\n", + "* [Multi phase pipe flow](fluidflow/multiphaseflow.ipynb)\n", + "* [Minimum-flow analysis for a long oil\u2013gas\u2013water flowline](fluidflow/minimum_flow_long_multiphase_flowline.ipynb): use the native `TwoFluidPipe` to locate terrain liquid accumulation, screen a multi-criterion minimum rate, test low-flow inventory growth and restart recovery, and quantify mesh sensitivity.\n", + "* **Advanced** - [Three-phase wellstream shutdown cooldown and executable OpenFOAM dead-leg screening](flowassurance/wellstream_shutdown_cooldown_to_openfoam_deadleg.ipynb): close a terrain-following gas\u2013oil\u2013water `TwoFluidPipe`, calculate axial no-touch time to an SRK-CPA hydrate-management boundary, then run an OpenFOAM 14 tee/dead-leg phase-settling and conjugate-cooldown screen with mesh, time-step, phase-volume, energy, cold-spot, hydrate-risk-volume, and heat-loss-feedback checks.\n", + "* [Flow induced vibrations (FIV)](fluidflow/FIVcalc.ipynb)\n", + "\n", + "## Heat and mass transfer\n", + "* [Non-equilibrium thermodynamics](thermodynamics/Nonequilibriumthermodynamics.ipynb)\n", + "* [Heat transfer](thermodynamics/heatTransfer.ipynb)\n", + "* [Mass transfer](thermodynamics/massTransfer.ipynb)\n", + "\n", + "## Thermodynamics of gas processing\n", + "* [PVT/density of gases](thermodynamics/density_of_gas.ipynb)\n", + "* [Phase envelopes of oil and gas](thermodynamics/Phase_envelopes_of_oil_and_gas.ipynb)\n", + "* [Water dew point calculation](thermodynamics/water_dew_point_claculations.ipynb)\n", + "* [Solubility of gases in water](thermodynamics/solubility_of_gases_in_water.ipynb)\n", + "* [Freezing point in LNG](thermodynamics/freezing_in_LNG.ipynb)\n", + "* [Phase behaviour of CO2](thermodynamics/PhaseBEhaviourCO2.ipynb)\n", + "* [Mercury in natural gas](thermodynamics/mercury_in_gas.ipynb)\n", + "* [H2S distribution in oil and gas processing](thermodynamics/H2Sdistribution.ipynb)\n", + "* [Simulation of fluids with water](thermodynamics/flash_with_salt_water.ipynb)\n", + "\n", + "\n", + "## Thermodynamic and Physical Properties\n", + "* [Thermodynamic properties](howtouseneqsim.ipynb)\n", + "* [Viscosty of fluids](thermodynamics/ViscosityOfFluids.ipynb)\n", + "* [Thermal conductivity of fluids](thermodynamics/ThermalConductivityOfFluids.ipynb)\n", + "* [Interfacial tension](thermodynamics/interfacialtension.ipynb)\n", + "* [Interface adsorption](thermodynamics/Interfacialadsorption.ipynb)\n", + "* [Diffusion coefficient](thermodynamics/diffusioncoefficients.ipynb)\n", + "\n", + "## Characterization of reservoir fluids\n", + "* [PVT of reservoir fluids](PVT/OilProperties.ipynb)\n", + "* [Characterization of a well fluid](PVT/fluidcharacterization.ipynb)\n", + "* [PVT experiments](PVT/PVTexperiments.ipynb)\n", + "* [Auditable PVT laboratory reports and fluid characterization](PVT/PVTreports.ipynb)\n", + "* [Complete PVT workflow: laboratory data, regression, separator optimization, and simulator export](PVT/pvt_workflow_from_lab_to_simulator.ipynb): characterize a reservoir oil, validate CCE and DLE, tune viscosity, optimize staged separation, and export reports, black-oil, and E300 models.\n", + "* [Black oil vs. computational simulation](PVT/blackoilvscomp.ipynb)\n", + "* [Eclipse-style PVT input and depletion-aware fluid recombination](PVT/eclipseFluidCharNeqSim.ipynb)\n", + "* [Characterization and plus fraction disitribution](PVT/GammaModel.ipynb)\n", + "* [Oil-assay cuts, pseudo-components, and fluid characterization](PVT/oilassay.ipynb)\n", + "\n", + "## Exploration and licence access\n", + "* [From opening an NCS area to an exploration discovery](fielddevelopment/ncs_area_opening_licensing_exploration_to_discovery.ipynb): follow the Norwegian opening and licensing framework through pre-qualification, licence groups and work obligations; screen synthetic public and MCS-style map layers; quantify play and prospect chance, probabilistic volumes and drilling value; characterize a hypothetical discovery sample in NeqSim; and hand an auditable discovery package to the reservoir-to-OPM Flow workflow.\n", + "* [NCS spatial history, discoveries, and future potential](fielddevelopment/ncs_spatial_history_discoveries_and_future_potential.ipynb): retrieve live SODIR discoveries, fields, wildcats, licences, plays, facilities, pipelines, and resource tables; reconstruct the staged northward development of the shelf; compare sea-area learning and opportunity archetypes; rank undeveloped discoveries with explicit limitations; and screen selected gas tie-backs with a source-built NeqSim Java master.\n", + "\n", + "## Reservoir simulations\n", + "* [Open SPE9 subsurface data with XTGeo, XTGeoViz, and NeqSim](reservoir/xtgeo_spe9_subsurface_to_neqsim.ipynb): load checksum-pinned synthetic SPE9 grid and restart properties with XTGeo; audit field-to-SI units; render maps and spaced candidate columns with XTGeoViz; parse the public PVTO deck; quantify STOIIP uncertainty; and pass rate scenarios into a composable NeqSim separation, cooling, and compression model with explicit conservation and capacity checks.\n", + "* [Introduction to reservoir simulations](reservoir/reservoirsimulation.ipynb)\n", + "* [Rock and reservoir flow from pore space to field development](reservoir/rock_flow_pore_to_field_neqsim.ipynb): use PoreSpy, OpenPNM, GSTools, SciPy, and current NeqSim Java master to connect digital rock, capillary invasion, pore-network permeability, SCAL, heterogeneous finite-volume waterflooding, well productivity, injection and water-handling constraints, surface separation, and OPM Flow handoffs.\n", + "* [A simplified reservoir simulation model](reservoir/simplereservoir.ipynb)\n", + "* [Composition gradient in a gas reservoir](reservoir/compositiongrad.ipynb)\n", + "* [NeqSim black-oil tables, OPM Flow reservoir simulation, and characterized process feeds](reservoir/neqsim_opm_flow_blackoil_coupling.ipynb): characterize a C20+ reservoir oil, generate Flow-compatible PVTO/PVDG/PVTW tables, run a 10 \u00d7 10 \u00d7 3 depletion and water-injection case, visualize the active-cell mesh and restart pressure/saturation states, reconstruct time-varying NeqSim well streams, and connect the maximum-load case to separation, compression, cooling, and oil letdown.\n", + "* [From seismic, samples, and petrophysics to an OPM Flow production forecast](reservoir/subsurface_data_to_opm_flow_production_forecast.ipynb): generate synthetic SEG-Y, LAS, RCAL/SCAL, pressure, and fluid-sample evidence; interpret petrophysics; build and upscale a seismic-guided geological model; generate all NeqSim PVT, grid, saturation-function, well, control, and schedule inputs; run low/base/high OPM Flow cases; and validate files, volumes, pressure, rates, and forecast results.\n", + "* **Advanced** - [FMU on the Norwegian Continental Shelf: Norne data to reservoir simulation, ERT and NeqSim](reservoir/fmu_norne_subsurface_to_facilities_workflow.ipynb): download a pinned public Norne model revision; parse and quality-control grid, petrophysical and well data; upscale to a transparent two-phase finite-volume reservoir model; run a base forecast; update uncertain reservoir parameters with ES-MDA and an ERT-ready case; propagate Q10/Q50/Q90 rates through a real NeqSim separation and compression process; apply facility constraints; and close the feedback loop to the reservoir controls.\n", + "* [ERT + NeqSim integrated field-development uncertainty](reservoir/ert_neqsim_integrated_field_development.ipynb): run 24 coupled reservoir, well, SURF, NeqSim process, and economic realizations with ERT; quantify P10/P50/P90 value, Q10/Q50/Q90 facility loads, capacity-exceedance probabilities, and dominant uncertainty drivers; and select internally consistent representative cases.\n", + "* [Stochastic reservoir-to-market optimization](reservoir/stochastic_reservoir_to_market_optimization.ipynb): preserve twelve internally consistent ERT, OPM Flow, NeqSim Java master, process, market, and economic realizations while optimizing well chokes, compressor configuration, host holdback, and common upgrade timing with Pyomo/HiGHS; rank complete P90/P50/P10 outcomes and quantify VSS and EVPI.\n", + "* [Discovery 1 NCS tie-in decision with OPM Flow and NeqSim master](reservoir/ncs_discovery_1_tie_in_opm_neqsim_master.ipynb): a guided neutral-labelled, composite open-data teaching case with learning objectives, 52 equations, interpretation prompts, 12 exercises, and a glossary. It uses public SODIR/FactMaps information, a public E300 analogue, OPM Flow, and NeqSim to explain reservoir-to-host coupling, holdback, utilization, and uncertainty. It contains no internal project data and is not a basis for real development decisions.\n", + "\n", + "## Wells\n", + "* [Introductin to oil and gas wells](well/wellcalcs.ipynb)\n", + "* [Rock-derived well productivity and injectivity with OPM Flow and NeqSim](well/rock_derived_productivity_injectivity_opm_neqsim.ipynb): derive permeability-thickness and skin from well-test evidence, generate SCAL with pyscal, run OPM Flow producer, injector, and bubble-point depletion cases, extract guarded pressure-to-rate curves with resdata, check voidage and fracture margin, and hand the inflow relationship to NeqSim wellbore and separation models.\n", + "* [Well nodal analysis and ECLIPSE VFPPROD lift curves](well/well_nodal_analysis_and_eclipse_vfp.ipynb): calculate gas-well IPR and VLP curves with NeqSim, solve and independently verify their operating-point crossing, screen tubing-head pressure, tubing size, and reservoir depletion, generate a rate\u2013THP lift-curve family, and export a validated five-dimensional VFPPROD include.\n", + "* [Integrated wells and production technology](well/integrated_wells_drilling_completion_lift_injection_intervention.ipynb): connect minimum-curvature drilling geometry, NeqSim well construction and cost, completion sensitivity, artificial-lift screening, water injection, well-integrity evidence, and acid intervention as one governed lifecycle.\n", + "\n", + "## Production technology\n", + "* [Production technology with NeqSim](production/production_technology.ipynb)\n", + "* [Well chemistry and water management with NeqSim](production/well_chemistry_and_water_management.ipynb)\n", + "* [Produced-water, sand, and production-chemicals management](production/produced_water_sand_chemicals_management.ipynb): connect three-phase separation, water quality, hydrocyclone/deoiling, discharge constraints, scale, corrosion, hydrate and wax inhibitor boundaries, MEG/TEG recovery, sand erosion, chemical consumption, and emissions.\n", + "\n", + "## Subsea facilities\n", + "* [Subsea production equipment and system screening with NeqSim](subseaequipment/subseaequipment.ipynb)\n", + "* [Simulation of subsea processes with NeqSim](subseaequipment/subsea_process_simulation.ipynb)\n", + "* [Multiphase flow of reservoir fluids with NeqSim](subseaequipment/multiphase_reservoir_fluid_flow.ipynb)\n", + "\n", + "## Unit Operations\n", + "* [Three-phase production separators with NeqSim](process/Separators.ipynb)\n", + "* [Heat Exchangers](process/heatexchangerDescription.ipynb)\n", + "* [Compressors and expanders](process/GasCompressors.ipynb)\n", + "* [Turboexpander-compressors](process/TurboExpanderCompressor_Example.ipynb)\n", + "* [Compressor curves and operating-envelope calculations](process/compressor_curves_and_compressor_calculations.ipynb)\n", + "* [Pumps](process/pumps.ipynb)\n", + "* [Valves](process/valves.ipynb)\n", + "* [Manifolds and pipes](process/manifoldsandpipes.ipynb)\n", + "* [Absoprtion](process/absorption.ipynb)\n", + "* [Adsorption](process/adsorption.ipynb)\n", + "* [Distillation](process/distillationoilgas.ipynb)\n", + "* [Mass transfer unit operations](process/masstransferMeOH.ipynb)\n", + "\n", + "## Process simulation\n", + "* [Reservoir-fluid tuning to a reference-process GOR with NeqSim](process/reservoirGORprocesstuning.ipynb): tune a bounded gas endmember against a four-stage separation train, close mass and energy balances, and quantify target and process-definition sensitivities.\n", + "* [Multistage oil stabilization with NeqSim](process/Simulationofanoilstabilizationprocess.ipynb): characterize a synthetic reservoir fluid, solve three equilibrium flash stages, close total/component/energy balances, screen separator capacity, and quantify volatility\u2013recovery trade-offs.\n", + "* [Simulation of a TEG dehydration process](process/simulationTEG.ipynb)\n", + "* [Reporting simulation results and report using field/SI units](process/processreportsandunits.ipynb)\n", + "* [Process simulation using neqsim](process/comparesimulations.ipynb)\n", + "* [Comparsion of process simulation using neqsim, UNISIM and DWSIM](process/comparesimulations2.ipynb)\n", + "* [Integrated oil stabilization, gas recompression, TEX, and NGL stabilization](process/oil_and_gas_Process_with_ngl_stabilizer_and_tex_process.ipynb): build a synthetic rich-fluid case through four-stage stabilization, three-stage flash-gas recompression, hydrocarbon-dew-point cooling, turboexpansion, a MESH-residual NGL stabilizer, product-quality checks, balances, and independent operating scenarios.\n", + "* [MEG regeneration and reclamation](process/MEGprocess.ipynb)\n", + "* [Development of large process models - use of sub-models](process/demo_field_process_model.ipynb)\n", + "\n", + "## Dynamic process simulations\n", + "* [Simulations of dynamic operation of a separator](process/dynamicsimul.ipynb)\n", + "* [Dynamic compressor calculations: speed response, pressure control, anti-surge, combined control, and startup](process/dynamiccompressor.ipynb): executable five-scenario workbook using current NeqSim thermodynamics, explicit two-volume mass balances, bounded PI and recycle controls, and engineering audits.\n", + "* [Dynamic compressor discharge-volume transient and PI flow control with NeqSim](process/dynamic_compressor_discharge_volume_control.ipynb): current complementary real-gas transient and controller example.\n", + "* [Dynamic compressor maps and anti-surge control](process/dynamiccompressor_dyn.ipynb): generate a five-speed chart with surge and stone-wall boundaries, compare native `AntiSurge` strategies, run a closed-loop turndown and recovery study, and preserve the five original connected-process transient workflows.\n", + "* [Integrated dynamic SURF\u2013topside\u2013control simulation](process/integrated_dynamic_surf_topside_control.ipynb): couple the corrected native `TwoFluidPipe` transient outlet process stream, terrain accumulation, and closed-ledger Lagrangian slug tracking to a dynamic receiving separator, compressor map, anti-surge recycle, stonewall arbitration, filtered pressure control, and fail-closed continuity and multiphase-flux audits using NeqSim Java built from `master`.\n", + "* [Custom external unit operation in dynamic simulation](process/dynamic_external_unit_operation.ipynb): implement a Python heater with thermal inertia, integrate it with NeqSim streams and equipment, and validate its energy balance and analytical response.\n", + "* [Pure component operations](process/singlecomponent.ipynb)\n", + "* [Pure component dynamic operations](process/singlecomponent_dyn.ipynb)\n", + "* [Dynamic control of an oil and gas separator](process/dynsep.ipynb)\n", + "* [Dynamic process simulation using reinforcement learning](process/RL_Process_Control.ipynb)\n", + "\n", + "## Gas processing design\n", + "* [Design of a gas-liquid separator](process/gas_oil_separation.ipynb)\n", + "* [Design of a TEG-dehydration process](process/TEGdehydration.ipynb)\n", + "* [Design of a shell and tube heat exchanger](process/heatexchanger.ipynb)\n", + "* [Offshore topside gas-processing train screening with NeqSim](process/topsideprocess.ipynb): cool and separate a synthetic well fluid, stabilize liquid, compress and cool export gas, close balances, and screen equipment capacity and operating sensitivities.\n", + "* [NGL extraction and fractionation with NeqSim](process/NGLextractionprocess.ipynb): model a characterized feed through cooling, expansion, scrubbing, heat recovery, MESH-residual fractionation, export compression, balance checks, product-quality calculations, and operating sensitivity.\n", + "* [Natural-gas compressor trains with NeqSim](process/GasCompressorTrain.ipynb): compare shortcut, detailed-EoS, Schultz, SRK, GERG-2008, and map-based methods; build two-stage compression, generate lift curves, and evaluate recycle-based antisurge control.\n", + "* [Automated process design in NeqSim](process/automatedprocessdesign.ipynb): build and audit a valve-cooler-separator-compressor flowsheet, screen 42 design points, apply constraints, trace a Pareto frontier, and export an automation snapshot.\n", + "* [Engineering calculations using the Python Fluids package](https://fluids.readthedocs.io/)\n", + "* [Engineering calculations using the Python heat transfer package](https://ht.readthedocs.io/)\n", + "* [Calculation of flow in fittings, valves, orifice plates etc.](process/fluidsandneqsim.ipynb)\n", + "* [NeqSim-connected separator sizing in SI units](process/sepsioze.ipynb): build a four-stage production process, extract phase properties, preserve and refresh seven vertical and horizontal two- and three-phase sizing cases, validate balances, and screen throughput and temperature sensitivities.\n", + "* [Gibbs reactor](reactions/GibbsReactor1.ipynb)\n", + "\n", + "## Downstream processing\n", + "* [How crude oil is sold to Europe](gasvaluechain/european_crude_oil_sales_foundation.ipynb): follow a synthetic North Sea cargo through quality, net standard quantity, quotation-period pricing, terminal and tanker logistics, buyer value, producer netback, and cash reconciliation.\n", + "* [Oil-refinery simulation foundation with NeqSim](process/neqsim_oil_refinery_simulation_foundation.ipynb): connect a synthetic TBP assay to a current-source NeqSim refinery front end, component and energy closure, cut recoveries, heat recovery, direct emissions, temperature sensitivity, margin, and crude-slate optimisation.\n", + "* [Crude-oil preflash and atmospheric flash-zone screening with NeqSim](process/oilrefineries.ipynb): characterize a synthetic TBP assay, model staged flash separation, close mass and energy balances, and screen operating sensitivities.\n", + "\n", + "## Refrigeration and heat pumps\n", + "* [Air and water cooling with NeqSim](process/air_and_water_cooling.ipynb)\n", + "* [Propane mechanical refrigeration with NeqSim](process/MechanicalCooling.ipynb): build and validate a vapour-compression cycle, balances, COP, scale-up, and operating sensitivities.\n", + "* [Propane heat-pump performance and operating limits with NeqSim](process/heatpumps.ipynb): calculate heating COP, source and sink sensitivities, seasonal performance, and preliminary 1 MW utility sizing.\n", + "\n", + "## LNG (liquified natural gas)\n", + "* [LNG process efficiency: SMR, C3MR, DMR, and nitrogen expansion](process/LNG_Liquefaction_Processes.ipynb)\n", + "* [Ship transport and LNG (LNG ageing)](process/lngageing.ipynb)\n", + "\n", + "## Hydrogen\n", + "* [Thermodynamic and physical properties of hydrogen](thermodynamics/ThermodynamicsOfHydrogen.ipynb)\n", + "* [Hydrogen production by reforming, shift, cooling, separation, and compression](thermodynamics/productionOfHydrogen.ipynb): preserve the original pre-reforming, thermochemistry, oxidation, and partial-oxidation cases; solve current NeqSim Gibbs equilibrium; audit balances and sensitivities; and connect syngas to a composable downstream process.\n", + "* [Hydrogen pipeline transport and compression with NeqSim](hydrogen/transportOfHydrogen.ipynb): preserve the original pure-H\u2082 property, two-stage compression, 500 km pipeline, profile, and ISO 6976 examples; add property-model comparison, mass and energy audits, diameter and throughput sensitivities, and seven inspected figures.\n", + "* [Liquefaction of hydrogen](hydrogen/liquefaction_of_hydrogen.ipynb)\n", + "\n", + "## Methanol\n", + "* [Thermodynamics of methanol](thermodynamics/ThermodynamicsOfMethanol.ipynb)\n", + "* [Physical properties of methanol](thermodynamics/PhysicalPropertiesOfMethanol.ipynb)\n", + "* [Production of methanol](reactions/Methanol_Production.ipynb)\n", + "\n", + "## Ammonia\n", + "* [Thermodynamics of ammonia](thermodynamics/ThermodynamicsOfAmmonia.ipynb): pure-ammonia phase equilibrium, public-property validation, model sensitivity, and process heating.\n", + "* [Physical properties of ammonia](thermodynamics/PhysicalPropertiesOfAmmonia.ipynb): phase-aware caloric and transport properties, validation, sensitivities, heat-transfer screening, and process integration.\n", + "* [Production of ammonia from natural gas](reactions/blue_ammonia_production.ipynb): steam reforming, water-gas shift, CO2 capture, compression, synthesis, and product recovery.\n", + "* [Ammonia as a refrigerant](process/ammonia_refrigeration.ipynb): saturation properties, vapour-compression cycle, composable process model, balances, and operating sensitivities.\n", + "* [Power production from ammonia](power/ammonia_power_generation.ipynb): atom-balanced combustion products, Brayton-cycle streams, power and efficiency balances, sensitivities, and emissions limitations.\n", + "\n", + "## Process safety\n", + "* Simulation of process blow down\n", + "* [Facilitator-ready HAZOP workshop with NeqSim](process/hazop_workshop_with_neqsim.ipynb): build a calculated inlet-separation and export-compression process; discover native nodes; apply facilitator-reviewed IEC 61882 deviations; quantify blocked outlet, compressor temperature, and gas blow-by; rank risk and produce an action register.\n", + "* [Loss-of-containment consequence analysis with NeqSim](process/loss_of_containment_consequence_analysis.ipynb): calculate transient source terms, choked-flow validation, 0.5 LFL and H2S endpoints, jet-fire radiation, VCE overpressure, and effect envelopes.\n", + "* [Barrier performance and ESD response-time verification with NeqSim](process/barrier_performance_and_esd_response_time.ipynb): derive process safety time; verify native detection, logic, and final-element timing; build cause-and-effect, evidence-linked performance standards, SCEs, and a barrier register; screen impairments.\n", + "* [DEXPI 2.0 to safety-study workflow with NeqSim](process/dexpi_safety_study_workflow.ipynb): export a calculated process to DEXPI 2.0; audit equipment, piping, instrumentation, design conditions, and safety semantics; build HAZOP nodes, C&E records, and a cross-study readiness matrix; retain missing SIS metadata in a controlled register.\n", + "* [Integrated SIL, ESD, HIPPS, and depressurization safety study](process/integrated_sil_esd_hipps_depressurization_study.ipynb): connect LOPA and native SIL verification to 2oo3 HIPPS voting, ESD timing, real-gas pressure rise, cold and fire blowdown, simultaneous flare load, and header-Mach sensitivity.\n", + "* [ESD, PSV, and controlled gas depressurization with NeqSim](process/ESD_PSV_Process_Safety_Demo.ipynb): calculate SRK gas properties, native PSV hysteresis and API 520 screening, blocked-outlet protection, manual and PSHH-triggered ESD blowdown, ISO 5167 orifice flow, balances, and blowdown-orifice trade-offs.\n", + "* [ESD system, alarm and flare](process/ESD_Fire_Alarm_System_Tutorial.ipynb)\n", + "* [Alarm handling in NeqSim](process/Alarm_Handling_NeqSim.ipynb)\n", + "* [Use of process simulators and NeqSim for process safety design](process/neqsim_process_safety_design.ipynb)\n", + "* [High Integrity Pressure Protection System (HIPPS) and process simulation](process/HIPPS_Safety_Simulations.ipynb)\n", + "* [Bow-tie, LOPA, and safety-instrumented risk analysis with NeqSim](process/bowtie_lopa_sif_risk_analysis.ipynb): connect an SRK inlet-separation process to native bow-tie generation, SIF PFD/SIL verification, LOPA, proof-test sensitivity, conservation checks, and a combined risk-and-capacity screen.\n", + "\n", + "## Power production\n", + "* [Electrical engineering fundamentals for oil and gas operations](power/electrical_engineering_fundamentals_oil_gas.ipynb): connect NeqSim compressor and water-injection pump duties to three-phase power, motors and VFDs, load lists, transformers, cables, fault current, starting, protection, harmonics, reliability, and operating constraints.\n", + "* [NCS wind, battery, and gas-turbine electrification with NeqSim](power/ncs_wind_battery_gas_electrification.ipynb): build NeqSim from a pinned repository commit, derive an offshore process load, dispatch Hywind-scale synthetic wind and balancing gas, evaluate native battery storage, and screen direct CO\u2082, curtailment, ramps, reserve, and field-maturity sensitivities.\n", + "* [Process-coupled offshore electrification with NeqSim](power/process_coupled_offshore_electrification_study.ipynb): extend a real separation and recompression process with 70-to-160 bara export compression, derive flow-dependent electrical demand from solved equipment duties, and compare gas turbines, shore power, wind, battery storage, and hybrid operation.\n", + "* [Natural-gas combustion, burner devices, and NeqSim integration with Cantera](reactions/natural_gas_combustion_with_cantera.ipynb): compare fuel and oxidizer blends; boiler, furnace, low-NOx, gas-turbine, duct-burner, and flare cases; validate composition and mass closure; model staged combustion; and run a Cantera custom unit inside a NeqSim process.\n", + "* [Gas-fired power plants with NeqSim](power/Gas_fired_power_plants.ipynb): fuel quality, stoichiometric combustion, Brayton-cycle states, heat recovery, balances, direct CO2 intensity, and operating sensitivities.\n", + "* [Natural-gas combined-cycle power plant with NeqSim](power/combined_cycle_power_plant.ipynb): Brayton gas turbine, single-pressure HRSG, CPA water/steam Rankine cycle, balances, quality checks, and operating sensitivities.\n", + "\n", + "## CO2 removal and handling\n", + "* [Thermodynamic and physical properties of CO2 \u2014 model selection, phase envelope, depressurization, and export conditioning](thermodynamics/ThermodynamicandphysicalpropertiesofCO2.ipynb)\n", + "* [EOS-CG and GERG-2008 for CO2](thermodynamics/EOSCG_vs_GERG2008.ipynb)\n", + "* [CO\u2082-rich phase envelopes, solids, hydrates, and conditioning](thermodynamics/phaseenvelopesofCO2richmixtures.ipynb): compare pure CO\u2082, CO\u2082\u2013CH\u2084, and CO\u2082\u2013CH\u2084\u2013H\u2082S phase behavior; screen solid and hydrate boundaries; map properties; and connect the results to compression, cooling, throttling, separation, balances, and operating sensitivity.\n", + "* [Thermodynamics of CO2 rich gases and water](thermodynamics/CO2richandwater.ipynb)\n", + "* [CO\u2082 solubility, speciation, and MDEA gas treating](thermodynamics/CO2alkonolamines.ipynb): compare Electrolyte-CPA and Electrolyte-ScRK equilibrium, inspect aqueous speciation, screen loading, temperature, model, and solvent-rate sensitivities, and run a balanced stream\u2013mixer\u2013separator contact process.\n", + "* CO2 removal from natural gas\n", + "* CO2 removal from gas fired power plants\n", + "* [CO\u2082 compression, intercooling, and dense-phase pumping with NeqSim](process/CO2_compression.ipynb): build a three-stage PR-EOS compression train with intercooling, dense-phase pumping, injection conditioning, phase-envelope diagnostics, stage-count and intercooler sensitivities, mass and energy closure, and operating-limit checks.\n", + "* [CO\u2082 dehydration for pipeline transport with NeqSim](process/CO2_Dehydration_Pipeline.ipynb): map saturated water content, impurity phase behaviour, transport density, TEG absorption, circulation and purity sensitivities, hydraulic screening, and mass balances.\n", + "* CO2 depressurization\n", + "* [CO2 injection pipelines](fluidflow/CO2pipeline.ipynb)\n", + "* [CO2 chain from compression to pipeline](process/co2_chain_from_compression_to_pipeline.ipynb): build and validate staged compression, intercooling, dense-phase diagnostics, and an 80 km NeqSim pipeline design study.\n", + "* [CO\u2082 trace-acid formation, partitioning, and compression conditioning with NeqSim](thermodynamics/CO2reactions.ipynb): preserve the original acid, sulfur, and pH lessons; apply an oxygen-limited element-balanced formation screen; refresh Peng\u2013Robinson and Electrolyte-CPA results; and run a two-stage compression, intercooling, and separation sensitivity workflow.\n", + "* [CO2 and reactions in a recompressor train](thermodynamics/Co2_recompression.ipynb)\n", + "\n", + "## Gas and oil transport\n", + "* [Design of a gas pipeline](fluidflow/gaspipeline.ipynb)\n", + "* [Design of a multi-phase pipeline](fluidflow/twophasepipeline.ipynb)\n", + "* [Two-fluid transient, slug-flow, and S-riser modelling](fluidflow/two_fluid_transient_slug_flow.ipynb): compare `TwoFluidPipe` with Beggs\u2013Brill, map flow regimes, simulate pressure and liquid-inventory transients, and study an oil-and-gas wellstream in an S-riser.\n", + "* [Flexible multiphase flowlines and risers](fluidflow/flixiblepipes.ipynb): use current `PipeBeggsAndBrills` profiles, thermal modes, connected lazy-wave-riser legs, API RP 14E velocity screening, and diameter/rate design scenarios.\n", + "* [Transient multiphase flow with NeqSim's drift-flux model](process/transient_multiphase_flow_tutorial.ipynb): build an SRK gas-condensate fluid, simulate horizontal and terrain flow, inspect drift-flux closure and accumulation, exercise controlled slug diagnostics, validate a vertical-riser initial state, test mesh sensitivity, and connect a flow-step case to a receiving separator.\n", + "* [Water-hammer simulation with NeqSim](process/water_hammer_simulation_tutorial.ipynb): use the released `WaterHammerPipe` MOC solver for valve closure, pressure histories and envelopes, closure-time sensitivity, cavitation screening, elevation, material effects, and ESD design.\n", + "\n", + "## Flow assurance\n", + "* [Thermodynamics of natural gas hydrates \u2014 CPA equilibrium, MEG and salt inhibition, and arrival-process screening](thermodynamics/thermodynamics_of_natural_gas_hydrates.ipynb)\n", + "* [Wax appearance, model tuning, and flowline operability with NeqSim](thermodynamics/thermodynamicsOfWax.ipynb): characterize a reservoir fluid, calculate and tune wax precipitation, map WAT and wax fraction, and screen cooling and insulation scenarios along a segmented flowline.\n", + "* [Mineral-scale thermodynamics and flowline control with NeqSim](thermodynamics/ThermodynamicsOfMineralScale.ipynb): calculate Electrolyte-CPA aqueous speciation and saturation indices, screen water chemistry and incompatible-brine mixing, profile flowline risk, assess inhibitor dose, and couple multiphase hydraulics to deposition.\n", + "* [PVT property tables for multiphase flow simulators](thermodynamics/PVTtableGeneration.ipynb): generate and audit OLGA-style tables for wet gas, characterized condensate, and an Eclipse-described well fluid; inspect phase/property grids, qualify interpolation resolution, and retain engineering checks.\n", + "* [Top-of-line condensation and MEG carryover with NeqSim](process/topoflinecondensation.ipynb): use CPA fugacity equilibrium to saturate gas with MEG-water liquid, quantify cold-wall condensate, close flash balances, screen a cooling flowline profile, and compare inhibitor strengths.\n", + "* [MEG injection and evaporation of water](thermodynamics/MEG_injection_and_evaporation.ipynb): model SRK-CPA water/MEG equilibrium, hydrate inhibition, evaporation, and a validated saturation\u2013injection\u2013separation workflow.\n", + "* [Asphaltenes in oil and gas production](thermodynamics/Asphaltene_Modeling_Tutorial.ipynb)\n", + "\n", + "## Process control\n", + "* [Dynamic process control with NeqSim](process/process_control_with_neqsim_v2.ipynb): build native separator pressure and level loops, connect two-stage separation and flash-gas recompression, implement cascade and ratio control, compare PI tuning, and filter transmitter noise.\n", + "* [Reinforcement Learning integration](process/reinforcement_learning_integration.ipynb)\n", + "* [Data-driven separator pressure control with NeqSim](process/data_driven_control_strategies.ipynb): build an SRK wellstream, equilibrium separator, gas valve, and pressure transmitter; derive real-gas inventory and valve-capacity tables; apply deterministic Kalman estimation and constrained MPC; and validate disturbance rejection, control bounds, and mass closure.\n", + "* [Model predictive control](process/model_predictive_controller_examples.ipynb)\n", + "* [DEXPI standard](process/dexpi_demo.ipynb)\n", + "\n", + "# Process automation and logics imlementation\n", + "* [Process logic and safeguarding with NeqSim](process/NeqSim_Process_Logic_Demo.ipynb): connect an SRK-CPA wellstream and heated three-phase separator to pressure detectors, 2oo3 HIPPS voting, sequenced ESD actions, fire-and-gas detection, response scenarios, and engineering checks.\n", + "* [Process alarms, interlocks, and sequential logic around a NeqSim model](process/ProcessLogicIntegrated.ipynb): build a current NeqSim valve-and-separator flowsheet, implement priority, deadband, latching, delayed blowdown, reset permissives, and validate a real-gas vessel balance.\n", + "\n", + "## Oil and Gas metering and analysis\n", + "* [Multi phase measurments](process/MultiphaseflowMeasurement.ipynb)\n", + "* [Allocation of production](process/allocationoilandgas2.ipynb)\n", + "\n", + "## Gas and Oil specifications\n", + "* [Calorific value of natural gas (ISO6976)](gasquality/CalorificValueNaturalGas.ipynb)\n", + "* [Natural-gas blending and quality optimization](gasquality/gas_blending_quality_optimization.ipynb)\n", + "* [Oil quality specifications: assay characterization, API gravity, viscosity, vapor pressure, and stabilization](gasquality/oilqualityspecifications.ipynb): executable SRK workflow with ASTM D6377 screening, six technical figures, and a connected valve-conditioner-separator application.\n", + "* [Hydrocarbon dew point](gasquality/hydrocarbon_dew_point_of_natural_gas.ipynb)\n", + "* [Oil vapour pressure: TVP, VPCR4, and RVP](process/TVP_RVP_Study.ipynb): calculate method-specific vapour pressure, composition and temperature sensitivity, and stabilization-pressure trade-offs.\n", + "\n", + "## Process simulation using NeqSim\n", + "* [Simulation of oil stabilization](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/oilstabilizationprocess.ipynb)\n", + "* [Produced-water treatment with NeqSim](process/producedwatertreatment.ipynb): preserve the two legacy CPA separation studies and hydrocyclone video; quantify flare gas, dissolved hydrocarbons, and BTEX over pressure and temperature; use the released Electrolyte-CPA water builder and native hydrocyclone sizing, PDR, droplet-distribution, capacity, reject-flow, and oil-in-water screening APIs; and close the connected process mass balance.\n", + "* [Simulation of TEG dehydration process](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/TEGprocessHX.ipynb)\n", + "* [Simulation of MEG hydrate inhibition and regeneration](process/MEGwaterprocess.ipynb): CPA hydrate envelopes, cold liquid dropout, and rich-MEG regeneration balances.\n", + "* [Exergy analysis of oil and gas processes](process/exergyanalysisofoilprocess.ipynb): preserve the legacy offshore process example while building a self-contained three-stage stabilization and export train, closing mass, component, and energy balances, auditing entropy and exergy, ranking irreversibility, and screening separator-pressure trade-offs.\n", + "* [Process equipment weight screening with NeqSim](process/weightofoilprocess.ipynb): rebuild the original offshore HP/MP/LP stabilization process with current APIs, close gas/oil/water balances, calculate native equipment and discipline weights, inspect separator dimensions, and rerun capacity sensitivities.\n", + "* [Optimization of a field producing from both gas and oil reservoir](reservoir/optimizationofoilandgasproduction.ipynb)\n", + "* [Process flow diagrams driven by NeqSim results](process/process_flow_diagram.ipynb): characterize a rich wellstream, connect separation, gas compression, and liquid throttling, verify balances, render a calculated pyflowsheet SVG with embedded tables, and screen export pressure.\n", + "* [Integration of third party tools into neqsim process simulations](process/neqsimreaktoro.ipynb)\n", + "* [Adding a new unit operation using python](process/newunitoperation.ipynb)\n", + "* [Adding a ML based unit operation](process/heat_exchanger_ml_external_unit.ipynb)\n", + "\n", + "\n", + "##Integrated modelling reservoir, well, transport and process\n", + "* [Reservoir-to-market system curves with a capacity-designed topside](reservoir/reservoir_well_topside_market_system_curves.ipynb): connect one fluid from reservoir inflow through well and flowline lift, HP/MP/LP separation, flash-gas recompression, two-stage export compression, separator and scrubber sizing, complete compressor maps, reservoir-pressure nodal matrices, debottlenecking, depletion, and capacity-bounded ECLIPSE VFPPROD export.\\n* [Integrating reservoir and process simulations](reservoir/reservoirandprocess.ipynb)\n", + "* [Ensemble-based integrated reservoir and process modelling](reservoir/ensamble_based_modelling.ipynb): update a reproducible reservoir ensemble with production history, transfer posterior rates through an explicit ERT-compatible contract, run a NeqSim choke, cooler, three-phase separator, and compressor, and quantify bottleneck probability and constrained production.\n", + "\n", + "## NeqSim PVT and Process Simulation API\n", + "* [How to create an API using NeqSim and Python](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/python)\n", + "* [How to create an API using NeqSim and Java](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/java)\n", + "* [NeqSim PVT API: executable property workflows](API/NeqSimPVTAPI.ipynb): build SRK, GERG-2008, and CPA fluids; run unit-aware TP flashes; characterize heavy fractions; retrieve named phase properties; compare gas models; sweep phase behavior; and validate closure and property trends.\n", + "* [NeqSim Process API: validated engineering calculation services](API/NeqSimProcessAPI.ipynb): build unit-aware request contracts for SRK-CPA TEG dehydration and a staged offshore process, return finite JSON-compatible results, reject invalid inputs, and screen independent operating scenarios.\n", + "\n", + "# Online process simulation\n", + "* [Online oil-stabilization simulation with NeqSim](process/onlineprocesssimulation.ipynb): build a PR well fluid, solve three-stage stabilization, recompress flash gas, update live inputs, run historian scenarios, and validate mass closure, product quality, power, and recycle convergence.\n", + "* [Implementing effective online process simulations](process/online_process_simulation_demo1.ipynb): build a four-stage stabilization, gas recompression, dew-point control, recycle, oil export, and produced-water process; compare cold, warm, and single-step online strategies over 30 points; inspect structured reports; and validate conservation, runtime, and provisional-result quality.\n", + "\n", + "## Process plant operation\n", + "* [Weather-aware gas processing and power generation with NeqSim](process/weatherandprocess.ipynb): preserve daily, hourly, station, and forecast workflows; calculate SRK air properties, export-compressor demand, turbine capacity, fuel, and direct emissions with deterministic weather fallbacks.\n", + "* Condition based monitoring\n", + "* [Condition-based monitoring of heat exchangers with NeqSim](process/heat_exchanger_condition_monitoring.ipynb): normalize historian data with native SRK and SRK-CPA streams, invert the two-stream HeatExchanger for effective UA, audit hot/cold duty consistency, detect fouling and sensor bias, verify cleaning recovery, and return reusable equipment-health snapshots.\n", + "\n", + "## Operations and asset management\n", + "\n", + "* [Facility lifecycle from commissioning to decommissioning](operations/facility_lifecycle_commissioning_to_decommissioning.ipynb): connect nitrogen properties, drying and inerting, hydrocarbon introduction, process ramp-up, shutdown inventory and low-temperature screening, late-life economic limits, cessation, P&A, removal schedules, waste and recycling, cost uncertainty, and emissions using public-PyPI NeqSim with Python.\n", + "* [Operations and asset management with NeqSim and Python](operations/operations_asset_management_with_neqsim.ipynb): connect source-built NeqSim process and failure consequences to censored reliability fitting, condition trends, maintenance-policy and shared-crew RAM simulation, availability, and uncertainty-weighted production accounting.\n", + "\n", + "## Materials in gas processing\n", + "* Material selection in gas processing\n", + "* [CO\u2082 corrosion, inhibition, and pipeline integrity](material/corrosionandthermo.ipynb): Electrolyte-CPA speciation, NORSOK M-506 screening, mechanistic inhibition, material selection, and coupled flowline integrity.\n", + "* [Elemental sulfur (S8) in natural gas processing](material/elemental_sulfur.ipynb)\n", + "* [Acid partitioning between gas, oil, and water](thermodynamics/formicacid_calculation.ipynb)\n", + "\n", + "## Emissions\n", + "* [CO2 emissions (scope 1, 2 and 3)](https://colab.research.google.com/drive/1Iwja5bKiP-fC2tS9O97-06j5XhqE0lJK#scrollTo=TgILfzXSzDAP)\n", + "* [Hydrocarbon emissions from processing and transport](emissions/Hydrocarbon_emissions.ipynb): use CPA phase equilibrium, high-pressure separation, isenthalpic letdown, low-pressure flash-gas accounting, methane/VOC breakdown, operating maps, and mitigation scenarios.\n", + "* [Chemicals consumption (glycols/etc)](process/cemicalsconsumption.ipynb): use CPA phase equilibrium and a connected SimpleTEGAbsorber to quantify equilibrium vapor loss, water removal, packing capacity, circulation sensitivity, and annual TEG make-up.\n", + "* [CO\u2082 emissions across the natural-gas value chain with NeqSim](emissions/CO2emsissionsinthevaluechain.ipynb): connect SRK separation, offshore and onshore compression, cooling, ISO 6976 gas quality, composition-derived carbon factors, Scope 1/2/3 boundaries, electrification scenarios, avoided-cost screening, and pressure\u2013power sensitivity.\n", + "\n", + "##Pipeline network optimization\n", + "* [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; use the same speed, power, map, and custom capacity constraints in typed operating-point results, bottleneck analysis, and maximum-throughput pressure-boundary optimization; quantify driver-upgrade value and production gains from declining inlet pressure.\n", + "* [Dry gas parallel-pipeline network optimization](process/PipeNetworkOptim.ipynb): balance unequal branches, screen capacity and diameters, map throughput, and connect compression, cooling, splitting, pipe hydraulics, and delivery-node mixing.\n", + "* [Multiphase network modeling and optimization](process/MultiphaseOptim.ipynb): balance two three-phase well branches at a shared manifold, screen export bore and backpressure, model thermal export hydraulics, and close a receiving-separator mass balance.\n", + "\n", + "\n", + "## Machine learning techniques and artificial intelligence (AI) in gas processing simulation\n", + "* Use of Languge Models and NeqSim\n", + "* Machine learning and PVT\n", + "* [Machine learning and process simulation](process/Machine_learning_and_process_simulation.ipynb)\n", + "* [AI-assisted gas-processing simulation and design with NeqSim](process/AIgasprocessing.ipynb): generate audited choke\u2013cooler\u2013three-phase-separator\u2013compressor cases, fit and validate a transparent quadratic surrogate, screen constrained operation, verify AI recommendations in NeqSim, and demonstrate synthetic residual monitoring.\n", + "* [Machine-learning-driven heat exchanger unit operations](process/heat_exchanger_ml_external_unit.ipynb)\n", + "\n", + "## Innovative technologies combining AI technologies and NeqSim\n", + "* [Real-Time process monitoring for operational safety and compliance](AI/Real_Time_process_monitoring_for_operational_safety_and_compliance.ipynb)\n", + "* [NeqSim and Data Analytics using Seeq](AI/NeqSim_and_Seeq.ipynb)\n", + "* Optimization of process parameters to maximize efficiency and production\n", + "* Anomaly detection to detect unusual patterns or deviations in process data\n", + "* Integration of different data sources to analyze data from different sources (sensor data, simulation data, weather data and external environmental data) to provide a more holistic understanding of processes\n", + "* Digital twins used to simulate and optimize processes in a virtual setting before implementation in the real world\n", + "* Self-adjusting control systems that adjust themselves based on continuous feedback and changes in process data\n", + "* Predicting oil spills or gas leaks for environmental monitoring\n", + "* Virtual measurements in oprocess plants using neqsim and AI technologies\n", + "* [IoT and Industry 4.0](AI/IoT_and_Industry4.0_with_NeqSim.ipynb)\n", + "\n", + "\n", + "## Statistics\n", + "* [Statistical Sites on the World Wide Web](statistics/WorldStats.ipynb)\n", + "* [World oil and natural-gas production statistics connected to NeqSim](statistics/worldOilandGasProduction.ipynb): audit an embedded 2024 Energy Institute/Our World in Data snapshot, close world totals, calculate producer shares and grouped concentration, apply SRK and ISO 6976 gas quality, bridge energy to standard volume, and screen export compression and direct-combustion scenarios.\n", + "* [Oil and Gas Price Statistics and Analysis](statistics/OilandGasPriceStatistics.ipynb)\n", + "* [Norwegian Continental Shelf production statistics and NeqSim export-process capacity screening](statistics/ProductionfromNorwegianContinentalShelf.ipynb)\n", + "* [CO2 emissions from oil and gas production](statistics/CO2emissionsNorwegianContinentalShelf.ipynb)\n", + "* [Statistics of CO2 in atmosphere](statistics/CO2inatm.ipynb)\n", + "\n", + "## Energy System Modelling\n", + "* [Energy system modelling](https://oemof.org/)\n", + "* [Thermal Engineering Systems](https://tespy.readthedocs.io/en/main/index.html)\n", + "* [A Sector-Coupled Open Optimisation Model of the European Energy System](https://pypsa-eur.readthedocs.io/en/latest/)\n", + "* [Examples of NeqSim in Energy System Modelling](energyopt/ThermalEnergyAndNeqSim.ipynb#scrollTo=C7-qUy51VbFs)\n", + "* [PyPSA-Earth example for Norway](energyopt/PyPSA-Earth_Norway.ipynb)\n", + "\n", + "## Economic analysis\n", + "* [NeqSim-connected NCS gas-field economy analysis](fielddevelopment/economy.ipynb): connect SRK separation and export compression, native mechanical-design cost estimation, declining field pressure and production, corrected tax-rate algebra, cash flow, NPV, IRR, payback, break-even, and commercial sensitivities.\n", + "* [Integrated process cost estimation and economic screening](fielddevelopment/process_cost_estimation_and_economics.ipynb): connect a gas-process simulation to mechanical design, CAPEX, OPEX, location/material/CEPCI sensitivities, and financial metrics.\n", + "\n", + "## Earth Tools and Metocean Data\n", + "* [Metocean and field development with NeqSim](fielddevelopment/metocean_and_field_development.ipynb): use a checksum-controlled public NORA10 teaching series with metocean statistics, weather windows, extremes, joint contours, spectra, tides, and NeqSim seasonal tie-back cases.\n", + "* [Earth tools and field development with NeqSim](fielddevelopment/earth_tools_and_field_development.ipynb): combine CRS-safe GeoPandas vectors, raster bathymetry, exclusion-aware least-cost routes, network analysis, interactive mapping, NeqSim tie-back hydraulics, and host economics.\n", + "\n", + "## Integrated energy and emission calculations\n", + "* [Integrated NeqSim + eCalc field-life, compressor-map, and offshore-energy study](power/neqsim_ecalc_integrated_energy_emissions.ipynb): combine three-pressure separation and recompression, complete NeqSim-generated compressor maps with surge and stonewall boundaries, declining inlet-pressure field life, native wind and battery dispatch, and eCalc fuel, CO\u2082, capacity, recirculation, and intensity accounting.\n", + "\n", + "## Northern Lights CCS open-data calculations\n", + "The numbered series uses the Equinor Northern Lights Databricks Marketplace listing as the authoritative full-data boundary and small hash-checked Eos snapshots for credential-free execution. NeqSim owns thermodynamics, wells, pipelines, facilities, and operations; OPM Flow owns dynamic storage-reservoir simulation.\n", + "* [01 - Northern Lights open-data foundation and NeqSim model basis](fielddevelopment/northern_lights/01_northern_lights_open_data_foundation.ipynb): audit licensed Eos trajectory, interpreted formation, UCS, and public well-test evidence; build an exact source-master NeqSim runtime; calculate EOS-CG CO\u2082 and CPA water anchors, a frictionless hydrostatic injection screen, and Phase 1/2 capacity translations; then issue explicit NeqSim and OPM Flow handoffs.\n", + "\n", + "## Volve field calculations\n", + "This principal eight-part field-lifecycle collection uses the Equinor Volve Data Village listing as its authoritative measured-data boundary and carries versioned handoff contracts from one discipline to the next. It follows the [NeqSim Field Development and Operations book](https://equinor.github.io/neqsimhome/doc/field_development_and_operations/book_standalone.html) from SEG-Y and development framing through OPM Flow, production technology, facilities, constrained operations, late life, shutdown, and decommissioning. A clean teaching fallback is stored for reproducibility, but it is explicitly labelled and is not represented as measured Volve data. Run the numbered notebooks in order.\n", + "* [01 - Volve seismic, wells, and static model](fielddevelopment/volve/01_volve_seismic_wells_static_model.ipynb): inventory and select Marketplace SEG-Y and well assets; perform geometry, amplitude, horizon, depth-conversion, log, petrophysical, fluid-density, volumetric, and uncertainty screens; then write the geoscience-to-reservoir handoff.\n", + "* [02 - Volve PVT, black oil, and reservoir](fielddevelopment/volve/02_volve_pvt_blackoil_reservoir.ipynb): select PVT, Eclipse/OPM, and production assets; characterize the fluid with NeqSim SRK; generate black-oil/DLE properties; screen history, material balance, depletion, and OPM Flow acceptance; then write the reservoir forecast handoff.\n", + "* [03 - Volve wells, SURF, and flow assurance](fielddevelopment/volve/03_volve_wells_surf_flow_assurance.ipynb): connect trajectories and completions to productivity, injectivity, IPR/VLP, NeqSim wellstreams, pipeline hydraulics, thermal response, hydrate margins, cooldown, and the facility-inlet envelope.\n", + "* [04 - Volve facilities and processing](fielddevelopment/volve/04_volve_facilities_processing.ipynb): model inlet conditioning, three-phase separation, oil stabilization, gas compression and cooling with NeqSim; locate bottlenecks; and screen produced water, chemicals, utilities, emissions, availability, and safeguarding.\n", + "* [05 - Volve integrated field twin and decisions](fielddevelopment/volve/05_volve_integrated_field_twin.ipynb): assemble all discipline contracts; reconcile history and capacity ownership; test interventions, uncertainty, economics, emissions, and decision gates; and issue a traceable integrated field-evaluation handoff.\n", + "* [06 - Volve reservoir simulation and production history matching](fielddevelopment/volve/06_volve_reservoir_history_matching.ipynb): obtain and audit the Marketplace production workbook; calculate NeqSim PVT anchors; run and fit a communicating two-tank reservoir simulator to pressure, oil, gas, and water; test a holdout period; diagnose identifiability; propagate uncertainty; and expose an optional controlled OPM Flow acceptance path.\n", + "* [07 - Volve all wells, nodal analysis, gathering, and full SURF](fielddevelopment/volve/07_volve_all_wells_gathering_surf.ipynb): simulate every active producer with IPR, NeqSim-calibrated tubing VLP, choke and branch loss; solve two manifolds, trunks, a common riser, and host backpressure; verify the network in a composable NeqSim ProcessSystem; and evaluate integrity, late-life, outage, shutdown, cooldown, hydrate, and restart cases.\n", + "* [08 - Volve closed-loop full-field development and operations](fielddevelopment/volve/08_volve_closed_loop_field_development_operations.ipynb): convert the static handoff into development scenarios and producer/injector placement; generate OPM well schedules and an OPM-primary history-match ensemble; feed NeqSim well, SURF, separation, compression, water, export, and injection limits back into monthly reservoir controls; and calculate the economic limit, shutdown handoff, plugging inventory, removal sequence, and decommissioning uncertainty.\n", + "* [Compact Volve full-field precursor with OPM Flow and NeqSim](fielddevelopment/volve_full_field_development_opm_neqsim.ipynb): compare producer/injector layouts in a reduced teaching sector and connect reservoir states to PVT, wells, SURF, topsides, schedule, economics, and uncertainty.\n", + "\n", + "## Field development\n", + "* **Advanced** - [NCS resource classification and project maturation with NeqSim](fielddevelopment/ncs_resource_classification_with_neqsim.ipynb): explain RC0-RC9, F/A project categories, BOI/BOK/BOV/BOG/PDO transitions, low/base/high uncertainty and discovery chance; build a governed project register with pandas and NetworkX; derive marketable gas and condensate yields with a source-traceable NeqSim process; connect volumes to capacity-limited profiles, synthetic economics, public SODIR data contracts, and reusable CSV/JSON handoffs; and link the exploration-to-market notebook chain.\n", + "* [SEG-Y with segyio to field-development decisions with NeqSim](fielddevelopment/segyio_to_field_development_workflow.ipynb): create and round-trip a synthetic 3-D SEG-Y cube, derive similarity and fault masks, interpret top/base/thickness, propagate low/base/high STOIIP, screen wells and field-life capacity, run NeqSim three-phase separation and export compression, and map the evidence to DG0-DG4 handoffs and decisions.\n", + "* [Offshore facility concept selection with NeqSim](fielddevelopment/offshore_facility_concept_selection_with_neqsim.ipynb): compare nine facility concepts using infrastructure distance, water depth, metocean extremes, environmental loads, weather operability, NeqSim tie-back hydraulics and process simulation, hard technical gates, lifecycle economics, weighted MCDA, TOPSIS, sensitivity analysis, and Monte Carlo robustness.\n", + "* [Norwegian field and area development lifecycle screening with NeqSim](fielddevelopment/norwegian_field_and_area_development_with_neqsim.ipynb): connect synthetic depletion and well deliverability to SRK phase behavior, choke/cooler/separator/compressor process models, SURF hydraulics, capacity constraints, export-pressure concepts, brownfield tie-in holdback, direct compressor emissions, product screens, and discounted cash flow.\n", + "* **Advanced** - [NCS tie-in from reservoir to market: host holdback, quality, tariffs, and tax](fielddevelopment/ncs_tie_in_quality_tariff_tax.ipynb): integrate a source-traceable NeqSim gas-condensate process, ASTM D6377 liquid vapour pressure, ISO 6976 gas blending, boundary-specific public quality examples, dated August 2026 Gassco K/I/O tariff and booking-vintage screens, host-priority/firm-tie/pro-rata holdback allocation, accepted-production economics, simplified petroleum tax, uncertainty, and multidisciplinary decision gates.\n", + "* [Producer and injector well-count and placement workflow](fielddevelopment/well_count_and_placement_workflow.ipynb): screen counts from deliverability, injectivity, and voidage replacement; optimize robust discrete layouts with pymoo NSGA-II; verify four concepts in OPM Flow; and connect the selected concept to NeqSim PVT, wells, SURF, facilities, capacity, economics, and uncertainty.\n", + "* [Discovery 1 DG1 SURF and host-facility design with NeqSim](fielddevelopment/discovery_1_dg1_surf_facilities_design.ipynb): mature the neutral-labelled Discovery 1 reservoir-to-host handoff into a 50-deliverable educational DG1 learning package covering flow assurance, subsea architecture, pipeline sizing, host processing, utilities, safety, cost, schedule, risk, and decision gates.\n", + "* Process design basis\n", + "* Proces design in a field development scenario\n", + "* [NPV of a gas-field tie-back with NeqSim](fielddevelopment/npv.ipynb): connect SRK fluid properties, tank depletion, well and tie-back hydraulics, host-arrival control, export compression, Norwegian petroleum-tax screening, break-even, coupled decision sensitivities, and driver-emissions scenarios.\n", + "\n", + "## Field developement case studies\n", + "* [Field development case 1 (combined oil and gas field)](reservoir/fieldDevelopment1.ipynb)\n", + "* Field development case 2 (rich gas and power from shore case)\n", + "* Field development case 3 (sales gas and power from shore)\n", + "* Field development case 4 (hydrogen production with power from shore/wind)\n", + "* Offshore field and onshore NGL process\n", + "\n", + "## Bio Engineering\n", + "* [Food-waste anaerobic digestion and biomethane-to-grid with NeqSim](bioprocesses/bioandneqsim.ipynb): connect empirical digestion, thermodynamic streams, gas upgrading, compression, cooling, mass balances, design sensitivities, and sustainability metrics.\n", + "* [Biomass to sustainable aviation fuel with NeqSim](bioprocesses/biomass_to_sustainable_aviation_fuel_with_neqsim.ipynb): compare HEFA, gasification\u2013FT, ATJ, fast pyrolysis, HTL, and a biogenic-CO\u2082 hybrid with sourced yields, oil quality, NeqSim ASTM oil-quality calculations, lifecycle data, logistics, economics, uncertainty, and validation.\n", + "\n", + "## Standards\n", + "* Energy Institute - Guideline for Flow Induced Vibrations (FIV) control in Production\n", + "* [NORSOK S-001 technical-safety screening with NeqSim](standards/technicalsafetyP0001.ipynb): flash a synthetic high-pressure gas, screen secondary pressure protection, run native transient-wall vessel blowdown and API pool-fire scenarios, compare BDV bore and flare-load trade-offs, and integrate 20 engineering checks.\n", + "* [NORSOK P-002 process-system design screening with NeqSim](standards/norsokP0002.ipynb)\n", + "* NORSOK I-106, Fiscal metering systems for hydrocarbon liquid and gas,\n", + "* (ISO 13703 - Design and installation of piping systems on offshore production platforms\n", + "* [API 521 blocked-liquid thermal-expansion and relief screening with NeqSim](standards/API521_thermal_expansion.ipynb): reproduce the original PR fluid and linear pressure calculation, correct derivative units and sign, solve a nonlinear density closure, estimate heat-duty relief load, exercise native Stream and SafetyValve blowdown, and verify 16 engineering checks.\n", + "\n", + "## Development of Process Digital Twins\n", + "* [Oil Process](process/oilgasprocess1.ipynb)\n", + "* Monitoring of Cricondenbar of Natural Gas\n", + "* Monitoring of TEG dehdydration processes\n", + "\n", + "## Excercise\n", + "* Excercise 1 - [Phase behaviour of reservoir fluids](excercise/Excercise_Phase_Behaviour_of_Reservoir_Fluids%20(1).ipynb)\n", + "* Excercise 2 - [Design of a gas-oil separation process](excercise/Design_of_a_separation_process_in_HYSYS.ipynb)\n", + "* Excercise 3 - [Design of a TEG dehydration process](excercise/Design_of_a_TEG_dehydration_process.ipynb): preserve and repair the three original CPA and Campbell examples; quantify wet-gas water, dew point, hydrate temperature, lean-TEG equilibrium, circulation, stage sensitivity, and a native `SimpleTEGAbsorber` workflow with total and water balances.\n", + "* Excercise 4 - [Export-gas phase envelopes and hydrocarbon dew-point control](excercise/calculationofphaseenvelopes.ipynb): preserve three original SRK envelope cases, correct temperature units, compare pressure-specific dew points, validate a Stream dew specification, and run a connected cooler and scrubber with balances and operating sensitivities.\n", + "* Excercise 5 - [Gas processing and the gas value chain](gasvaluechain/GasProcessingChain.ipynb)\n", + "\n", + "## TEP4185 - Gas Process Technology\n", + "* [NeqSim Thermodynamics](https://colab.research.google.com/drive/1c6OY3O1xX8nmaU5JadF3CIjaj4CSYb1Q?usp=sharing)\n", + "* [Pandas + NeqSim \u2014 Data\u2011driven Thermodynamics](https://colab.research.google.com/drive/1ngKz_Ry8PkJwENdNXIkZpO8PmARiqyGr?usp=sharing)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RMS-origin reservoir automation\n", + "\n", + "- [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and an RMS-agent contract.\n" + ] + } + ], + "metadata": { + "colab": { + "name": "examples of NeqSim in Colab.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json b/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json new file mode 100644 index 00000000..c5eb9722 --- /dev/null +++ b/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json @@ -0,0 +1,110 @@ +{ + "schema_version": 1, + "shard": "rms-opm-ert-agent-20260831", + "updated_at": "2026-08-31T17:05:33Z", + "notebooks": [ + { + "path": "notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb", + "verified_date": "2026-08-31", + "verified_at_utc": "2026-08-31T17:05:33Z", + "neqsim_version": "3.18.0 Python bridge with current source-built Java master", + "neqsim_commit": "1f7a01b06307d9d56451e43bb8d6023d28d14007", + "neqsim_jar_sha256": "1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739", + "python_version": "3.12.14", + "xtgeo_version": "4.25.1", + "opm_version": "2026.4", + "ert_version": "23.0.1", + "execution_status": "passed", + "execution_method": "Clean GitHub Actions Python 3.12 kernel; all cells executed top-to-bottom; NeqSim Java master built from source; OPM Flow base case and four ERT realizations completed.", + "code_cells": 42, + "substantive_code_cells": 42, + "markdown_cells": 20, + "git_blob_sha1": "160b7ffab7e3bf5d4f2dcfeaede8567d82b7d557", + "notebook_bytes": 2735505, + "publication": { + "branch": "codex/rms-agent-opm-ert-notebook", + "mode": "focused draft pull request", + "files": [ + "notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb", + "README.md", + "notebooks/examples_of_NeqSim_in_Colab.ipynb", + "notebooks/notebook_maintenance_ledger.json", + "notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json", + ".github/workflows/rms-opm-ert-notebook-validation.yml", + "scripts/generate_rms_agent_opm_ert_notebook.py" + ] + }, + "open_data": { + "repository": "equinor/xtgeo-testdata", + "commit": "cad17f24e22c19c6cefe6f647185395cc0a11add", + "license": "LGPL-3.0", + "dataset": "Public Reek geological and simulation ROFF exports with RMS-origin provenance", + "integrity": "Seven immutable files verified by embedded SHA-256 and byte counts." + }, + "capabilities_demonstrated": [ + "XTGeo ROFF ingestion, grid QC, 2x2x4 blocking, facies spreading, GRDECL export", + "NeqSim master-source SRK characterization and black-oil PVT generation", + "OPM Flow corner-point simulation with summary and restart-state inspection", + "ERT four-realization ensemble experiment using the FLOW forward model", + "NeqSim surface-process handover and governed RMS-agent job contract" + ], + "engineering_validation": { + "named_assertions_passed": 26, + "assertions_failed": 0, + "retained_png_figures": 13 + }, + "result_summary": { + "data_commit": "cad17f24e22c19c6cefe6f647185395cc0a11add", + "neqsim_commit": "1f7a01b06307d9d56451e43bb8d6023d28d14007", + "neqsim_jar_sha256": "1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739", + "grid_dimensions": [ + 40, + 64, + 14 + ], + "active_cells": 35838, + "blocking": [ + 2, + 2, + 4 + ], + "porosity_blocking_rmse": 0.0918770723782011, + "bubble_pressure_bara": 193.24040031433105, + "base_final_pressure_bara": 223.61802673339844, + "base_final_cumulative_oil_million_sm3": 7.305, + "ert_realizations": 4, + "ert_final_oil_range_million_sm3": [ + 7.305, + 7.305 + ], + "selected_process_realization": 0, + "compressor_power_MW": 2.680346000234267, + "process_mass_residual_kg_s": 5.423572702056845e-11, + "figures": [ + "workflow_architecture.png", + "reek_structure_and_static_maps.png", + "reek_property_distributions.png", + "reek_blocking_comparison.png", + "reek_property_spreading_layers.png", + "reek_well_screening.png", + "neqsim_black_oil_pvt.png", + "opm_flow_forecast.png", + "opm_final_restart_maps.png", + "opm_3d_water_front.png", + "ert_flow_ensemble.png", + "ert_parameter_response.png", + "integrated_process_workflow.png" + ], + "validation_checks_passed": 26, + "validation_checks_total": 26 + }, + "rendered_visual_validation": { + "renderer": "nbconvert HTML plus retained Matplotlib PNG outputs", + "figures_retained": 13, + "result": "pending human visual inspection before draft PR" + }, + "known_upstream_issue": null, + "issue_handling": "No upstream defect is claimed; limitations distinguish teaching assumptions from decision-grade RMS studies." + } + ] +} diff --git a/notebooks/notebook_maintenance_ledger.json b/notebooks/notebook_maintenance_ledger.json index 63fee7b1..8f32d82c 100644 --- a/notebooks/notebook_maintenance_ledger.json +++ b/notebooks/notebook_maintenance_ledger.json @@ -1,38 +1,38 @@ { - "schema_version": 3, - "updated_at": "2026-08-24T16:40:49Z", - "active_notebook_count": 294, - "shards_glob": "maintenance_ledger/*.json", - "retired_notebooks": [ - { - "path": "notebooks/process/syntheticdatageneration.ipynb", - "retired_date": "2026-08-04", - "reason": "The current Git blob is truncated at 100000 bytes and cannot be parsed; the last complete historical revision remains recoverable from Git history.", - "follow_up": "Replace with the planned NeqSim-master data-reconciliation and Bayesian digital-twin notebook." - }, - { - "path": "notebooks/reactions/reactions.ipynb", - "retired_date": "2026-08-04", - "reason": "One-byte blank placeholder with no executable or instructional content.", - "follow_up": "Use the maintained reaction notebooks in the same directory." - }, - { - "path": "notebooks/PVT/newtestNeqSim.ipynb", - "retired_date": "2026-08-04", - "reason": "Byte-identical orphaned duplicate with an obsolete external test-repository badge.", - "follow_up": "Use notebooks/examples_of_NeqSim_in_Colab.ipynb and topic-specific maintained tutorials." - }, - { - "path": "notebooks/fluidflow/newtestNeqSim.ipynb", - "retired_date": "2026-08-04", - "reason": "Byte-identical orphaned duplicate with an obsolete external test-repository badge.", - "follow_up": "Use notebooks/examples_of_NeqSim_in_Colab.ipynb and topic-specific maintained tutorials." - }, - { - "path": "notebooks/process/newtestNeqSim.ipynb", - "retired_date": "2026-08-04", - "reason": "Byte-identical orphaned duplicate with an obsolete external test-repository badge.", - "follow_up": "Use notebooks/examples_of_NeqSim_in_Colab.ipynb and topic-specific maintained tutorials." - } - ] + "schema_version": 3, + "updated_at": "2026-08-31T17:05:33Z", + "active_notebook_count": 295, + "shards_glob": "maintenance_ledger/*.json", + "retired_notebooks": [ + { + "path": "notebooks/process/syntheticdatageneration.ipynb", + "retired_date": "2026-08-04", + "reason": "The current Git blob is truncated at 100000 bytes and cannot be parsed; the last complete historical revision remains recoverable from Git history.", + "follow_up": "Replace with the planned NeqSim-master data-reconciliation and Bayesian digital-twin notebook." + }, + { + "path": "notebooks/reactions/reactions.ipynb", + "retired_date": "2026-08-04", + "reason": "One-byte blank placeholder with no executable or instructional content.", + "follow_up": "Use the maintained reaction notebooks in the same directory." + }, + { + "path": "notebooks/PVT/newtestNeqSim.ipynb", + "retired_date": "2026-08-04", + "reason": "Byte-identical orphaned duplicate with an obsolete external test-repository badge.", + "follow_up": "Use notebooks/examples_of_NeqSim_in_Colab.ipynb and topic-specific maintained tutorials." + }, + { + "path": "notebooks/fluidflow/newtestNeqSim.ipynb", + "retired_date": "2026-08-04", + "reason": "Byte-identical orphaned duplicate with an obsolete external test-repository badge.", + "follow_up": "Use notebooks/examples_of_NeqSim_in_Colab.ipynb and topic-specific maintained tutorials." + }, + { + "path": "notebooks/process/newtestNeqSim.ipynb", + "retired_date": "2026-08-04", + "reason": "Byte-identical orphaned duplicate with an obsolete external test-repository badge.", + "follow_up": "Use notebooks/examples_of_NeqSim_in_Colab.ipynb and topic-specific maintained tutorials." + } + ] } diff --git a/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb b/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb new file mode 100644 index 00000000..160b7ffa --- /dev/null +++ b/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb @@ -0,0 +1,7767 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ea4385bb", + "metadata": {}, + "source": [ + "# From an RMS-origin reservoir model to OPM Flow, ERT, and NeqSim\n", + "\n", + "**A fully executed NeqSim-Colab reservoir example using the public Reek model**\n", + "\n", + "This notebook reads a real exported corner-point model whose public provenance identifies an\n", + "RMS project, audits every input by SHA-256, demonstrates 2 × 2 × 4 blocking and property\n", + "spreading, generates a complete OPM Flow black-oil case, runs the simulator, reads restart\n", + "states, runs a four-realization ERT ensemble, and transfers a selected result to a NeqSim\n", + "surface-process model.\n", + "\n", + "Open in Colab:\n", + "https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb\n", + "\n", + "The stored outputs are evidence from a clean top-to-bottom run. Re-running will download the\n", + "same immutable public inputs and will build the current NeqSim Java master, recording its exact\n", + "commit and JAR digest." + ] + }, + { + "cell_type": "markdown", + "id": "2ebc0344", + "metadata": {}, + "source": [ + "## What this notebook proves\n", + "\n", + "By the end, the notebook has produced and checked:\n", + "\n", + "1. An immutable inventory of the public Reek ROFF files, including geometry and all properties.\n", + "2. A geological grid of 80 × 128 × 56 cells and a simulation grid of 40 × 64 × 14 cells.\n", + "3. Explicit 2 × 2 × 4 blocking, pore-volume-weighted porosity, and volume-majority facies.\n", + "4. Property maps, cross-sections, histograms, a 3-D reservoir view, and well screening.\n", + "5. NeqSim SRK fluid characterization and Flow-compatible PVTO, PVDG, PVTW, and DENSITY data.\n", + "6. A complete OPM Flow corner-point deck and a real dynamic simulation with restart maps.\n", + "7. A real ERT ensemble experiment in which porosity, permeability, and injection rate vary.\n", + "8. A NeqSim choke, separation, compression, cooling, and oil-letdown process driven by Flow rates.\n", + "9. A machine-readable job contract and tool-call ledger for a future RMS automation agent.\n", + "\n", + "**Important boundary.** RMS is commercial software and cannot be installed in a public Colab\n", + "runtime. The numerical data used here are genuine public ROFF exports from an RMS-origin Reek\n", + "model. The future-agent section shows how the same request is dispatched to a licensed,\n", + "allow-listed RMS worker. No proprietary project or license is copied into this notebook." + ] + }, + { + "cell_type": "markdown", + "id": "9fc524d6", + "metadata": {}, + "source": [ + "## End-to-end architecture and trust boundary\n", + "\n", + "The public teaching path and the future licensed path share the same versioned handover contract.\n", + "\n", + "| Stage | This executed notebook | Future production agent |\n", + "|---|---|---|\n", + "| RMS source | Public RMS-origin ROFF export | Licensed RMS worker |\n", + "| Grid/property API | XTGeo | RMS Python API plus XTGeo validation |\n", + "| Fluid model | NeqSim current Java master | Same |\n", + "| Dynamic model | OPM Flow 2026.04 | Same or approved simulator |\n", + "| Uncertainty | ERT 23.0.1 | ERT with governed storage/compute |\n", + "| Audit | Checksums, assertions, tool ledger | Signed job, identity, approvals, artifact registry |" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6c9022b9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:01:02.983408Z", + "iopub.status.busy": "2026-08-31T17:01:02.983235Z", + "iopub.status.idle": "2026-08-31T17:02:42.478459Z", + "shell.execute_reply": "2026-08-31T17:02:42.477471Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flow 2026.04\n", + "Python packages installed: {'neqsim': '3.18.0', 'opm': '2026.4', 'xtgeo': '4.25.1', 'ert': '23.0.1', 'nbformat': '5.10.4'}\n" + ] + } + ], + "source": [ + "import importlib.metadata\n", + "import os\n", + "from pathlib import Path\n", + "import re\n", + "import shutil\n", + "import subprocess\n", + "import sys\n", + "\n", + "REQUIRED_PACKAGES = {\n", + " \"neqsim\": \"3.18.0\",\n", + " \"opm\": \"2026.4\",\n", + " \"xtgeo\": \"4.25.1\",\n", + " \"ert\": \"23.0.1\",\n", + " \"nbformat\": \"5.10.4\",\n", + "}\n", + "\n", + "installed = {}\n", + "for package_name in REQUIRED_PACKAGES:\n", + " try:\n", + " installed[package_name] = importlib.metadata.version(package_name)\n", + " except importlib.metadata.PackageNotFoundError:\n", + " installed[package_name] = None\n", + "\n", + "requirements = [\n", + " f\"{name}=={required}\"\n", + " for name, required in REQUIRED_PACKAGES.items()\n", + " if installed[name] != required\n", + "]\n", + "if requirements:\n", + " subprocess.run(\n", + " [sys.executable, \"-m\", \"pip\", \"install\", \"--quiet\", *requirements],\n", + " check=True,\n", + " timeout=1200,\n", + " )\n", + "\n", + "FLOW_RUN_ENV = os.environ.copy()\n", + "FLOW_EXECUTABLE = os.environ.get(\"OPM_FLOW_EXECUTABLE\") or shutil.which(\"flow\")\n", + "if FLOW_EXECUTABLE is None:\n", + " os_release = Path(\"/etc/os-release\").read_text(encoding=\"utf-8\")\n", + " if \"Ubuntu\" not in os_release:\n", + " raise RuntimeError(\"OPM Flow binary installation requires an Ubuntu/Colab runtime.\")\n", + " privilege = [] if os.geteuid() == 0 else [\"sudo\"]\n", + " commands = [\n", + " privilege + [\"apt-get\", \"update\", \"-qq\"],\n", + " privilege + [\n", + " \"apt-get\", \"install\", \"-y\", \"-qq\", \"--no-install-recommends\",\n", + " \"software-properties-common\", \"mpi-default-bin\",\n", + " ],\n", + " privilege + [\"add-apt-repository\", \"-y\", \"ppa:opm/ppa\"],\n", + " privilege + [\"apt-get\", \"update\", \"-qq\"],\n", + " privilege + [\n", + " \"apt-get\", \"install\", \"-y\", \"-qq\", \"--no-install-recommends\",\n", + " \"libopm-simulators-bin\",\n", + " ],\n", + " ]\n", + " for command in commands:\n", + " result = subprocess.run(command, capture_output=True, text=True, timeout=1200)\n", + " if result.returncode != 0:\n", + " raise RuntimeError(result.stdout[-2000:] + \"\\n\" + result.stderr[-4000:])\n", + " FLOW_EXECUTABLE = shutil.which(\"flow\")\n", + "\n", + "if FLOW_EXECUTABLE is None:\n", + " raise RuntimeError(\"The OPM Flow executable was not found.\")\n", + "\n", + "flow_version_output = subprocess.run(\n", + " [FLOW_EXECUTABLE, \"--version\"],\n", + " check=True,\n", + " capture_output=True,\n", + " text=True,\n", + " timeout=60,\n", + ")\n", + "print(flow_version_output.stdout.strip() or flow_version_output.stderr.strip())\n", + "print(\"Python packages installed:\", REQUIRED_PACKAGES)" + ] + }, + { + "cell_type": "markdown", + "id": "3635f1c5", + "metadata": {}, + "source": [ + "## Reproducible current-master NeqSim runtime\n", + "\n", + "The public PyPI NeqSim package supplies only the Python bridge. The Java runtime below is built\n", + "from the selected equinor/neqsim source ref. The resolved Git commit, JAR SHA-256, and loaded\n", + "class location are recorded before any thermodynamic calculation. Set NEQSIM_SOURCE_REF to use\n", + "another reviewed ref. Validation automation may provide NEQSIM_SOURCE_ROOT and\n", + "NEQSIM_SOURCE_JAR from an exact pre-built checkout." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef84ab5f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:02:42.480377Z", + "iopub.status.busy": "2026-08-31T17:02:42.480164Z", + "iopub.status.idle": "2026-08-31T17:04:11.331524Z", + "shell.execute_reply": "2026-08-31T17:04:11.330525Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NeqSim source ref: master\n", + "NeqSim resolved commit: 1f7a01b06307d9d56451e43bb8d6023d28d14007\n", + "NeqSim JAR SHA-256: 1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739\n", + "Loaded class source: file:/home/runner/work/_temp/neqsim-java-master/target/neqsim-3.18.0.jar\n" + ] + } + ], + "source": [ + "import hashlib\n", + "import importlib.util\n", + "import jpype\n", + "\n", + "\n", + "def run_command(command, *, cwd=None, timeout=1800, environment=None):\n", + " result = subprocess.run(\n", + " command,\n", + " cwd=cwd,\n", + " env=environment,\n", + " text=True,\n", + " stdout=subprocess.PIPE,\n", + " stderr=subprocess.STDOUT,\n", + " timeout=timeout,\n", + " )\n", + " if result.returncode != 0:\n", + " tail = \"\\n\".join(result.stdout.splitlines()[-80:])\n", + " raise RuntimeError(f\"Command failed ({result.returncode}): {command}\\n{tail}\")\n", + " return result.stdout.strip()\n", + "\n", + "\n", + "NEQSIM_SOURCE_REF = os.environ.get(\"NEQSIM_SOURCE_REF\", \"master\")\n", + "supplied_source = os.environ.get(\"NEQSIM_SOURCE_ROOT\", \"\").strip()\n", + "supplied_jar = os.environ.get(\"NEQSIM_SOURCE_JAR\", \"\").strip()\n", + "\n", + "if supplied_source and supplied_jar:\n", + " neqsim_source = Path(supplied_source).resolve()\n", + " neqsim_jar = Path(supplied_jar).resolve()\n", + "else:\n", + " build_root = Path(\"/content\") if Path(\"/content\").exists() else Path(os.environ.get(\"RUNNER_TEMP\", \"/tmp\")).resolve()\n", + " neqsim_source = build_root / \"neqsim-java-master\"\n", + " if not neqsim_source.exists():\n", + " run_command(\n", + " [\n", + " \"git\", \"clone\", \"--depth\", \"1\", \"--branch\", NEQSIM_SOURCE_REF,\n", + " \"https://github.com/equinor/neqsim.git\", str(neqsim_source),\n", + " ],\n", + " timeout=600,\n", + " )\n", + " else:\n", + " run_command([\"git\", \"fetch\", \"--depth\", \"1\", \"origin\", NEQSIM_SOURCE_REF],\n", + " cwd=neqsim_source, timeout=600)\n", + " run_command([\"git\", \"checkout\", \"--detach\", \"FETCH_HEAD\"], cwd=neqsim_source)\n", + "\n", + " maven_settings = build_root / \"neqsim-maven-settings.xml\"\n", + " maven_settings.write_text(\n", + " \"canonical-central\"\n", + " \"central\"\n", + " \"https://repo.maven.apache.org/maven2/\"\n", + " \"\",\n", + " encoding=\"utf-8\",\n", + " )\n", + " run_command(\n", + " [\n", + " \"./mvnw\", \"-q\", \"-s\", str(maven_settings), \"-DskipTests\",\n", + " \"-Dmaven.javadoc.skip=true\", \"package\",\n", + " ],\n", + " cwd=neqsim_source,\n", + " timeout=2400,\n", + " )\n", + " built_jars = [\n", + " path for path in (neqsim_source / \"target\").glob(\"neqsim-*.jar\")\n", + " if \"sources\" not in path.name\n", + " and \"javadoc\" not in path.name\n", + " and not path.name.startswith(\"original-\")\n", + " ]\n", + " if not built_jars:\n", + " raise FileNotFoundError(\"Maven completed but no NeqSim JAR was found.\")\n", + " neqsim_jar = max(built_jars, key=lambda path: path.stat().st_size)\n", + "\n", + "if not neqsim_source.is_dir() or not neqsim_jar.is_file():\n", + " raise FileNotFoundError(\"NeqSim source root or built JAR is missing.\")\n", + "\n", + "neqsim_commit = run_command([\"git\", \"rev-parse\", \"HEAD\"], cwd=neqsim_source)\n", + "neqsim_jar_sha256 = hashlib.sha256(neqsim_jar.read_bytes()).hexdigest()\n", + "\n", + "os.environ[\"NEQSIM_JVM_AUTOSTART\"] = \"0\"\n", + "if not jpype.isJVMStarted():\n", + " jpype.addClassPath(str(neqsim_jar))\n", + " neqsim_package = importlib.util.find_spec(\"neqsim\")\n", + " if neqsim_package is None or not neqsim_package.submodule_search_locations:\n", + " raise ImportError(\"The public-PyPI neqsim bridge is not installed.\")\n", + " neqsim_python_root = Path(next(iter(neqsim_package.submodule_search_locations)))\n", + " for runtime_jar in sorted((neqsim_python_root / \"lib\").glob(\"*.jar\")):\n", + " if runtime_jar.resolve() != neqsim_jar:\n", + " jpype.addClassPath(str(runtime_jar))\n", + " jpype.startJVM()\n", + "\n", + "SystemSrkEos = jpype.JClass(\"neqsim.thermo.system.SystemSrkEos\")\n", + "class_source = str(\n", + " SystemSrkEos.class_.getProtectionDomain().getCodeSource().getLocation()\n", + ")\n", + "if neqsim_jar.name not in class_source:\n", + " raise RuntimeError(\"NeqSim classes were not loaded from the source-built JAR.\")\n", + "\n", + "print(\"NeqSim source ref:\", NEQSIM_SOURCE_REF)\n", + "print(\"NeqSim resolved commit:\", neqsim_commit)\n", + "print(\"NeqSim JAR SHA-256:\", neqsim_jar_sha256)\n", + "print(\"Loaded class source:\", class_source)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d4967baa", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:11.333487Z", + "iopub.status.busy": "2026-08-31T17:04:11.333262Z", + "iopub.status.idle": "2026-08-31T17:04:15.279363Z", + "shell.execute_reply": "2026-08-31T17:04:15.278580Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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runtimeversion or identity
0Python3.12.14
1Javaopenjdk version \"17.0.20.1\" 2026-08-18
2NeqSim Python bridge3.18.0
3NeqSim Java1f7a01b06307
4XTGeo4.25.1
5OPM Python2026.4
6OPM Flowflow 2026.04
7ERT23.0.1
\n", + "
" + ], + "text/plain": [ + " runtime version or identity\n", + "0 Python 3.12.14\n", + "1 Java openjdk version \"17.0.20.1\" 2026-08-18\n", + "2 NeqSim Python bridge 3.18.0\n", + "3 NeqSim Java 1f7a01b06307\n", + "4 XTGeo 4.25.1\n", + "5 OPM Python 2026.4\n", + "6 OPM Flow flow 2026.04\n", + "7 ERT 23.0.1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from importlib.metadata import version\n", + "import json\n", + "import math\n", + "import platform\n", + "import textwrap\n", + "import time\n", + "from urllib.request import urlretrieve\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xtgeo\n", + "from jpype import JClass\n", + "from neqsim import jneqsim\n", + "from neqsim.process.processTools import (\n", + " clearProcess, compressor, cooler, mixer, runProcess, separator,\n", + " separator3phase, stream, valve,\n", + ")\n", + "from neqsim.thermo import TPflash\n", + "from opm.io import Parser\n", + "from opm.io.ecl import EclFile, EGrid, ERst, ESmry\n", + "\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "pd.set_option(\"display.max_columns\", 40)\n", + "pd.set_option(\"display.width\", 160)\n", + "pd.set_option(\"display.max_rows\", 120)\n", + "\n", + "RANDOM_SEED = 20260831\n", + "RESERVOIR_TEMPERATURE_C = 97.5\n", + "RESERVOIR_TEMPERATURE_K = RESERVOIR_TEMPERATURE_C + 273.15\n", + "STANDARD_TEMPERATURE_C = 15.0\n", + "STANDARD_PRESSURE_BARA = 1.01325\n", + "INITIAL_RESERVOIR_PRESSURE_BARA = 260.0\n", + "PRODUCER_BHP_LIMIT_BARA = 80.0\n", + "SECONDS_PER_DAY = 86400.0\n", + "DLE_PRESSURES_BARA = np.array([300.0, 250.0, 220.0, 200.0, 180.0, 150.0, 100.0, 50.0, 1.01325])\n", + "GAS_PVT_PRESSURES_BARA = np.array([1.01325, 25.0, 50.0, 75.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0])\n", + "\n", + "OUTPUT_DIRECTORY = Path(\"rms_to_opm_outputs\").resolve()\n", + "DATA_DIRECTORY = OUTPUT_DIRECTORY / \"input\"\n", + "ERT_DIRECTORY = OUTPUT_DIRECTORY / \"ert\"\n", + "if OUTPUT_DIRECTORY.exists():\n", + " shutil.rmtree(OUTPUT_DIRECTORY)\n", + "DATA_DIRECTORY.mkdir(parents=True)\n", + "ERT_DIRECTORY.mkdir(parents=True)\n", + "FIGURE_PATHS = []\n", + "\n", + "runtime_table = pd.DataFrame({\n", + " \"runtime\": [\"Python\", \"Java\", \"NeqSim Python bridge\", \"NeqSim Java\", \"XTGeo\", \"OPM Python\", \"OPM Flow\", \"ERT\"],\n", + " \"version or identity\": [\n", + " platform.python_version(),\n", + " subprocess.run([\"java\", \"-version\"], capture_output=True, text=True, check=True).stderr.splitlines()[0],\n", + " version(\"neqsim\"),\n", + " neqsim_commit[:12],\n", + " version(\"xtgeo\"),\n", + " version(\"opm\"),\n", + " (flow_version_output.stdout or flow_version_output.stderr).strip().splitlines()[0],\n", + " version(\"ert\"),\n", + " ],\n", + "})\n", + "display(runtime_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "64dd1ab4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:15.281542Z", + "iopub.status.busy": "2026-08-31T17:04:15.281359Z", + "iopub.status.idle": "2026-08-31T17:04:15.622209Z", + "shell.execute_reply": "2026-08-31T17:04:15.621296Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "architecture_figure, axis = plt.subplots(figsize=(13.5, 5.0))\n", + "axis.set_xlim(0, 13.5)\n", + "axis.set_ylim(0, 5.0)\n", + "axis.axis(\"off\")\n", + "\n", + "nodes = [\n", + " (0.3, 2.0, 2.1, 1.1, \"RMS-origin\\nROFF export\", \"#5B8FF9\"),\n", + " (2.9, 2.0, 2.1, 1.1, \"XTGeo\\nQC + blocking\", \"#61DDAA\"),\n", + " (5.5, 3.25, 2.1, 1.1, \"NeqSim\\nPVT\", \"#F6BD16\"),\n", + " (5.5, 0.75, 2.1, 1.1, \"Agent job\\ncontract\", \"#9270CA\"),\n", + " (8.1, 2.0, 2.1, 1.1, \"OPM Flow\\nsimulation\", \"#E8684A\"),\n", + " (10.8, 3.25, 2.1, 1.1, \"ERT\\nensemble\", \"#6DC8EC\"),\n", + " (10.8, 0.75, 2.1, 1.1, \"NeqSim\\nfacilities\", \"#FF9D4D\"),\n", + "]\n", + "for x, y, width, height, label, color in nodes:\n", + " axis.add_patch(plt.Rectangle((x, y), width, height, facecolor=color, edgecolor=\"#263238\", linewidth=1.4))\n", + " axis.text(x + width / 2, y + height / 2, label, ha=\"center\", va=\"center\", color=\"white\", weight=\"bold\", fontsize=10)\n", + "\n", + "arrows = [\n", + " ((2.4, 2.55), (2.9, 2.55)),\n", + " ((5.0, 2.55), (8.1, 2.55)),\n", + " ((7.6, 3.8), (8.5, 3.1)),\n", + " ((7.6, 1.3), (8.5, 2.0)),\n", + " ((10.2, 2.75), (10.8, 3.55)),\n", + " ((10.2, 2.25), (10.8, 1.55)),\n", + "]\n", + "for start, end in arrows:\n", + " axis.annotate(\"\", xy=end, xytext=start, arrowprops={\"arrowstyle\": \"->\", \"linewidth\": 2, \"color\": \"#263238\"})\n", + "\n", + "axis.text(1.35, 1.55, \"public fixture\", ha=\"center\", color=\"#455A64\")\n", + "axis.text(6.55, 4.55, \"fluid contract\", ha=\"center\", color=\"#455A64\")\n", + "axis.text(6.55, 0.30, \"governed execution\", ha=\"center\", color=\"#455A64\")\n", + "axis.set_title(\"Executed public path and reusable production-agent boundary\", fontsize=15, weight=\"bold\")\n", + "path = OUTPUT_DIRECTORY / \"workflow_architecture.png\"\n", + "architecture_figure.savefig(path, dpi=160, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6e1c2997", + "metadata": {}, + "source": [ + "## 1. Immutable public RMS-origin inputs\n", + "\n", + "The data come from equinor/xtgeo-testdata at commit\n", + "cad17f24e22c19c6cefe6f647185395cc0a11add under LGPL-3.0. Its Reek readme states that the data\n", + "were taken from an RMS project. The public files contain no license, credentials, or private\n", + "asset paths.\n", + "\n", + "The geological ROFF contains geometry plus Poro, EQLNUM, and Facies. The simulation ROFF\n", + "contains the blocked grid. Separate simulation property files provide porosity, permeability,\n", + "facies, and zone. Every byte used below is downloaded from the immutable commit and checked." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ef5c87c1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:15.624231Z", + "iopub.status.busy": "2026-08-31T17:04:15.623918Z", + "iopub.status.idle": "2026-08-31T17:04:16.826876Z", + "shell.execute_reply": "2026-08-31T17:04:16.826071Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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filebytessha256verifiedimmutable URL
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" + ], + "text/plain": [ + " file bytes sha256 verified immutable URL\n", + "0 0readme.txt 149 8f76ce966e77a13cdeb1be9bf021b330719f34ac51d810... True https://raw.githubusercontent.com/equinor/xtge...\n", + "1 reek_geo2_grid_3props.roff 7837496 cf199d7126dc96b574e05c6709a5216f454e2da7ff5e01... True https://raw.githubusercontent.com/equinor/xtge...\n", + "2 reek_sim_grid.roff 376580 6454b7aa1d1f2701438310b8b3dc3101e70dd9643b49ec... True https://raw.githubusercontent.com/equinor/xtge...\n", + "3 reek_sim_poro.roff 143718 7368cc75d0436c3816fd16d61f77621c00f4f97ec25f0f... True https://raw.githubusercontent.com/equinor/xtge...\n", + "4 reek_sim_permx.roff 143719 37866e0fb8d9ab8e036f2ed9d34537a7274efbf6346652... True https://raw.githubusercontent.com/equinor/xtge...\n", + "5 reek_sim_facies2.roff 143786 6aeb52f6dd41f1e0783fb998a92b3da08cfa0f8fd5621f... True https://raw.githubusercontent.com/equinor/xtge...\n", + "6 reek_sim_zone.roff 143825 1d8f60577a4da72b6234eb036e8d1d0248513fb93d61b9... True https://raw.githubusercontent.com/equinor/xtge..." + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reek data are from\n", + "/project/fmu/tutorial/reek/resmod/ff/2016a/r004\n", + "copied at 2017-10-01. JRIV\n", + "\n", + "RMS project at ~jriv/work/testing/reek_grid.rms10.1.1\n", + "\n" + ] + } + ], + "source": [ + "DATA_COMMIT = \"cad17f24e22c19c6cefe6f647185395cc0a11add\"\n", + "DATA_BASE = f\"https://raw.githubusercontent.com/equinor/xtgeo-testdata/{DATA_COMMIT}/3dgrids/reek\"\n", + "DATA_MANIFEST = [\n", + " (\"0readme.txt\", \"8f76ce966e77a13cdeb1be9bf021b330719f34ac51d810569fc8e27df05cedcd\", 149),\n", + " (\"reek_geo2_grid_3props.roff\", \"cf199d7126dc96b574e05c6709a5216f454e2da7ff5e017bc7a503a6ab1a165c\", 7837496),\n", + " (\"reek_sim_grid.roff\", \"6454b7aa1d1f2701438310b8b3dc3101e70dd9643b49ec2f837a257e802ea90d\", 376580),\n", + " (\"reek_sim_poro.roff\", \"7368cc75d0436c3816fd16d61f77621c00f4f97ec25f0fcc2a7b0ddfe3cb3c14\", 143718),\n", + " (\"reek_sim_permx.roff\", \"37866e0fb8d9ab8e036f2ed9d34537a7274efbf63466526942e7da34ff297b53\", 143719),\n", + " (\"reek_sim_facies2.roff\", \"6aeb52f6dd41f1e0783fb998a92b3da08cfa0f8fd5621f537b4b10e4903f9bb9\", 143786),\n", + " (\"reek_sim_zone.roff\", \"1d8f60577a4da72b6234eb036e8d1d0248513fb93d61b955e58fab3563521baa\", 143825),\n", + "]\n", + "\n", + "download_rows = []\n", + "for filename, expected_sha256, expected_bytes in DATA_MANIFEST:\n", + " destination = DATA_DIRECTORY / filename\n", + " if not destination.exists():\n", + " urlretrieve(f\"{DATA_BASE}/{filename}\", destination)\n", + " actual_bytes = destination.stat().st_size\n", + " actual_sha256 = hashlib.sha256(destination.read_bytes()).hexdigest()\n", + " verified = actual_sha256 == expected_sha256 and actual_bytes == expected_bytes\n", + " if not verified:\n", + " raise RuntimeError(f\"Integrity check failed for {filename}\")\n", + " download_rows.append({\n", + " \"file\": filename,\n", + " \"bytes\": actual_bytes,\n", + " \"sha256\": actual_sha256,\n", + " \"verified\": verified,\n", + " \"immutable URL\": f\"{DATA_BASE}/{filename}\",\n", + " })\n", + "\n", + "download_table = pd.DataFrame(download_rows)\n", + "display(download_table)\n", + "print((DATA_DIRECTORY / \"0readme.txt\").read_text(encoding=\"utf-8\"))" + ] + }, + { + "cell_type": "markdown", + "id": "f76df784", + "metadata": {}, + "source": [ + "## 2. Read the geological and simulation grids with XTGeo\n", + "\n", + "ROFF is the native handover format in this example. XTGeo reads both corner-point geometry and\n", + "properties while retaining the I, J, K ordering and inactive-cell masks. The fine geological\n", + "model is not sent directly to Flow. It is compared with the supplied simulation-scale export so\n", + "that the blocking rules are visible and testable." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cb8ae552", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:16.828684Z", + "iopub.status.busy": "2026-08-31T17:04:16.828501Z", + "iopub.status.idle": "2026-08-31T17:04:16.985609Z", + "shell.execute_reply": "2026-08-31T17:04:16.984781Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " grid NI NJ NK total cells active cells\n", + "0 geological 80 128 56 573440 572992\n", + "1 simulation 40 64 14 35840 35838" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fine_grid_path = DATA_DIRECTORY / \"reek_geo2_grid_3props.roff\"\n", + "sim_grid_path = DATA_DIRECTORY / \"reek_sim_grid.roff\"\n", + "\n", + "fine_grid = xtgeo.grid_from_file(fine_grid_path)\n", + "fine_properties_collection = xtgeo.gridproperties_from_file(\n", + " fine_grid_path,\n", + " names=[\"Poro\", \"EQLNUM\", \"Facies\"],\n", + " grid=fine_grid,\n", + ")\n", + "fine_properties = {prop.name.lower(): prop for prop in fine_properties_collection.props}\n", + "\n", + "sim_grid = xtgeo.grid_from_file(sim_grid_path)\n", + "sim_poro = xtgeo.gridproperty_from_file(DATA_DIRECTORY / \"reek_sim_poro.roff\", grid=sim_grid)\n", + "sim_permx = xtgeo.gridproperty_from_file(DATA_DIRECTORY / \"reek_sim_permx.roff\", grid=sim_grid)\n", + "sim_facies = xtgeo.gridproperty_from_file(DATA_DIRECTORY / \"reek_sim_facies2.roff\", grid=sim_grid)\n", + "sim_zone = xtgeo.gridproperty_from_file(DATA_DIRECTORY / \"reek_sim_zone.roff\", grid=sim_grid)\n", + "sim_poro.name = \"PORO\"\n", + "sim_permx.name = \"PERMX\"\n", + "sim_facies.name = \"FACIES\"\n", + "sim_zone.name = \"FIPNUM\"\n", + "\n", + "fine_poro = fine_properties[\"poro\"]\n", + "fine_eqlnum = fine_properties[\"eqlnum\"]\n", + "fine_facies = fine_properties[\"facies\"]\n", + "\n", + "grid_table = pd.DataFrame([\n", + " {\n", + " \"grid\": \"geological\",\n", + " \"NI\": fine_grid.ncol,\n", + " \"NJ\": fine_grid.nrow,\n", + " \"NK\": fine_grid.nlay,\n", + " \"total cells\": fine_grid.ntotal,\n", + " \"active cells\": fine_grid.nactive,\n", + " },\n", + " {\n", + " \"grid\": \"simulation\",\n", + " \"NI\": sim_grid.ncol,\n", + " \"NJ\": sim_grid.nrow,\n", + " \"NK\": sim_grid.nlay,\n", + " \"total cells\": sim_grid.ntotal,\n", + " \"active cells\": sim_grid.nactive,\n", + " },\n", + "])\n", + "display(grid_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "768725ab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:16.987339Z", + "iopub.status.busy": "2026-08-31T17:04:16.987156Z", + "iopub.status.idle": "2026-08-31T17:04:17.043102Z", + "shell.execute_reply": "2026-08-31T17:04:17.042298Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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propertyminimummeanmaximumdefined cellsdiscretecodes
0fine Poro0.0000000.1587750.474730572992False
1fine EQLNUM1.0000001.5102202.000000572992True1, 2
2fine Facies0.0000000.4888922.000000572992True0, 1, 2
3simulation PORO0.0003870.1677400.36127735838False
4simulation PERMX [mD]0.148544984.24164510000.00000035838False
5simulation FACIES0.0000000.4275072.00000035838True0, 1, 2
6simulation Zone1.0000001.9277303.00000035810True1, 2, 3
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" + ], + "text/plain": [ + " property minimum mean maximum defined cells discrete codes\n", + "0 fine Poro 0.000000 0.158775 0.474730 572992 False \n", + "1 fine EQLNUM 1.000000 1.510220 2.000000 572992 True 1, 2\n", + "2 fine Facies 0.000000 0.488892 2.000000 572992 True 0, 1, 2\n", + "3 simulation PORO 0.000387 0.167740 0.361277 35838 False \n", + "4 simulation PERMX [mD] 0.148544 984.241645 10000.000000 35838 False \n", + "5 simulation FACIES 0.000000 0.427507 2.000000 35838 True 0, 1, 2\n", + "6 simulation Zone 1.000000 1.927730 3.000000 35810 True 1, 2, 3" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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quantityminimummeanmaximumunit
0X456620.791461866.191467106.330m
1Y5926989.5025932905.7695938826.892m
2depth1553.3801726.8801977.857m TVDSS
3cell thickness0.0573.2797.720m
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" + ], + "text/plain": [ + " quantity minimum mean maximum unit\n", + "0 X 456620.791 461866.191 467106.330 m\n", + "1 Y 5926989.502 5932905.769 5938826.892 m\n", + "2 depth 1553.380 1726.880 1977.857 m TVDSS\n", + "3 cell thickness 0.057 3.279 7.720 m" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "property_objects = {\n", + " \"fine Poro\": fine_poro,\n", + " \"fine EQLNUM\": fine_eqlnum,\n", + " \"fine Facies\": fine_facies,\n", + " \"simulation PORO\": sim_poro,\n", + " \"simulation PERMX [mD]\": sim_permx,\n", + " \"simulation FACIES\": sim_facies,\n", + " \"simulation Zone\": sim_zone,\n", + "}\n", + "summary_rows = []\n", + "for label, prop in property_objects.items():\n", + " values = prop.values\n", + " summary_rows.append({\n", + " \"property\": label,\n", + " \"minimum\": float(np.ma.min(values)),\n", + " \"mean\": float(np.ma.mean(values)),\n", + " \"maximum\": float(np.ma.max(values)),\n", + " \"defined cells\": int(values.count()),\n", + " \"discrete\": bool(prop.isdiscrete),\n", + " \"codes\": \", \".join(str(int(value)) for value in np.unique(values.compressed())[:12]) if prop.isdiscrete else \"\",\n", + " })\n", + "property_summary = pd.DataFrame(summary_rows)\n", + "display(property_summary.round(6))\n", + "\n", + "sim_x, sim_y, sim_z = sim_grid.get_xyz()\n", + "sim_dz = sim_grid.get_dz()\n", + "coordinate_table = pd.DataFrame({\n", + " \"quantity\": [\"X\", \"Y\", \"depth\", \"cell thickness\"],\n", + " \"minimum\": [\n", + " float(np.ma.min(sim_x.values)), float(np.ma.min(sim_y.values)),\n", + " float(np.ma.min(sim_z.values)), float(np.ma.min(sim_dz.values)),\n", + " ],\n", + " \"mean\": [\n", + " float(np.ma.mean(sim_x.values)), float(np.ma.mean(sim_y.values)),\n", + " float(np.ma.mean(sim_z.values)), float(np.ma.mean(sim_dz.values)),\n", + " ],\n", + " \"maximum\": [\n", + " float(np.ma.max(sim_x.values)), float(np.ma.max(sim_y.values)),\n", + " float(np.ma.max(sim_z.values)), float(np.ma.max(sim_dz.values)),\n", + " ],\n", + " \"unit\": [\"m\", \"m\", \"m TVDSS\", \"m\"],\n", + "})\n", + "display(coordinate_table.round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "619b7aab", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:17.044802Z", + "iopub.status.busy": "2026-08-31T17:04:17.044633Z", + "iopub.status.idle": "2026-08-31T17:04:18.882098Z", + "shell.execute_reply": "2026-08-31T17:04:18.881232Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fine_x, fine_y, fine_z = fine_grid.get_xyz()\n", + "fine_top = np.ma.filled(fine_z.values[:, :, 0], np.nan)\n", + "sim_top = np.ma.filled(sim_z.values[:, :, 0], np.nan)\n", + "sim_poro_map = np.nanmean(np.ma.filled(sim_poro.values, np.nan), axis=2)\n", + "sim_logk_map = np.nanmean(np.log10(np.ma.filled(sim_permx.values, np.nan)), axis=2)\n", + "\n", + "structure_figure, axes = plt.subplots(2, 2, figsize=(13.5, 9.0), constrained_layout=True)\n", + "items = [\n", + " (fine_top.T, \"Geological-grid top depth\", \"viridis_r\", \"m TVDSS\"),\n", + " (sim_top.T, \"Simulation-grid top depth\", \"viridis_r\", \"m TVDSS\"),\n", + " (sim_poro_map.T, \"Simulation-grid mean porosity\", \"YlGnBu\", \"fraction\"),\n", + " (sim_logk_map.T, \"Simulation-grid mean log10(PERMX)\", \"magma\", \"log10(mD)\"),\n", + "]\n", + "for axis, (array, title, cmap, unit) in zip(axes.ravel(), items):\n", + " image = axis.imshow(array, origin=\"lower\", aspect=\"auto\", cmap=cmap)\n", + " axis.set_title(title)\n", + " axis.set_xlabel(\"I column\")\n", + " axis.set_ylabel(\"J row\")\n", + " structure_figure.colorbar(image, ax=axis, label=unit, shrink=0.82)\n", + "\n", + "path = OUTPUT_DIRECTORY / \"reek_structure_and_static_maps.png\"\n", + "structure_figure.savefig(path, dpi=165, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9f2d29e2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:18.883948Z", + "iopub.status.busy": "2026-08-31T17:04:18.883773Z", + "iopub.status.idle": "2026-08-31T17:04:21.225611Z", + "shell.execute_reply": "2026-08-31T17:04:21.224848Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "active_poro = sim_poro.values.compressed()\n", + "active_permx = sim_permx.values.compressed()\n", + "active_facies = sim_facies.values.compressed().astype(int)\n", + "\n", + "distribution_figure, axes = plt.subplots(1, 3, figsize=(14.5, 4.4), constrained_layout=True)\n", + "axes[0].hist(active_poro, bins=30, color=\"#007F87\", edgecolor=\"white\")\n", + "axes[0].set(xlabel=\"Porosity [-]\", ylabel=\"Active cells\", title=\"Simulation porosity\")\n", + "axes[1].hist(active_permx, bins=np.logspace(np.log10(active_permx.min()), np.log10(active_permx.max()), 32),\n", + " color=\"#C75B12\", edgecolor=\"white\")\n", + "axes[1].set_xscale(\"log\")\n", + "axes[1].set(xlabel=\"PERMX [mD]\", ylabel=\"Active cells\", title=\"Permeability\")\n", + "scatter = axes[2].scatter(active_poro, active_permx, c=active_facies, cmap=\"viridis\",\n", + " s=7, alpha=0.28, rasterized=True)\n", + "axes[2].set_yscale(\"log\")\n", + "axes[2].set(xlabel=\"Porosity [-]\", ylabel=\"PERMX [mD]\", title=\"Rock-property cross-plot\")\n", + "distribution_figure.colorbar(scatter, ax=axes[2], label=\"Facies code\")\n", + "path = OUTPUT_DIRECTORY / \"reek_property_distributions.png\"\n", + "distribution_figure.savefig(path, dpi=165, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7589961c", + "metadata": {}, + "source": [ + "## 3. Blocking and spreading properties\n", + "\n", + "The dimensions reveal an exact 2 × 2 × 4 relationship:\n", + "\n", + "$$\n", + "(80,128,56) \\rightarrow (40,64,14).\n", + "$$\n", + "\n", + "For block B, porosity is pore-volume weighted:\n", + "\n", + "$$\n", + "\\phi_B = \\frac{\\sum_{c\\in B} \\phi_c V_c A_c}\n", + "{\\sum_{c\\in B} V_c A_c},\n", + "$$\n", + "\n", + "where V is bulk volume and A is the active-cell indicator. Facies is assigned by the largest\n", + "active bulk volume in the block. For teaching, fine Background, Channel, and Crevasse codes are\n", + "mapped to simulation SHALE, COARSESAND, and FINESAND codes. This mapping is explicit and is not\n", + "claimed to reproduce every hidden RMS workflow setting.\n", + "\n", + "Permeability is not arithmetically averaged here. The supplied simulation PERMX is used because\n", + "flow upscaling is directional and depends on boundary conditions. The future RMS job contract\n", + "therefore names the approved permeability-upscaling workflow rather than silently inventing one." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ada89394", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:21.227626Z", + "iopub.status.busy": "2026-08-31T17:04:21.227434Z", + "iopub.status.idle": "2026-08-31T17:04:21.307410Z", + "shell.execute_reply": "2026-08-31T17:04:21.306600Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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metricvalueunit
0fine cells per simulation block16.000000fine cells
1porosity RMSE0.091877fraction
2porosity mean bias-0.008949fraction
3porosity correlation0.089336correlation
4facies agreement after teaching map0.406870fraction
5compared active blocks35810.000000blocks
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" + ], + "text/plain": [ + " metric value unit\n", + "0 fine cells per simulation block 16.000000 fine cells\n", + "1 porosity RMSE 0.091877 fraction\n", + "2 porosity mean bias -0.008949 fraction\n", + "3 porosity correlation 0.089336 correlation\n", + "4 facies agreement after teaching map 0.406870 fraction\n", + "5 compared active blocks 35810.000000 blocks" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "BLOCK = (2, 2, 4)\n", + "assert (\n", + " fine_grid.ncol // sim_grid.ncol,\n", + " fine_grid.nrow // sim_grid.nrow,\n", + " fine_grid.nlay // sim_grid.nlay,\n", + ") == BLOCK\n", + "\n", + "fine_actnum = np.ma.filled(fine_grid.get_actnum().values, 0).astype(bool)\n", + "fine_bulk_volume = np.ma.filled(fine_grid.get_bulk_volume().values, 0.0)\n", + "fine_poro_values = np.ma.filled(fine_poro.values, 0.0)\n", + "fine_facies_values = np.ma.filled(fine_facies.values, 0).astype(int)\n", + "\n", + "def block_sum(array):\n", + " reshaped = array.reshape(\n", + " sim_grid.ncol, BLOCK[0],\n", + " sim_grid.nrow, BLOCK[1],\n", + " sim_grid.nlay, BLOCK[2],\n", + " )\n", + " return reshaped.sum(axis=(1, 3, 5))\n", + "\n", + "active_volume = fine_bulk_volume * fine_actnum\n", + "blocked_volume = block_sum(active_volume)\n", + "blocked_poro = np.divide(\n", + " block_sum(fine_poro_values * active_volume),\n", + " blocked_volume,\n", + " out=np.full_like(blocked_volume, np.nan, dtype=float),\n", + " where=blocked_volume > 0.0,\n", + ")\n", + "\n", + "facies_volume = np.stack([\n", + " block_sum(active_volume * (fine_facies_values == code_value))\n", + " for code_value in [0, 1, 2]\n", + "])\n", + "fine_majority_facies = np.argmax(facies_volume, axis=0)\n", + "fine_to_sim_facies = np.array([0, 2, 1])\n", + "blocked_facies = fine_to_sim_facies[fine_majority_facies]\n", + "\n", + "sim_actnum = np.ma.filled(sim_grid.get_actnum().values, 0).astype(bool)\n", + "sim_poro_values = np.ma.filled(sim_poro.values, np.nan)\n", + "sim_facies_values = np.ma.filled(sim_facies.values, -1).astype(int)\n", + "comparison_mask = sim_actnum & np.isfinite(blocked_poro) & np.isfinite(sim_poro_values)\n", + "\n", + "poro_difference = blocked_poro[comparison_mask] - sim_poro_values[comparison_mask]\n", + "blocking_metrics = pd.DataFrame({\n", + " \"metric\": [\n", + " \"fine cells per simulation block\",\n", + " \"porosity RMSE\",\n", + " \"porosity mean bias\",\n", + " \"porosity correlation\",\n", + " \"facies agreement after teaching map\",\n", + " \"compared active blocks\",\n", + " ],\n", + " \"value\": [\n", + " int(np.prod(BLOCK)),\n", + " float(np.sqrt(np.mean(poro_difference ** 2))),\n", + " float(np.mean(poro_difference)),\n", + " float(np.corrcoef(blocked_poro[comparison_mask], sim_poro_values[comparison_mask])[0, 1]),\n", + " float(np.mean(blocked_facies[comparison_mask] == sim_facies_values[comparison_mask])),\n", + " int(comparison_mask.sum()),\n", + " ],\n", + " \"unit\": [\"fine cells\", \"fraction\", \"fraction\", \"correlation\", \"fraction\", \"blocks\"],\n", + "})\n", + "display(blocking_metrics.round(6))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8ae29c6c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:21.309459Z", + "iopub.status.busy": "2026-08-31T17:04:21.309274Z", + "iopub.status.idle": "2026-08-31T17:04:23.122360Z", + "shell.execute_reply": "2026-08-31T17:04:23.121549Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "layer_index = sim_grid.nlay // 2\n", + "fine_layer_start = layer_index * BLOCK[2]\n", + "fine_layer_mean = np.nanmean(\n", + " np.ma.filled(fine_poro.values[:, :, fine_layer_start:fine_layer_start + BLOCK[2]], np.nan),\n", + " axis=2,\n", + ")\n", + "\n", + "blocking_figure, axes = plt.subplots(2, 2, figsize=(13.0, 9.0), constrained_layout=True)\n", + "arrays = [\n", + " (fine_layer_mean.T, \"Fine porosity: four layers before blocking\", \"YlGnBu\", 0.0, 0.40),\n", + " (blocked_poro[:, :, layer_index].T, \"Calculated PV-weighted blocked porosity\", \"YlGnBu\", 0.0, 0.40),\n", + " (sim_poro_values[:, :, layer_index].T, \"Supplied RMS simulation porosity\", \"YlGnBu\", 0.0, 0.40),\n", + " ((blocked_poro[:, :, layer_index] - sim_poro_values[:, :, layer_index]).T,\n", + " \"Calculated minus supplied\", \"coolwarm\", -0.12, 0.12),\n", + "]\n", + "for axis, (array, title, cmap, vmin, vmax) in zip(axes.ravel(), arrays):\n", + " image = axis.imshow(array, origin=\"lower\", aspect=\"auto\", cmap=cmap, vmin=vmin, vmax=vmax)\n", + " axis.set(title=title, xlabel=\"I column\", ylabel=\"J row\")\n", + " blocking_figure.colorbar(image, ax=axis, shrink=0.82)\n", + "blocking_figure.suptitle(f\"Blocking audit for simulation layer {layer_index + 1}\", fontsize=15)\n", + "path = OUTPUT_DIRECTORY / \"reek_blocking_comparison.png\"\n", + "blocking_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "399b1e7d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:23.125226Z", + "iopub.status.busy": "2026-08-31T17:04:23.124998Z", + "iopub.status.idle": "2026-08-31T17:04:25.093781Z", + "shell.execute_reply": "2026-08-31T17:04:25.092866Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spread_figure, axes = plt.subplots(2, 3, figsize=(15.0, 8.5), constrained_layout=True)\n", + "selected_layers = [1, sim_grid.nlay // 2]\n", + "facies_colors = \"viridis\"\n", + "for row, k_index in enumerate(selected_layers):\n", + " facies_image = axes[row, 0].imshow(sim_facies_values[:, :, k_index].T, origin=\"lower\",\n", + " aspect=\"auto\", cmap=facies_colors, vmin=0, vmax=2)\n", + " axes[row, 0].set_title(f\"Facies, layer {k_index + 1}\")\n", + " poro_image = axes[row, 1].imshow(sim_poro_values[:, :, k_index].T, origin=\"lower\",\n", + " aspect=\"auto\", cmap=\"YlGnBu\", vmin=0.0, vmax=0.4)\n", + " axes[row, 1].set_title(f\"PORO, layer {k_index + 1}\")\n", + " perm_image = axes[row, 2].imshow(\n", + " np.log10(np.ma.filled(sim_permx.values[:, :, k_index], np.nan)).T,\n", + " origin=\"lower\", aspect=\"auto\", cmap=\"magma\", vmin=-1, vmax=4,\n", + " )\n", + " axes[row, 2].set_title(f\"log10(PERMX/mD), layer {k_index + 1}\")\n", + " for axis in axes[row]:\n", + " axis.set(xlabel=\"I column\", ylabel=\"J row\")\n", + "spread_figure.colorbar(facies_image, ax=axes[:, 0], label=\"Facies code\", shrink=0.85)\n", + "spread_figure.colorbar(poro_image, ax=axes[:, 1], label=\"Porosity [-]\", shrink=0.85)\n", + "spread_figure.colorbar(perm_image, ax=axes[:, 2], label=\"log10(mD)\", shrink=0.85)\n", + "path = OUTPUT_DIRECTORY / \"reek_property_spreading_layers.png\"\n", + "spread_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5085a236", + "metadata": {}, + "source": [ + "## 4. Screen an injector–producer pair\n", + "\n", + "A transparent screening score is used only to place demonstration wells. It combines column\n", + "hydrocarbon pore volume with a logarithmic permeability-thickness proxy. The producer uses the\n", + "best column. The injector is selected among high-score columns 8–14 Manhattan grid steps away,\n", + "which makes the displacement visible without claiming an optimized field-development plan.\n", + "Only active layers are completed." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6bdb7c62", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:25.095650Z", + "iopub.status.busy": "2026-08-31T17:04:25.095473Z", + "iopub.status.idle": "2026-08-31T17:04:25.119018Z", + "shell.execute_reply": "2026-08-31T17:04:25.118290Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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wellIJfirst Klast Kactive completionscolumn HCPV [million rm3]score
0PROD2538114140.2923581725.417
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" + ], + "text/plain": [ + " well I J first K last K active completions column HCPV [million rm3] score\n", + "0 PROD 25 38 1 14 14 0.292 3581725.417\n", + "1 WINJ 19 34 1 14 14 0.244 2971149.240" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sim_bulk_volume = np.ma.filled(sim_grid.get_bulk_volume().values, 0.0)\n", + "sim_perm_values = np.ma.filled(sim_permx.values, 0.0)\n", + "INITIAL_WATER_SATURATION = 0.18\n", + "column_hcpv = np.sum(sim_bulk_volume * sim_poro_values * (1.0 - INITIAL_WATER_SATURATION), axis=2)\n", + "column_kh = np.sum(sim_perm_values * np.ma.filled(sim_dz.values, 0.0), axis=2)\n", + "screening_score = column_hcpv * np.log1p(column_kh)\n", + "screening_score[~np.any(sim_actnum, axis=2)] = -np.inf\n", + "\n", + "producer_flat = int(np.nanargmax(screening_score))\n", + "producer_i, producer_j = np.unravel_index(producer_flat, screening_score.shape)\n", + "\n", + "candidate_order = np.argsort(screening_score.ravel())[::-1]\n", + "injector_i = injector_j = None\n", + "for flat_index in candidate_order:\n", + " i_index, j_index = np.unravel_index(int(flat_index), screening_score.shape)\n", + " distance = abs(i_index - producer_i) + abs(j_index - producer_j)\n", + " if 8 <= distance <= 14 and np.isfinite(screening_score[i_index, j_index]):\n", + " injector_i, injector_j = i_index, j_index\n", + " break\n", + "if injector_i is None:\n", + " raise RuntimeError(\"No suitable injector column was found.\")\n", + "\n", + "producer_layers = np.flatnonzero(sim_actnum[producer_i, producer_j, :]) + 1\n", + "injector_layers = np.flatnonzero(sim_actnum[injector_i, injector_j, :]) + 1\n", + "\n", + "well_table = pd.DataFrame([\n", + " {\n", + " \"well\": \"PROD\", \"I\": producer_i + 1, \"J\": producer_j + 1,\n", + " \"first K\": int(producer_layers.min()), \"last K\": int(producer_layers.max()),\n", + " \"active completions\": len(producer_layers),\n", + " \"column HCPV [million rm3]\": column_hcpv[producer_i, producer_j] / 1e6,\n", + " \"score\": screening_score[producer_i, producer_j],\n", + " },\n", + " {\n", + " \"well\": \"WINJ\", \"I\": injector_i + 1, \"J\": injector_j + 1,\n", + " \"first K\": int(injector_layers.min()), \"last K\": int(injector_layers.max()),\n", + " \"active completions\": len(injector_layers),\n", + " \"column HCPV [million rm3]\": column_hcpv[injector_i, injector_j] / 1e6,\n", + " \"score\": screening_score[injector_i, injector_j],\n", + " },\n", + "])\n", + "display(well_table.round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a08c486b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:25.120748Z", + "iopub.status.busy": "2026-08-31T17:04:25.120566Z", + "iopub.status.idle": "2026-08-31T17:04:25.777293Z", + "shell.execute_reply": "2026-08-31T17:04:25.776449Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "well_figure, axis = plt.subplots(figsize=(10.5, 7.0), constrained_layout=True)\n", + "image = axis.imshow((column_hcpv / 1e6).T, origin=\"lower\", aspect=\"auto\", cmap=\"viridis\")\n", + "axis.scatter(producer_i, producer_j, marker=\"v\", s=180, color=\"#D55E00\",\n", + " edgecolor=\"white\", linewidth=1.3, label=\"PROD\")\n", + "axis.scatter(injector_i, injector_j, marker=\"^\", s=180, color=\"#0072B2\",\n", + " edgecolor=\"white\", linewidth=1.3, label=\"WINJ\")\n", + "axis.plot([producer_i, injector_i], [producer_j, injector_j], color=\"white\", linestyle=\"--\", linewidth=1.8)\n", + "axis.set(xlabel=\"I column\", ylabel=\"J row\", title=\"Hydrocarbon pore-volume screen and selected wells\")\n", + "axis.legend()\n", + "well_figure.colorbar(image, ax=axis, label=\"Column HCPV [million reservoir m3]\")\n", + "path = OUTPUT_DIRECTORY / \"reek_well_screening.png\"\n", + "well_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6238a7bb", + "metadata": {}, + "source": [ + "## 5. Generate black-oil PVT with NeqSim\n", + "\n", + "The composition below is fully visible. NeqSim characterizes the C20+ fraction into twelve\n", + "pseudo-components, runs a differential-liberation calculation, derives gas and water\n", + "properties, and emits strict Flow tables. The PVT input and every generated table row are\n", + "printed, not hidden behind a pre-built include file." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "63acf2a8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:25.779133Z", + "iopub.status.busy": "2026-08-31T17:04:25.778917Z", + "iopub.status.idle": "2026-08-31T17:04:26.932452Z", + "shell.execute_reply": "2026-08-31T17:04:26.928873Z" + } + }, + "outputs": [], + "source": [ + "LIGHT_COMPONENTS = [\n", + " (\"nitrogen\", 0.39),\n", + " (\"CO2\", 0.30),\n", + " (\"methane\", 40.20),\n", + " (\"ethane\", 7.61),\n", + " (\"propane\", 7.95),\n", + " (\"i-butane\", 1.19),\n", + " (\"n-butane\", 4.08),\n", + " (\"i-pentane\", 1.39),\n", + " (\"n-pentane\", 2.15),\n", + " (\"n-hexane\", 2.79),\n", + "]\n", + "\n", + "TBP_CUTS = [\n", + " (\"C7\", 4.28, 0.095, 0.729),\n", + " (\"C8\", 4.31, 0.106, 0.749),\n", + " (\"C9\", 3.08, 0.121, 0.770),\n", + " (\"C10\", 2.47, 0.135, 0.786),\n", + " (\"C11\", 1.91, 0.148, 0.792),\n", + " (\"C12\", 1.69, 0.161, 0.804),\n", + " (\"C13\", 1.59, 0.175, 0.819),\n", + " (\"C14\", 1.22, 0.196, 0.833),\n", + " (\"C15\", 1.25, 0.206, 0.836),\n", + " (\"C16\", 1.00, 0.225, 0.843),\n", + " (\"C17\", 0.99, 0.236, 0.840),\n", + " (\"C18\", 0.92, 0.245, 0.846),\n", + " (\"C19\", 0.60, 0.265, 0.857),\n", + "]\n", + "\n", + "PLUS_FRACTION = (\"C20\", 6.64, 0.453, 0.918)\n", + "\n", + "\n", + "def build_reservoir_fluid():\n", + " fluid_model = jneqsim.thermo.system.SystemSrkEos(\n", + " RESERVOIR_TEMPERATURE_K,\n", + " INITIAL_RESERVOIR_PRESSURE_BARA,\n", + " )\n", + "\n", + " for component_name, amount_mol_percent in LIGHT_COMPONENTS:\n", + " fluid_model.addComponent(component_name, amount_mol_percent)\n", + "\n", + " for cut_name, amount, molar_mass, relative_density in TBP_CUTS:\n", + " fluid_model.addTBPfraction(\n", + " cut_name,\n", + " amount,\n", + " molar_mass,\n", + " relative_density,\n", + " )\n", + "\n", + " plus_name, plus_amount, plus_molar_mass, plus_density = PLUS_FRACTION\n", + " fluid_model.addPlusFraction(\n", + " plus_name,\n", + " plus_amount,\n", + " plus_molar_mass,\n", + " plus_density,\n", + " )\n", + "\n", + " characterization = fluid_model.getCharacterization()\n", + " characterization.getLumpingModel().setNumberOfPseudoComponents(12)\n", + " characterization.characterisePlusFraction()\n", + "\n", + " fluid_model.setMixingRule(\"classic\")\n", + " fluid_model.useVolumeCorrection(True)\n", + " fluid_model.init(0)\n", + " fluid_model.init(1)\n", + " return fluid_model\n", + "\n", + "\n", + "reservoir_fluid = build_reservoir_fluid()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8c58c98f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:26.938179Z", + "iopub.status.busy": "2026-08-31T17:04:26.937887Z", + "iopub.status.idle": "2026-08-31T17:04:27.837523Z", + "shell.execute_reply": "2026-08-31T17:04:27.836666Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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quantityvalueunit
0input assay closure100.000000mol%
1characterized components22.000000count
2characterized composition sum1.000000mol/mol
3initial phase count1.000000count
4bubble pressure193.240934bara
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" + ], + "text/plain": [ + " quantity value unit\n", + "0 input assay closure 100.000000 mol%\n", + "1 characterized components 22.000000 count\n", + "2 characterized composition sum 1.000000 mol/mol\n", + "3 initial phase count 1.000000 count\n", + "4 bubble pressure 193.240934 bara" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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componentmole_fractionmolar_mass_g_mol
0nitrogen0.00390028.013500
1CO20.00300044.010000
2methane0.40200016.043000
3ethane0.07610030.070000
4propane0.07950044.097000
5i-butane0.01190058.123000
6n-butane0.04080058.123000
7i-pentane0.01390072.151000
8n-pentane0.02150072.150000
9n-hexane0.02790086.177000
10PC1_PC0.085900100.519208
11PC2_PC0.055500127.230631
12PC3_PC0.036000154.102778
13PC4_PC0.028100184.117438
14PC5_PC0.032400221.030864
15PC6_PC0.019866258.086523
16PC7_PC0.018903316.078814
17PC8_PC0.015315391.877654
18PC9_PC0.011232481.993874
19PC10_PC0.008808596.773111
20PC11_PC0.006387782.655879
21PC12_PC0.0010881022.045700
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" + ], + "text/plain": [ + " component mole_fraction molar_mass_g_mol\n", + "0 nitrogen 0.003900 28.013500\n", + "1 CO2 0.003000 44.010000\n", + "2 methane 0.402000 16.043000\n", + "3 ethane 0.076100 30.070000\n", + "4 propane 0.079500 44.097000\n", + "5 i-butane 0.011900 58.123000\n", + "6 n-butane 0.040800 58.123000\n", + "7 i-pentane 0.013900 72.151000\n", + "8 n-pentane 0.021500 72.150000\n", + "9 n-hexane 0.027900 86.177000\n", + "10 PC1_PC 0.085900 100.519208\n", + "11 PC2_PC 0.055500 127.230631\n", + "12 PC3_PC 0.036000 154.102778\n", + "13 PC4_PC 0.028100 184.117438\n", + "14 PC5_PC 0.032400 221.030864\n", + "15 PC6_PC 0.019866 258.086523\n", + "16 PC7_PC 0.018903 316.078814\n", + "17 PC8_PC 0.015315 391.877654\n", + "18 PC9_PC 0.011232 481.993874\n", + "19 PC10_PC 0.008808 596.773111\n", + "20 PC11_PC 0.006387 782.655879\n", + "21 PC12_PC 0.001088 1022.045700" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "characterized_rows = []\n", + "for component_index in range(reservoir_fluid.getNumberOfComponents()):\n", + " component = reservoir_fluid.getComponent(component_index)\n", + " characterized_rows.append(\n", + " {\n", + " \"component\": str(component.getComponentName()),\n", + " \"mole_fraction\": float(component.getz()),\n", + " \"molar_mass_g_mol\": 1000.0 * float(component.getMolarMass()),\n", + " }\n", + " )\n", + "\n", + "characterized_composition = pd.DataFrame(characterized_rows)\n", + "composition_sum = characterized_composition[\"mole_fraction\"].sum()\n", + "\n", + "initial_state = reservoir_fluid.clone()\n", + "initial_state.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + "initial_state.setPressure(INITIAL_RESERVOIR_PRESSURE_BARA, \"bara\")\n", + "TPflash(initial_state)\n", + "initial_state.initPhysicalProperties()\n", + "\n", + "SaturationPressure = jneqsim.pvtsimulation.simulation.SaturationPressure\n", + "saturation_calculation = SaturationPressure(reservoir_fluid.clone())\n", + "saturation_calculation.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + "saturation_calculation.run()\n", + "bubble_pressure_reference_bara = (\n", + " saturation_calculation.getSaturationPressure()\n", + ")\n", + "\n", + "fluid_audit = pd.DataFrame(\n", + " {\n", + " \"quantity\": [\n", + " \"input assay closure\",\n", + " \"characterized components\",\n", + " \"characterized composition sum\",\n", + " \"initial phase count\",\n", + " \"bubble pressure\",\n", + " ],\n", + " \"value\": [\n", + " sum(amount for _, amount in LIGHT_COMPONENTS)\n", + " + sum(row[1] for row in TBP_CUTS)\n", + " + PLUS_FRACTION[1],\n", + " reservoir_fluid.getNumberOfComponents(),\n", + " composition_sum,\n", + " initial_state.getNumberOfPhases(),\n", + " bubble_pressure_reference_bara,\n", + " ],\n", + " \"unit\": [\"mol%\", \"count\", \"mol/mol\", \"count\", \"bara\"],\n", + " }\n", + ")\n", + "display(fluid_audit)\n", + "display(characterized_composition)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "13fd2899", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:27.839802Z", + "iopub.status.busy": "2026-08-31T17:04:27.839394Z", + "iopub.status.idle": "2026-08-31T17:04:28.422447Z", + "shell.execute_reply": "2026-08-31T17:04:28.421253Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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pressure_baraRs_Sm3_Sm3Bo_rm3_Sm3oil_density_kg_m3oil_viscosity_cPDLE_Bg_rm3_Sm3gas_viscosity_cP
0300.00000224.6727101.764295669.0304340.2510130.0000000.021248
1250.00000224.6727101.789529659.5962130.2359800.0000000.021248
2220.00000224.6727101.806786653.2963980.2265020.0000000.021248
3200.00000224.6727101.819362648.7806480.2199670.0000000.021248
4180.00000209.0182451.779299654.8300150.2303650.0063010.020399
5150.00000176.5261561.687488672.1511680.2630800.0075620.018454
6100.00000128.9712461.552728701.6268000.3365580.0115330.015971
750.0000086.2775671.427528733.1481540.4619010.0238730.014126
81.013250.0000001.050859810.4245781.9552261.2716520.011572
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" + ], + "text/plain": [ + " pressure_bara Rs_Sm3_Sm3 Bo_rm3_Sm3 oil_density_kg_m3 oil_viscosity_cP DLE_Bg_rm3_Sm3 gas_viscosity_cP\n", + "0 300.00000 224.672710 1.764295 669.030434 0.251013 0.000000 0.021248\n", + "1 250.00000 224.672710 1.789529 659.596213 0.235980 0.000000 0.021248\n", + "2 220.00000 224.672710 1.806786 653.296398 0.226502 0.000000 0.021248\n", + "3 200.00000 224.672710 1.819362 648.780648 0.219967 0.000000 0.021248\n", + "4 180.00000 209.018245 1.779299 654.830015 0.230365 0.006301 0.020399\n", + "5 150.00000 176.526156 1.687488 672.151168 0.263080 0.007562 0.018454\n", + "6 100.00000 128.971246 1.552728 701.626800 0.336558 0.011533 0.015971\n", + "7 50.00000 86.277567 1.427528 733.148154 0.461901 0.023873 0.014126\n", + "8 1.01325 0.000000 1.050859 810.424578 1.955226 1.271652 0.011572" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DLE bubble pressure: 193.240400 bara\n" + ] + } + ], + "source": [ + "DifferentialLiberation = (\n", + " jneqsim.pvtsimulation.simulation.DifferentialLiberation\n", + ")\n", + "dle = DifferentialLiberation(reservoir_fluid.clone())\n", + "dle.setPressures(DLE_PRESSURES_BARA.tolist())\n", + "dle.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + "dle.runCalc()\n", + "\n", + "bubble_pressure_bara = float(dle.getSaturationPressure())\n", + "dle_rs = np.asarray(dle.getRs(), dtype=float)\n", + "dle_bo = np.asarray(dle.getBo(), dtype=float)\n", + "dle_bg = np.asarray(dle.getBg(), dtype=float)\n", + "dle_oil_density_kg_m3 = np.asarray(dle.getOilDensity(), dtype=float)\n", + "\n", + "saturation_gas_state = reservoir_fluid.clone()\n", + "saturation_gas_state.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + "saturation_gas_state.setPressure(bubble_pressure_bara * 0.99, \"bara\")\n", + "TPflash(saturation_gas_state)\n", + "saturation_gas_state.initPhysicalProperties()\n", + "saturation_gas_viscosity_pa_s = (\n", + " saturation_gas_state.getPhase(\"gas\").getViscosity(\"cP\") / 1000.0\n", + ")\n", + "\n", + "black_oil_rows = []\n", + "for pressure_bara, rs_value, bo_value, bg_value, oil_density in zip(\n", + " DLE_PRESSURES_BARA,\n", + " dle_rs,\n", + " dle_bo,\n", + " dle_bg,\n", + " dle_oil_density_kg_m3,\n", + "):\n", + " property_state = reservoir_fluid.clone()\n", + " property_state.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + " property_state.setPressure(float(pressure_bara), \"bara\")\n", + " TPflash(property_state)\n", + " property_state.initPhysicalProperties()\n", + "\n", + " oil_viscosity_pa_s = (\n", + " property_state.getPhase(\"oil\").getViscosity(\"cP\") / 1000.0\n", + " )\n", + " if property_state.hasPhaseType(\"gas\"):\n", + " gas_viscosity_pa_s = (\n", + " property_state.getPhase(\"gas\").getViscosity(\"cP\") / 1000.0\n", + " )\n", + " else:\n", + " gas_viscosity_pa_s = saturation_gas_viscosity_pa_s\n", + "\n", + " black_oil_rows.append(\n", + " {\n", + " \"pressure_bara\": float(pressure_bara),\n", + " \"Rs_Sm3_Sm3\": float(rs_value),\n", + " \"Bo_rm3_Sm3\": float(bo_value),\n", + " \"oil_density_kg_m3\": float(oil_density),\n", + " \"oil_viscosity_cP\": 1000.0 * float(oil_viscosity_pa_s),\n", + " \"DLE_Bg_rm3_Sm3\": float(bg_value),\n", + " \"gas_viscosity_cP\": 1000.0 * float(gas_viscosity_pa_s),\n", + " }\n", + " )\n", + "\n", + "black_oil_source_table = pd.DataFrame(black_oil_rows)\n", + "display(black_oil_source_table)\n", + "print(f\"DLE bubble pressure: {bubble_pressure_bara:.6f} bara\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a2c117d6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:28.424648Z", + "iopub.status.busy": "2026-08-31T17:04:28.424411Z", + "iopub.status.idle": "2026-08-31T17:04:29.520470Z", + "shell.execute_reply": "2026-08-31T17:04:29.519617Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pvt_figure, pvt_axes = plt.subplots(\n", + " 1,\n", + " 3,\n", + " figsize=(13.0, 4.0),\n", + " constrained_layout=True,\n", + ")\n", + "\n", + "pvt_axes[0].plot(\n", + " black_oil_source_table[\"pressure_bara\"],\n", + " black_oil_source_table[\"Rs_Sm3_Sm3\"],\n", + " marker=\"o\",\n", + ")\n", + "pvt_axes[0].axvline(\n", + " bubble_pressure_bara,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + " label=\"bubble pressure\",\n", + ")\n", + "pvt_axes[0].set_xlabel(\"Pressure [bara]\")\n", + "pvt_axes[0].set_ylabel(\"$R_s$ [Sm³/Sm³]\")\n", + "pvt_axes[0].set_title(\"Solution gas\")\n", + "pvt_axes[0].legend()\n", + "\n", + "pvt_axes[1].plot(\n", + " black_oil_source_table[\"pressure_bara\"],\n", + " black_oil_source_table[\"Bo_rm3_Sm3\"],\n", + " marker=\"o\",\n", + " color=\"#009E73\",\n", + ")\n", + "pvt_axes[1].axvline(\n", + " bubble_pressure_bara,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + ")\n", + "pvt_axes[1].set_xlabel(\"Pressure [bara]\")\n", + "pvt_axes[1].set_ylabel(\"$B_o$ [rm³/Sm³]\")\n", + "pvt_axes[1].set_title(\"Oil volume factor\")\n", + "\n", + "pvt_axes[2].plot(\n", + " black_oil_source_table[\"pressure_bara\"],\n", + " black_oil_source_table[\"oil_viscosity_cP\"],\n", + " marker=\"o\",\n", + " color=\"#D55E00\",\n", + ")\n", + "pvt_axes[2].axvline(\n", + " bubble_pressure_bara,\n", + " color=\"black\",\n", + " linestyle=\"--\",\n", + ")\n", + "pvt_axes[2].set_xlabel(\"Pressure [bara]\")\n", + "pvt_axes[2].set_ylabel(\"Oil viscosity [cP]\")\n", + "pvt_axes[2].set_title(\"Oil viscosity\")\n", + "\n", + "pvt_plot_path = OUTPUT_DIRECTORY / \"neqsim_black_oil_pvt.png\"\n", + "pvt_figure.savefig(pvt_plot_path, dpi=150)\n", + "plt.show()\n", + "FIGURE_PATHS.append(pvt_plot_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ea8347b8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:29.522669Z", + "iopub.status.busy": "2026-08-31T17:04:29.522490Z", + "iopub.status.idle": "2026-08-31T17:04:29.552725Z", + "shell.execute_reply": "2026-08-31T17:04:29.550028Z" + } + }, + "outputs": [], + "source": [ + "stock_tank_state = reservoir_fluid.clone()\n", + "stock_tank_state.setTemperature(STANDARD_TEMPERATURE_C, \"C\")\n", + "stock_tank_state.setPressure(STANDARD_PRESSURE_BARA, \"bara\")\n", + "TPflash(stock_tank_state)\n", + "stock_tank_state.initPhysicalProperties()\n", + "\n", + "stock_oil_density_kg_m3 = (\n", + " stock_tank_state.getPhase(\"oil\").getDensity(\"kg/m3\")\n", + ")\n", + "stock_gas_density_kg_m3 = (\n", + " stock_tank_state.getPhase(\"gas\").getDensity(\"kg/m3\")\n", + ")\n", + "\n", + "ArrayList = JClass(\"java.util.ArrayList\")\n", + "BlackOilPVTTable = JClass(\"neqsim.blackoil.BlackOilPVTTable\")\n", + "black_oil_records = ArrayList()\n", + "for row in black_oil_source_table.sort_values(\n", + " \"pressure_bara\"\n", + ").to_dict(\"records\"):\n", + " black_oil_records.add(\n", + " BlackOilPVTTable.Record(\n", + " row[\"pressure_bara\"],\n", + " row[\"Rs_Sm3_Sm3\"],\n", + " row[\"Bo_rm3_Sm3\"],\n", + " row[\"oil_viscosity_cP\"] / 1000.0,\n", + " max(row[\"DLE_Bg_rm3_Sm3\"], 1.0e-6),\n", + " row[\"gas_viscosity_cP\"] / 1000.0,\n", + " 0.0,\n", + " 1.0,\n", + " 1.0e-3,\n", + " )\n", + " )\n", + "\n", + "black_oil_table = BlackOilPVTTable(\n", + " black_oil_records,\n", + " bubble_pressure_bara,\n", + ")\n", + "\n", + "bubble_bo = float(\n", + " np.interp(\n", + " bubble_pressure_bara,\n", + " np.sort(DLE_PRESSURES_BARA),\n", + " dle_bo[np.argsort(DLE_PRESSURES_BARA)],\n", + " )\n", + ")\n", + "bubble_state = reservoir_fluid.clone()\n", + "bubble_state.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + "bubble_state.setPressure(bubble_pressure_bara, \"bara\")\n", + "TPflash(bubble_state)\n", + "bubble_state.initPhysicalProperties()\n", + "bubble_oil_viscosity_cp = (\n", + " bubble_state.getPhase(\"oil\").getViscosity(\"cP\")\n", + ")\n", + "maximum_solution_gor = float(np.max(dle_rs))\n", + "\n", + "saturated_oil_rows = []\n", + "for row in black_oil_source_table.sort_values(\n", + " \"pressure_bara\"\n", + ").to_dict(\"records\"):\n", + " if row[\"pressure_bara\"] >= bubble_pressure_bara:\n", + " continue\n", + " if (\n", + " saturated_oil_rows\n", + " and row[\"Rs_Sm3_Sm3\"] <= saturated_oil_rows[-1][\"Rs\"]\n", + " ):\n", + " continue\n", + " saturated_oil_rows.append(\n", + " {\n", + " \"Rs\": max(row[\"Rs_Sm3_Sm3\"], 1.0e-6),\n", + " \"pressure\": row[\"pressure_bara\"],\n", + " \"Bo\": row[\"Bo_rm3_Sm3\"],\n", + " \"mu_o\": row[\"oil_viscosity_cP\"],\n", + " }\n", + " )\n", + "\n", + "saturated_oil_rows.append(\n", + " {\n", + " \"Rs\": maximum_solution_gor,\n", + " \"pressure\": bubble_pressure_bara,\n", + " \"Bo\": bubble_bo,\n", + " \"mu_o\": bubble_oil_viscosity_cp,\n", + " }\n", + ")\n", + "\n", + "maximum_pressure_row = black_oil_source_table.loc[\n", + " black_oil_source_table[\"pressure_bara\"].idxmax()\n", + "]\n", + "undersaturated_oil_row = {\n", + " \"pressure\": float(maximum_pressure_row[\"pressure_bara\"]),\n", + " \"Bo\": float(maximum_pressure_row[\"Bo_rm3_Sm3\"]),\n", + " \"mu_o\": float(maximum_pressure_row[\"oil_viscosity_cP\"]),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9febcdc5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:29.555949Z", + "iopub.status.busy": "2026-08-31T17:04:29.555417Z", + "iopub.status.idle": "2026-08-31T17:04:29.701463Z", + "shell.execute_reply": "2026-08-31T17:04:29.700531Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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pressure_baraBg_rm3_Sm3gas_viscosity_cP
01.013251.2906460.012544
125.000000.0492620.013143
250.000000.0231450.014019
375.000000.0145720.015213
4100.000000.0104460.016778
5125.000000.0081340.018700
6150.000000.0067330.020876
7175.000000.0058310.023164
8200.000000.0052160.025456
9225.000000.0047760.027697
10250.000000.0044460.029862
11275.000000.0041890.031947
12300.000000.0039840.033952
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" + ], + "text/plain": [ + " pressure_bara Bg_rm3_Sm3 gas_viscosity_cP\n", + "0 1.01325 1.290646 0.012544\n", + "1 25.00000 0.049262 0.013143\n", + "2 50.00000 0.023145 0.014019\n", + "3 75.00000 0.014572 0.015213\n", + "4 100.00000 0.010446 0.016778\n", + "5 125.00000 0.008134 0.018700\n", + "6 150.00000 0.006733 0.020876\n", + "7 175.00000 0.005831 0.023164\n", + "8 200.00000 0.005216 0.025456\n", + "9 225.00000 0.004776 0.027697\n", + "10 250.00000 0.004446 0.029862\n", + "11 275.00000 0.004189 0.031947\n", + "12 300.00000 0.003984 0.033952" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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water_propertyvalueunit
0stock-tank density997.784768kg/m³
1reservoir density978.587636kg/m³
2formation-volume factor1.019617rm³/Sm³
3compressibility0.0000501/bar
4viscosity0.296512cP
\n", + "
" + ], + "text/plain": [ + " water_property value unit\n", + "0 stock-tank density 997.784768 kg/m³\n", + "1 reservoir density 978.587636 kg/m³\n", + "2 formation-volume factor 1.019617 rm³/Sm³\n", + "3 compressibility 0.000050 1/bar\n", + "4 viscosity 0.296512 cP" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stock_gas_template = stock_tank_state.phaseToSystem(\"gas\")\n", + "dry_gas_rows = []\n", + "for pressure_bara in GAS_PVT_PRESSURES_BARA:\n", + " gas_state = stock_gas_template.clone()\n", + " gas_state.setTemperature(RESERVOIR_TEMPERATURE_C, \"C\")\n", + " gas_state.setPressure(float(pressure_bara), \"bara\")\n", + " TPflash(gas_state)\n", + " gas_state.initPhysicalProperties()\n", + " gas_density_kg_m3 = gas_state.getPhase(\"gas\").getDensity(\"kg/m3\")\n", + " dry_gas_rows.append(\n", + " {\n", + " \"pressure_bara\": float(pressure_bara),\n", + " \"Bg_rm3_Sm3\": (\n", + " stock_gas_density_kg_m3 / gas_density_kg_m3\n", + " ),\n", + " \"gas_viscosity_cP\": (\n", + " gas_state.getPhase(\"gas\").getViscosity(\"cP\")\n", + " ),\n", + " }\n", + " )\n", + "\n", + "dry_gas_table = pd.DataFrame(dry_gas_rows)\n", + "\n", + "\n", + "def build_water_state(temperature_c, pressure_bara):\n", + " water_state = jneqsim.thermo.system.SystemSrkCPAstatoil(\n", + " temperature_c + 273.15,\n", + " pressure_bara,\n", + " )\n", + " water_state.addComponent(\"water\", 1.0)\n", + " water_state.setMixingRule(10)\n", + " TPflash(water_state)\n", + " water_state.initPhysicalProperties()\n", + " return water_state\n", + "\n", + "\n", + "standard_water_state = build_water_state(\n", + " STANDARD_TEMPERATURE_C,\n", + " STANDARD_PRESSURE_BARA,\n", + ")\n", + "reference_water_state = build_water_state(\n", + " RESERVOIR_TEMPERATURE_C,\n", + " bubble_pressure_bara,\n", + ")\n", + "water_low_pressure_state = build_water_state(\n", + " RESERVOIR_TEMPERATURE_C,\n", + " bubble_pressure_bara - 1.0,\n", + ")\n", + "water_high_pressure_state = build_water_state(\n", + " RESERVOIR_TEMPERATURE_C,\n", + " bubble_pressure_bara + 1.0,\n", + ")\n", + "\n", + "stock_water_density_kg_m3 = (\n", + " standard_water_state.getPhase(\"aqueous\").getDensity(\"kg/m3\")\n", + ")\n", + "reservoir_water_density_kg_m3 = (\n", + " reference_water_state.getPhase(\"aqueous\").getDensity(\"kg/m3\")\n", + ")\n", + "water_fvf = (\n", + " stock_water_density_kg_m3 / reservoir_water_density_kg_m3\n", + ")\n", + "water_compressibility_per_bar = (\n", + " water_high_pressure_state.getPhase(\"aqueous\").getDensity(\"kg/m3\")\n", + " - water_low_pressure_state.getPhase(\"aqueous\").getDensity(\"kg/m3\")\n", + ") / (2.0 * reservoir_water_density_kg_m3)\n", + "water_viscosity_cp = (\n", + " reference_water_state.getPhase(\"aqueous\").getViscosity(\"cP\")\n", + ")\n", + "\n", + "display(dry_gas_table)\n", + "display(\n", + " pd.DataFrame(\n", + " {\n", + " \"water_property\": [\n", + " \"stock-tank density\",\n", + " \"reservoir density\",\n", + " \"formation-volume factor\",\n", + " \"compressibility\",\n", + " \"viscosity\",\n", + " ],\n", + " \"value\": [\n", + " stock_water_density_kg_m3,\n", + " reservoir_water_density_kg_m3,\n", + " water_fvf,\n", + " water_compressibility_per_bar,\n", + " water_viscosity_cp,\n", + " ],\n", + " \"unit\": [\"kg/m³\", \"kg/m³\", \"rm³/Sm³\", \"1/bar\", \"cP\"],\n", + " }\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9c74e905", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:29.703975Z", + "iopub.status.busy": "2026-08-31T17:04:29.703740Z", + "iopub.status.idle": "2026-08-31T17:04:29.722451Z", + "shell.execute_reply": "2026-08-31T17:04:29.721551Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RspressureBomu_o
00.0000011.013251.0508591.955226
186.27756750.000001.4275280.461901
2128.971246100.000001.5527280.336558
3176.526156150.000001.6874880.263080
4209.018245180.000001.7792990.230365
5224.672710193.240401.8058220.217716
\n", + "
" + ], + "text/plain": [ + " Rs pressure Bo mu_o\n", + "0 0.000001 1.01325 1.050859 1.955226\n", + "1 86.277567 50.00000 1.427528 0.461901\n", + "2 128.971246 100.00000 1.552728 0.336558\n", + "3 176.526156 150.00000 1.687488 0.263080\n", + "4 209.018245 180.00000 1.779299 0.230365\n", + "5 224.672710 193.24040 1.805822 0.217716" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Flow-compatible black-oil tables calculated with NeqSim\n", + "-- Pressure is absolute and the table unit system is METRIC.\n", + "DENSITY\n", + " 832.46987287 997.78476781 1.12375584 /\n", + "\n", + "PVTO\n", + " 0.00000100 1.01325000 1.05085900 1.95522636 /\n", + " 86.27756659 50.00000000 1.42752753 0.46190091 /\n", + " 128.97124554 100.00000000 1.55272761 0.33655787 /\n", + " 176.52615626 150.00000000 1.68748770 0.26307966 /\n", + " 209.01824465 180.00000000 1.77929947 0.23036455 /\n", + " 224.67271009 193.24040031 1.80582153 0.21771608\n", + " 300.00000000 1.76429452 0.25101335 /\n", + "/\n", + "\n", + "PVDG\n", + " 1.01325000 1.2906458622 0.01254417\n", + " 25.00000000 0.0492622652 0.01314293\n", + " 50.00000000 0.0231449092 0.01401858\n", + " 75.00000000 0.0145720509 0.01521278\n", + " 100.00000000 0.0104455093 0.01677753\n", + " 125.00000000 0.0081343925 0.01870015\n", + " 150.00000000 0.0067329734 0.02087585\n", + " 175.00000000 0.0058305684 0.02316351\n", + " 200.00000000 0.0052160944 0.02545613\n", + " 225.00000000 0.0047757014 0.02769676\n", + " 250.00000000 0.0044458169 0.02986229\n", + " 275.00000000 0.0041894725 0.03194685\n", + " 300.00000000 0.0039841961 0.03395217 /\n", + "\n", + "PVTW\n", + " 193.24040031 1.01961718 4.9717407456e-05 0.29651245 0.0 /\n", + "Black-oil include: /home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/NEQSIM_PVT.INC\n", + "\n", + "Complete generated NEQSIM_PVT.INC:\n", + "\n", + "-- Flow-compatible black-oil tables calculated with NeqSim\n", + "-- Pressure is absolute and the table unit system is METRIC.\n", + "DENSITY\n", + " 832.46987287 997.78476781 1.12375584 /\n", + "\n", + "PVTO\n", + " 0.00000100 1.01325000 1.05085900 1.95522636 /\n", + " 86.27756659 50.00000000 1.42752753 0.46190091 /\n", + " 128.97124554 100.00000000 1.55272761 0.33655787 /\n", + " 176.52615626 150.00000000 1.68748770 0.26307966 /\n", + " 209.01824465 180.00000000 1.77929947 0.23036455 /\n", + " 224.67271009 193.24040031 1.80582153 0.21771608\n", + " 300.00000000 1.76429452 0.25101335 /\n", + "/\n", + "\n", + "PVDG\n", + " 1.01325000 1.2906458622 0.01254417\n", + " 25.00000000 0.0492622652 0.01314293\n", + " 50.00000000 0.0231449092 0.01401858\n", + " 75.00000000 0.0145720509 0.01521278\n", + " 100.00000000 0.0104455093 0.01677753\n", + " 125.00000000 0.0081343925 0.01870015\n", + " 150.00000000 0.0067329734 0.02087585\n", + " 175.00000000 0.0058305684 0.02316351\n", + " 200.00000000 0.0052160944 0.02545613\n", + " 225.00000000 0.0047757014 0.02769676\n", + " 250.00000000 0.0044458169 0.02986229\n", + " 275.00000000 0.0041894725 0.03194685\n", + " 300.00000000 0.0039841961 0.03395217 /\n", + "\n", + "PVTW\n", + " 193.24040031 1.01961718 4.9717407456e-05 0.29651245 0.0 /\n", + "\n" + ] + } + ], + "source": [ + "pvto_lines = [\"PVTO\"]\n", + "for row_index, row in enumerate(saturated_oil_rows):\n", + " is_last_saturated_row = row_index == len(saturated_oil_rows) - 1\n", + " row_suffix = \"\" if is_last_saturated_row else \" /\"\n", + " pvto_lines.append(\n", + " \" \"\n", + " f\"{row['Rs']:.8f} \"\n", + " f\"{row['pressure']:.8f} \"\n", + " f\"{row['Bo']:.8f} \"\n", + " f\"{row['mu_o']:.8f}\"\n", + " f\"{row_suffix}\"\n", + " )\n", + " if is_last_saturated_row:\n", + " pvto_lines.append(\n", + " \" \"\n", + " f\"{undersaturated_oil_row['pressure']:.8f} \"\n", + " f\"{undersaturated_oil_row['Bo']:.8f} \"\n", + " f\"{undersaturated_oil_row['mu_o']:.8f} /\"\n", + " )\n", + "pvto_lines.append(\"/\")\n", + "\n", + "pvdg_lines = [\"PVDG\"]\n", + "for row_index, row in dry_gas_table.iterrows():\n", + " row_suffix = \" /\" if row_index == len(dry_gas_table) - 1 else \"\"\n", + " pvdg_lines.append(\n", + " \" \"\n", + " f\"{row['pressure_bara']:.8f} \"\n", + " f\"{row['Bg_rm3_Sm3']:.10f} \"\n", + " f\"{row['gas_viscosity_cP']:.8f}\"\n", + " f\"{row_suffix}\"\n", + " )\n", + "\n", + "black_oil_include = \"\\n\".join(\n", + " [\n", + " \"-- Flow-compatible black-oil tables calculated with NeqSim\",\n", + " \"-- Pressure is absolute and the table unit system is METRIC.\",\n", + " \"DENSITY\",\n", + " \" \"\n", + " f\"{stock_oil_density_kg_m3:.8f} \"\n", + " f\"{stock_water_density_kg_m3:.8f} \"\n", + " f\"{stock_gas_density_kg_m3:.8f} /\",\n", + " \"\",\n", + " *pvto_lines,\n", + " \"\",\n", + " *pvdg_lines,\n", + " \"\",\n", + " \"PVTW\",\n", + " \" \"\n", + " f\"{bubble_pressure_bara:.8f} \"\n", + " f\"{water_fvf:.8f} \"\n", + " f\"{water_compressibility_per_bar:.10e} \"\n", + " f\"{water_viscosity_cp:.8f} \"\n", + " \"0.0 /\",\n", + " \"\",\n", + " ]\n", + ")\n", + "\n", + "black_oil_path = OUTPUT_DIRECTORY / \"NEQSIM_PVT.INC\"\n", + "black_oil_path.write_text(black_oil_include, encoding=\"utf-8\")\n", + "\n", + "oil_table_audit = pd.DataFrame(saturated_oil_rows)\n", + "display(oil_table_audit)\n", + "print(\"\\n\".join(black_oil_include.splitlines()[:42]))\n", + "print(f\"Black-oil include: {black_oil_path}\")\n", + "\n", + "print(\"\\nComplete generated NEQSIM_PVT.INC:\\n\")\n", + "print(black_oil_include)" + ] + }, + { + "cell_type": "markdown", + "id": "ba2487e3", + "metadata": {}, + "source": [ + "## 6. Export Flow grid and static properties\n", + "\n", + "The OPM model uses the supplied RMS simulation-scale geometry and properties. The exact modeling\n", + "assumptions are:\n", + "\n", + "- PORO and PERMX: supplied public simulation properties;\n", + "- PERMY = 0.70 × PERMX;\n", + "- PERMZ = 0.10 × PERMX;\n", + "- FIPNUM: supplied Zone property;\n", + "- initial saturation: equilibrium plus the SWOF endpoint;\n", + "- relative permeability: the complete tables printed below.\n", + "\n", + "Each GRDECL include is written by XTGeo and hashed. These generated files plus the immutable ROFF\n", + "sources make the complete numerical input reproducible without printing hundreds of thousands\n", + "of cell values into the browser." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b63134c2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:29.724895Z", + "iopub.status.busy": "2026-08-31T17:04:29.724663Z", + "iopub.status.idle": "2026-08-31T17:04:30.417218Z", + "shell.execute_reply": "2026-08-31T17:04:30.416254Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SWOF\n", + " 0.12 0.00000 1.0000 0.0\n", + " 0.20 0.00010 0.8500 0.0\n", + " 0.30 0.00100 0.6200 0.0\n", + " 0.40 0.00800 0.4000 0.0\n", + " 0.50 0.03000 0.2200 0.0\n", + " 0.60 0.09000 0.1000 0.0\n", + " 0.70 0.22000 0.0300 0.0\n", + " 0.80 0.48000 0.0050 0.0\n", + " 0.88 1.00000 0.0000 0.0 /\n", + "\n", + "SGOF\n", + " 0.00 0.0000 1.0000 0.0\n", + " 0.05 0.0020 0.9300 0.0\n", + " 0.10 0.0100 0.8000 0.0\n", + " 0.20 0.0600 0.5500 0.0\n", + " 0.30 0.1600 0.3200 0.0\n", + " 0.40 0.3300 0.1500 0.0\n", + " 0.50 0.5500 0.0500 0.0\n", + " 0.60 0.7800 0.0100 0.0\n", + " 0.70 0.9300 0.0010 0.0\n", + " 0.88 1.0000 0.0000 0.0 /\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " file bytes sha256\n", + "0 REEK_GRID.GRDECL 5576400 f52cc87ee49ae0f715e504910386c67593be7a301d36e8...\n", + "1 PORO.GRDECL 471901 bd6e51ace0e867d61c4f72504109bb679e2dfc5124454e...\n", + "2 PERMX.GRDECL 471902 885fb2e50c8de23b22efff0b5d27f356bb25a736de2b5f...\n", + "3 PERMY.GRDECL 471902 1b6ed1e344aaa150090c01c7f3e883c7005e201990f4b6...\n", + "4 PERMZ.GRDECL 471902 2948737c53ad15e1c93d632e1cd90f436dfe113ad35d10...\n", + "5 FIPNUM.GRDECL 77663 8de3e8fada8dc2e50075547bfb07e5c1dd2935018e0f41..." + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Grid include header:\n", + " SPECGRID\n", + " 40 64 14 1 F\n", + " /\n", + "COORD\n", + " 456511.0732421875 5935688.224121094 1726.18701171875 456510.625 5935687.958984375 1769.114990234375\n", + " 456651.255859375 5935769.156005859 1726.7149658203125 456651.26708984375 5935769.1630859375 1769.9449462890625\n", + " 456791.35302734375 5935850.0419921875 1728.3050537109375 456792.0107421875 5935850.4208984375 1771.9649658203125\n", + " 456931.36279296875 5935930.8759765625 1730.2080078125 456932.9091796875 5935931.769042969 1774.0279541015625\n", + " 457071.27880859375 5936011.656005859 1732.238037109375 457073.98486328125 5936013.219970703 1776.1810302734375\n", + " 457211.09912109375 5936092.381103516 1734.5169677734375 457215.25 5936094.779052734 1778.5789794921875\n", + " 457350.81982421875 5936173.049072266 1736.79296875 457356.68994140625 5936176.439941406 1781.0159912109375\n", + " 457490.4501953125 5936253.6650390625 1738.490966796875 457498.27392578125 5936258.185058594 1782.98095703125\n", + " 457630.04296875 5936334.258056641 1738.9100341796875 457639.9189453125 5936339.962890625 1783.5849609375\n", + " 457769.47216796875 5936414.7548828125 1737.2060546875 457781.2170410156 5936421.541015625 1781.9610595703125\n", + " 457908.56689453125 5936495.062011719 1734.01904296875 457921.9541015625 5936502.7958984375 1778.8489990234375\n", + " 458047.21484375 5936575.111083984 1730.656982421875 458062.3720703125 5936583.867919922 1775.56298828125\n", + " 458186.083984375 5936655.284912109 1729.3919677734375 458203.244140625 5936665.200927734 1774.583984375\n", + " 458324.98486328125 5936735.479003906 1729.5369873046875 458344.4091796875 5936746.701904297 1775.06201171875\n", + " 458464.2080078125 5936815.858886719 1730.842041015625 458486.0119628906 5936828.4580078125 1776.699951171875\n", + " 458603.68603515625 5936896.385986328 1732.656982421875 458627.8720703125 5936910.360839844 1778.6290283203125\n", + " 458742.7150878906 5936976.654296875 1733.9229736328125 458769.587890625 5936992.181152344 1780.0699462890625\n", + " 458882.1179199219 5937057.13671875 1735.4090576171875 458911.29296875 5937073.9951171875 1781.97900390625\n", + " 459022.35498046875 5937138.104003906 1738.333984375 459053.0830078125 5937155.857910156 1785.6510009765625\n", + " 459163.52392578125 5937219.607910156 1744.0260009765625 459195.6379394531 5937238.1630859375 1792.406982421875\n" + ] + } + ], + "source": [ + "relative_permeability_tables = '''\n", + "SWOF\n", + " 0.12 0.00000 1.0000 0.0\n", + " 0.20 0.00010 0.8500 0.0\n", + " 0.30 0.00100 0.6200 0.0\n", + " 0.40 0.00800 0.4000 0.0\n", + " 0.50 0.03000 0.2200 0.0\n", + " 0.60 0.09000 0.1000 0.0\n", + " 0.70 0.22000 0.0300 0.0\n", + " 0.80 0.48000 0.0050 0.0\n", + " 0.88 1.00000 0.0000 0.0 /\n", + "\n", + "SGOF\n", + " 0.00 0.0000 1.0000 0.0\n", + " 0.05 0.0020 0.9300 0.0\n", + " 0.10 0.0100 0.8000 0.0\n", + " 0.20 0.0600 0.5500 0.0\n", + " 0.30 0.1600 0.3200 0.0\n", + " 0.40 0.3300 0.1500 0.0\n", + " 0.50 0.5500 0.0500 0.0\n", + " 0.60 0.7800 0.0100 0.0\n", + " 0.70 0.9300 0.0010 0.0\n", + " 0.88 1.0000 0.0000 0.0 /\n", + "'''.strip()\n", + "print(relative_permeability_tables)\n", + "\n", + "grid_include_path = OUTPUT_DIRECTORY / \"REEK_GRID.GRDECL\"\n", + "sim_grid.to_file(grid_include_path, fformat=\"grdecl\")\n", + "\n", + "def export_grid_property(source_property, keyword, multiplier=1.0):\n", + " exported = source_property.copy()\n", + " exported.name = keyword\n", + " exported.values = source_property.values * multiplier\n", + " output_path = OUTPUT_DIRECTORY / f\"{keyword}.GRDECL\"\n", + " exported.to_file(output_path, fformat=\"grdecl\")\n", + " return output_path\n", + "\n", + "poro_path = export_grid_property(sim_poro, \"PORO\")\n", + "permx_path = export_grid_property(sim_permx, \"PERMX\")\n", + "permy_path = export_grid_property(sim_permx, \"PERMY\", 0.70)\n", + "permz_path = export_grid_property(sim_permx, \"PERMZ\", 0.10)\n", + "fipnum_path = export_grid_property(sim_zone, \"FIPNUM\")\n", + "\n", + "generated_static_paths = [grid_include_path, poro_path, permx_path, permy_path, permz_path, fipnum_path]\n", + "generated_static_table = pd.DataFrame([\n", + " {\n", + " \"file\": path.name,\n", + " \"bytes\": path.stat().st_size,\n", + " \"sha256\": hashlib.sha256(path.read_bytes()).hexdigest(),\n", + " }\n", + " for path in generated_static_paths\n", + "])\n", + "display(generated_static_table)\n", + "print(\"\\nGrid include header:\\n\", \"\\n\".join(grid_include_path.read_text(encoding=\"utf-8\").splitlines()[:24]))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a6aee21f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:30.418988Z", + "iopub.status.busy": "2026-08-31T17:04:30.418819Z", + "iopub.status.idle": "2026-08-31T17:04:30.427208Z", + "shell.execute_reply": "2026-08-31T17:04:30.426516Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUNSPEC\n", + "TITLE\n", + " PUBLIC RMS-ORIGIN REEK MODEL: NEQSIM PVT, OPM FLOW, ERT\n", + "\n", + "DIMENS\n", + " 40 64 14 /\n", + "\n", + "OIL\n", + "GAS\n", + "WATER\n", + "DISGAS\n", + "METRIC\n", + "\n", + "START\n", + " 1 'JAN' 2025 /\n", + "\n", + "WELLDIMS\n", + " 2 14 1 2 /\n", + "\n", + "TABDIMS\n", + "/\n", + "\n", + "EQLDIMS\n", + "/\n", + "\n", + "UNIFOUT\n", + "\n", + "GRID\n", + "INIT\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/REEK_GRID.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PORO.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PERMX.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PERMY.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PERMZ.GRDECL' /\n", + "\n", + "MULTIPLY\n", + " PORO 1.0 6* /\n", + " PERMX 1.0 6* /\n", + " PERMY 1.0 6* /\n", + " PERMZ 1.0 6* /\n", + "/\n", + "\n", + "PROPS\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/NEQSIM_PVT.INC' /\n", + "\n", + "ROCK\n", + " 260.0 4.0E-5 /\n", + "\n", + "SWOF\n", + " 0.12 0.00000 1.0000 0.0\n", + " 0.20 0.00010 0.8500 0.0\n", + " 0.30 0.00100 0.6200 0.0\n", + " 0.40 0.00800 0.4000 0.0\n", + " 0.50 0.03000 0.2200 0.0\n", + " 0.60 0.09000 0.1000 0.0\n", + " 0.70 0.22000 0.0300 0.0\n", + " 0.80 0.48000 0.0050 0.0\n", + " 0.88 1.00000 0.0000 0.0 /\n", + "\n", + "SGOF\n", + " 0.00 0.0000 1.0000 0.0\n", + " 0.05 0.0020 0.9300 0.0\n", + " 0.10 0.0100 0.8000 0.0\n", + " 0.20 0.0600 0.5500 0.0\n", + " 0.30 0.1600 0.3200 0.0\n", + " 0.40 0.3300 0.1500 0.0\n", + " 0.50 0.5500 0.0500 0.0\n", + " 0.60 0.7800 0.0100 0.0\n", + " 0.70 0.9300 0.0010 0.0\n", + " 0.88 1.0000 0.0000 0.0 /\n", + "\n", + "REGIONS\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/FIPNUM.GRDECL' /\n", + "\n", + "SOLUTION\n", + "EQUIL\n", + " 1726.879839 260.000000\n", + " 2077.856735 0.0 1453.380249 0.0 1 0 0 /\n", + "\n", + "RSVD\n", + " 1553.380249 224.67271009\n", + " 1977.856735 224.67271009 /\n", + "\n", + "SUMMARY\n", + "FPR\n", + "FOPR\n", + "FGPR\n", + "FWPR\n", + "FWIR\n", + "FOPT\n", + "FGPT\n", + "FWPT\n", + "WBHP\n", + " 'PROD' 'WINJ' /\n", + "WGOR\n", + " 'PROD' /\n", + "\n", + "SCHEDULE\n", + "RPTRST\n", + " 'BASIC=1' /\n", + "\n", + "GRUPTREE\n", + " 'WELLS' 'FIELD' /\n", + "/\n", + "\n", + "WELSPECS\n", + " 'PROD' 'WELLS' 25 38 1726.880 'OIL' /\n", + " 'WINJ' 'WELLS' 19 34 1726.880 'WATER' /\n", + "/\n", + "\n", + "COMPDAT\n", + " 'PROD' 25 38 1 1 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 2 2 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 3 3 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 4 4 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 5 5 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 6 6 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 7 7 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 8 8 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 9 9 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 10 10 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 11 11 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 12 12 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 13 13 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 14 14 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 1 1 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 2 2 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 3 3 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 4 4 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 5 5 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 6 6 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 7 7 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 8 8 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 9 9 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 10 10 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 11 11 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 12 12 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 13 13 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 14 14 'OPEN' 1* 1* 0.20 /\n", + "/\n", + "\n", + "WCONPROD\n", + " 'PROD' 'OPEN' 'ORAT' 10000.0 4* 80.000 /\n", + "/\n", + "\n", + "WCONINJE\n", + " 'WINJ' 'WATER' 'OPEN' 'RATE' 12000.0 1* 320.0 /\n", + "/\n", + "\n", + "TSTEP\n", + " 24*30.4375 /\n", + "\n", + "END\n" + ] + } + ], + "source": [ + "minimum_depth = float(np.ma.min(sim_z.values))\n", + "maximum_depth = float(np.ma.max(sim_z.values))\n", + "datum_depth = float(np.ma.mean(sim_z.values))\n", + "water_contact_depth = maximum_depth + 100.0\n", + "gas_contact_depth = minimum_depth - 100.0\n", + "initial_rs_sm3_sm3 = saturated_oil_rows[-1][\"Rs\"]\n", + "\n", + "def completion_lines(well_name, i_index, j_index, layers):\n", + " return \"\\n\".join(\n", + " f\" '{well_name}' {i_index + 1} {j_index + 1} {int(k)} {int(k)} 'OPEN' 1* 1* 0.20 /\"\n", + " for k in layers\n", + " )\n", + "\n", + "def render_deck(perm_multiplier, poro_multiplier, injection_rate):\n", + " return f'''\n", + "RUNSPEC\n", + "TITLE\n", + " PUBLIC RMS-ORIGIN REEK MODEL: NEQSIM PVT, OPM FLOW, ERT\n", + "\n", + "DIMENS\n", + " {sim_grid.ncol} {sim_grid.nrow} {sim_grid.nlay} /\n", + "\n", + "OIL\n", + "GAS\n", + "WATER\n", + "DISGAS\n", + "METRIC\n", + "\n", + "START\n", + " 1 'JAN' 2025 /\n", + "\n", + "WELLDIMS\n", + " 2 {max(len(producer_layers), len(injector_layers))} 1 2 /\n", + "\n", + "TABDIMS\n", + "/\n", + "\n", + "EQLDIMS\n", + "/\n", + "\n", + "UNIFOUT\n", + "\n", + "GRID\n", + "INIT\n", + "INCLUDE\n", + " '{grid_include_path.as_posix()}' /\n", + "INCLUDE\n", + " '{poro_path.as_posix()}' /\n", + "INCLUDE\n", + " '{permx_path.as_posix()}' /\n", + "INCLUDE\n", + " '{permy_path.as_posix()}' /\n", + "INCLUDE\n", + " '{permz_path.as_posix()}' /\n", + "\n", + "MULTIPLY\n", + " PORO {poro_multiplier} 6* /\n", + " PERMX {perm_multiplier} 6* /\n", + " PERMY {perm_multiplier} 6* /\n", + " PERMZ {perm_multiplier} 6* /\n", + "/\n", + "\n", + "PROPS\n", + "INCLUDE\n", + " '{black_oil_path.resolve().as_posix()}' /\n", + "\n", + "ROCK\n", + " 260.0 4.0E-5 /\n", + "\n", + "{relative_permeability_tables}\n", + "\n", + "REGIONS\n", + "INCLUDE\n", + " '{fipnum_path.as_posix()}' /\n", + "\n", + "SOLUTION\n", + "EQUIL\n", + " {datum_depth:.6f} {INITIAL_RESERVOIR_PRESSURE_BARA:.6f}\n", + " {water_contact_depth:.6f} 0.0 {gas_contact_depth:.6f} 0.0 1 0 0 /\n", + "\n", + "RSVD\n", + " {minimum_depth:.6f} {initial_rs_sm3_sm3:.8f}\n", + " {maximum_depth:.6f} {initial_rs_sm3_sm3:.8f} /\n", + "\n", + "SUMMARY\n", + "FPR\n", + "FOPR\n", + "FGPR\n", + "FWPR\n", + "FWIR\n", + "FOPT\n", + "FGPT\n", + "FWPT\n", + "WBHP\n", + " 'PROD' 'WINJ' /\n", + "WGOR\n", + " 'PROD' /\n", + "\n", + "SCHEDULE\n", + "RPTRST\n", + " 'BASIC=1' /\n", + "\n", + "GRUPTREE\n", + " 'WELLS' 'FIELD' /\n", + "/\n", + "\n", + "WELSPECS\n", + " 'PROD' 'WELLS' {producer_i + 1} {producer_j + 1} {datum_depth:.3f} 'OIL' /\n", + " 'WINJ' 'WELLS' {injector_i + 1} {injector_j + 1} {datum_depth:.3f} 'WATER' /\n", + "/\n", + "\n", + "COMPDAT\n", + "{completion_lines(\"PROD\", producer_i, producer_j, producer_layers)}\n", + "{completion_lines(\"WINJ\", injector_i, injector_j, injector_layers)}\n", + "/\n", + "\n", + "WCONPROD\n", + " 'PROD' 'OPEN' 'ORAT' 10000.0 4* {PRODUCER_BHP_LIMIT_BARA:.3f} /\n", + "/\n", + "\n", + "WCONINJE\n", + " 'WINJ' 'WATER' 'OPEN' 'RATE' {injection_rate} 1* 320.0 /\n", + "/\n", + "\n", + "TSTEP\n", + " 24*30.4375 /\n", + "\n", + "END\n", + "'''.strip()\n", + "\n", + "deck_text = render_deck(1.0, 1.0, 12000.0)\n", + "deck_path = OUTPUT_DIRECTORY / \"RMS_REEK_BASE.DATA\"\n", + "deck_path.write_text(deck_text + \"\\n\", encoding=\"utf-8\")\n", + "print(deck_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ded41d28", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:30.428895Z", + "iopub.status.busy": "2026-08-31T17:04:30.428736Z", + "iopub.status.idle": "2026-08-31T17:04:30.493986Z", + "shell.execute_reply": "2026-08-31T17:04:30.493266Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " contract passed\n", + "0 saturated Rs strictly increases True\n", + "1 bubble pressure strictly increases True\n", + "2 dry-gas pressure strictly increases True\n", + "3 dry-gas Bg strictly decreases True\n", + "4 no invalid numeric tokens True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OPM parser accepted 56 expanded keywords.\n" + ] + } + ], + "source": [ + "parsed_deck = Parser().parse(str(deck_path))\n", + "required_keywords = [\n", + " \"DIMENS\", \"COORD\", \"ZCORN\", \"ACTNUM\", \"PORO\", \"PERMX\", \"PERMY\", \"PERMZ\",\n", + " \"DENSITY\", \"PVTO\", \"PVDG\", \"PVTW\", \"EQUIL\", \"WELSPECS\", \"COMPDAT\",\n", + " \"WCONPROD\", \"WCONINJE\",\n", + "]\n", + "deck_keyword_audit = pd.DataFrame({\n", + " \"keyword\": required_keywords,\n", + " \"present\": [keyword in parsed_deck for keyword in required_keywords],\n", + "})\n", + "oil_table_audit = pd.DataFrame(saturated_oil_rows)\n", + "pvt_contract_audit = pd.DataFrame({\n", + " \"contract\": [\n", + " \"saturated Rs strictly increases\",\n", + " \"bubble pressure strictly increases\",\n", + " \"dry-gas pressure strictly increases\",\n", + " \"dry-gas Bg strictly decreases\",\n", + " \"no invalid numeric tokens\",\n", + " ],\n", + " \"passed\": [\n", + " np.all(np.diff(oil_table_audit[\"Rs\"]) > 0),\n", + " np.all(np.diff(oil_table_audit[\"pressure\"]) > 0),\n", + " np.all(np.diff(dry_gas_table[\"pressure_bara\"]) > 0),\n", + " np.all(np.diff(dry_gas_table[\"Bg_rm3_Sm3\"]) < 0),\n", + " not any(token in black_oil_include for token in [\"NaN\", \"Infinity\", \"-Infinity\"]),\n", + " ],\n", + "})\n", + "display(deck_keyword_audit)\n", + "display(pvt_contract_audit)\n", + "if not deck_keyword_audit[\"present\"].all() or not pvt_contract_audit[\"passed\"].all():\n", + " raise AssertionError(\"Deck or PVT contract validation failed.\")\n", + "print(f\"OPM parser accepted {len(parsed_deck)} expanded keywords.\")" + ] + }, + { + "cell_type": "markdown", + "id": "08514558", + "metadata": {}, + "source": [ + "## 7. Run OPM Flow and retain diagnostics\n", + "\n", + "This is the real simulator invocation. It is not a surrogate decline curve. The return code,\n", + "reported timing, output files, and log tail are retained. A failed simulator call raises an\n", + "exception and prevents publication of an apparently successful notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9976bace", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:30.495890Z", + "iopub.status.busy": "2026-08-31T17:04:30.495722Z", + "iopub.status.idle": "2026-08-31T17:04:42.585943Z", + "shell.execute_reply": "2026-08-31T17:04:42.585194Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " diagnostic value unit\n", + "0 return code 0 -\n", + "1 wall time 12.080665 s\n", + "2 SMSPEC exists True boolean\n", + "3 UNRST exists True boolean\n", + "4 PRT exists True boolean" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Report step 22/24 at day 669.625/730.5, date = 01-Nov-2026\n", + "Restart file written for report step 22/24, date = 01-Nov-2026 15:00:00\n", + "\n", + "Starting time step 0, stepsize 30.4375 days, at day 669.625/700.062, date = 01-Nov-2026\n", + " Newton its= 2, linearizations= 3 (0.1sec), linear its= 3 (0.1sec)\n", + "\n", + "Report step 23/24 at day 700.062/730.5, date = 02-Dec-2026\n", + "Restart file written for report step 23/24, date = 02-Dec-2026 01:30:00\n", + "\n", + "Starting time step 0, stepsize 30.4375 days, at day 700.062/730.5, date = 02-Dec-2026\n", + " Oscillating behavior detected: Relaxation set to 0.900000\n", + " Newton its= 5, linearizations= 6 (0.2sec), linear its= 6 (0.3sec)\n", + "Restart file written for report step 24/24, date = 01-Jan-2027 12:00:00\n", + "\n", + "\n", + "================ End of simulation ===============\n", + "\n", + "Number of MPI processes: 1\n", + "Threads per MPI process: 2\n", + "Setup time: 0.51 s\n", + " Deck input: 0.19 s\n", + "Number of timesteps: 27\n", + "Simulation time: 11.12 s\n", + " Assembly time: 3.72 s (Wasted: 0.0 s; 0.0%)\n", + " Well assembly: 0.03 s (Wasted: 0.0 s; 0.0%)\n", + " Linear solve time: 5.20 s (Wasted: 0.0 s; 0.0%)\n", + " Linear setup: 2.65 s (Wasted: 0.0 s; 0.0%)\n", + " Props/update time: 1.42 s (Wasted: 0.0 s; 0.0%)\n", + " Pre/post step: 0.61 s (Wasted: 0.0 s; 0.0%)\n", + " Output write time: 0.03 s\n", + "Overall Linearizations: 120 (Wasted: 0; 0.0%)\n", + "Overall Newton Iterations: 93 (Wasted: 0; 0.0%)\n", + "Overall Linear Iterations: 123 (Wasted: 0; 0.0%)\n", + "\n" + ] + } + ], + "source": [ + "flow_start = time.perf_counter()\n", + "flow_run = subprocess.run(\n", + " [FLOW_EXECUTABLE, deck_path.name],\n", + " cwd=OUTPUT_DIRECTORY,\n", + " env=FLOW_RUN_ENV,\n", + " capture_output=True,\n", + " text=True,\n", + " timeout=1200,\n", + ")\n", + "flow_elapsed = time.perf_counter() - flow_start\n", + "if flow_run.returncode != 0:\n", + " raise RuntimeError(\n", + " \"OPM Flow failed:\\n\" + flow_run.stdout[-8000:] + \"\\n\" + flow_run.stderr[-4000:]\n", + " )\n", + "flow_log_tail = \"\\n\".join(flow_run.stdout.splitlines()[-35:])\n", + "flow_diagnostics = pd.DataFrame({\n", + " \"diagnostic\": [\"return code\", \"wall time\", \"SMSPEC exists\", \"UNRST exists\", \"PRT exists\"],\n", + " \"value\": [\n", + " flow_run.returncode,\n", + " flow_elapsed,\n", + " (OUTPUT_DIRECTORY / \"RMS_REEK_BASE.SMSPEC\").exists(),\n", + " (OUTPUT_DIRECTORY / \"RMS_REEK_BASE.UNRST\").exists(),\n", + " (OUTPUT_DIRECTORY / \"RMS_REEK_BASE.PRT\").exists(),\n", + " ],\n", + " \"unit\": [\"-\", \"s\", \"boolean\", \"boolean\", \"boolean\"],\n", + "})\n", + "display(flow_diagnostics)\n", + "print(flow_log_tail)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "675d2ce3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:42.588076Z", + "iopub.status.busy": "2026-08-31T17:04:42.587887Z", + "iopub.status.idle": "2026-08-31T17:04:42.610403Z", + "shell.execute_reply": "2026-08-31T17:04:42.609430Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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datetime_daysfield_pressure_baraoil_rate_Sm3_daygas_rate_Sm3_daywater_rate_Sm3_daywater_injection_Sm3_daycumulative_oil_MSm3cumulative_gas_GSm3cumulative_water_MSm3producer_bhp_barawell_gor_Sm3_Sm3
02025-01-02 00:00:001.0000259.66888410000.02246727.00.00151312000.00.0100000.0022471.512924e-09249.359833224.672714
12025-01-05 00:00:004.0000259.52276610000.02246727.00.00228112000.00.0400000.0089878.356746e-09245.149246224.672714
22025-01-14 00:00:0013.0000259.08401510000.02246727.00.00268212000.00.1300000.0292073.249741e-08242.938095224.672714
32025-01-31 10:30:0030.4375258.23397810000.02246727.00.00295012000.00.3043750.0683858.394567e-08241.457336224.672714
42025-03-02 21:00:0060.8750256.74893210000.02246727.00.00328812000.00.6087500.1367701.840319e-07239.587173224.672714
52025-04-02 07:30:0091.3125255.26213110000.02246727.00.00359612000.00.9131250.2051542.934751e-07237.877884224.672714
62025-05-02 18:00:00121.7500253.77357510000.02246727.00.00388712000.01.2175000.2735394.118004e-07236.249237224.672714
72025-06-02 04:30:00152.1875252.28321810000.02246727.00.00417112000.01.5218750.3419245.387495e-07234.662354224.672714
82025-07-02 15:00:00182.6250250.79107710000.02246727.00.00445112000.01.8262500.4103096.742363e-07233.085846224.672714
92025-08-02 01:30:00213.0625249.29715010000.02246727.00.00472912000.02.1306250.4786938.181660e-07231.521439224.672714
102025-09-01 12:00:00243.5000247.80140710000.02246727.00.00500412000.02.4350000.5470789.704735e-07229.963760224.672714
112025-10-01 22:30:00273.9375246.30381810000.02246727.00.00527812000.02.7393750.6154631.131130e-06228.406265224.672714
122025-11-01 09:00:00304.3750244.80450410000.02246727.00.00555012000.03.0437500.6838481.300057e-06226.857697224.672714
132025-12-01 19:30:00334.8125243.30331410000.02246727.00.00582212000.03.3481250.7522321.477265e-06225.302551224.672714
142026-01-01 06:00:00365.2500241.80030810000.02246727.00.00609512000.03.6525000.8206171.662770e-06223.738480224.672714
152026-01-31 16:30:00395.6875240.29547110000.02246727.00.00636812000.03.9568750.8890021.856599e-06222.164276224.672714
162026-03-03 03:00:00426.1250238.78877310000.02246727.00.00664412000.04.2612500.9573872.058831e-06220.569641224.672714
172026-04-02 13:30:00456.5625237.28007510000.02246727.00.00692812000.04.5656251.0257712.269689e-06218.926651224.672714
182026-05-03 00:00:00487.0000235.76937910000.02246727.00.00721712000.04.8700001.0941562.489368e-06217.240448224.672714
192026-06-02 10:30:00517.4375234.25672910000.02246727.00.00750912000.05.1743751.1625412.717920e-06215.538254224.672714
202026-07-02 21:00:00547.8750232.74227910000.02246727.00.00779812000.05.4787501.2309262.955285e-06213.841293224.672714
212026-08-02 07:30:00578.3125231.22622710000.02246727.00.00807812000.05.7831251.2993103.201147e-06212.199692224.672714
222026-09-01 18:00:00608.7500229.70838910000.02246727.00.00835312000.06.0875001.3676953.455400e-06210.572861224.672714
232026-10-02 04:30:00639.1875228.18869010000.02246727.00.00863112000.06.3918751.4360803.718091e-06208.931488224.672714
242026-11-01 15:00:00669.6250226.66711410000.02246727.00.00891012000.06.6962501.5044653.989294e-06207.270493224.672714
252026-12-02 01:30:00700.0625225.14360010000.02246727.00.00919412000.07.0006251.5728494.269141e-06205.577927224.672714
262027-01-01 12:00:00730.5000223.61802710000.02246727.00.00949012000.07.3050001.6412344.557990e-06203.814545224.672714
\n", + "
" + ], + "text/plain": [ + " date time_days field_pressure_bara oil_rate_Sm3_day gas_rate_Sm3_day water_rate_Sm3_day water_injection_Sm3_day cumulative_oil_MSm3 \\\n", + "0 2025-01-02 00:00:00 1.0000 259.668884 10000.0 2246727.0 0.001513 12000.0 0.010000 \n", + "1 2025-01-05 00:00:00 4.0000 259.522766 10000.0 2246727.0 0.002281 12000.0 0.040000 \n", + "2 2025-01-14 00:00:00 13.0000 259.084015 10000.0 2246727.0 0.002682 12000.0 0.130000 \n", + "3 2025-01-31 10:30:00 30.4375 258.233978 10000.0 2246727.0 0.002950 12000.0 0.304375 \n", + "4 2025-03-02 21:00:00 60.8750 256.748932 10000.0 2246727.0 0.003288 12000.0 0.608750 \n", + "5 2025-04-02 07:30:00 91.3125 255.262131 10000.0 2246727.0 0.003596 12000.0 0.913125 \n", + "6 2025-05-02 18:00:00 121.7500 253.773575 10000.0 2246727.0 0.003887 12000.0 1.217500 \n", + "7 2025-06-02 04:30:00 152.1875 252.283218 10000.0 2246727.0 0.004171 12000.0 1.521875 \n", + "8 2025-07-02 15:00:00 182.6250 250.791077 10000.0 2246727.0 0.004451 12000.0 1.826250 \n", + "9 2025-08-02 01:30:00 213.0625 249.297150 10000.0 2246727.0 0.004729 12000.0 2.130625 \n", + "10 2025-09-01 12:00:00 243.5000 247.801407 10000.0 2246727.0 0.005004 12000.0 2.435000 \n", + "11 2025-10-01 22:30:00 273.9375 246.303818 10000.0 2246727.0 0.005278 12000.0 2.739375 \n", + "12 2025-11-01 09:00:00 304.3750 244.804504 10000.0 2246727.0 0.005550 12000.0 3.043750 \n", + "13 2025-12-01 19:30:00 334.8125 243.303314 10000.0 2246727.0 0.005822 12000.0 3.348125 \n", + "14 2026-01-01 06:00:00 365.2500 241.800308 10000.0 2246727.0 0.006095 12000.0 3.652500 \n", + "15 2026-01-31 16:30:00 395.6875 240.295471 10000.0 2246727.0 0.006368 12000.0 3.956875 \n", + "16 2026-03-03 03:00:00 426.1250 238.788773 10000.0 2246727.0 0.006644 12000.0 4.261250 \n", + "17 2026-04-02 13:30:00 456.5625 237.280075 10000.0 2246727.0 0.006928 12000.0 4.565625 \n", + "18 2026-05-03 00:00:00 487.0000 235.769379 10000.0 2246727.0 0.007217 12000.0 4.870000 \n", + "19 2026-06-02 10:30:00 517.4375 234.256729 10000.0 2246727.0 0.007509 12000.0 5.174375 \n", + "20 2026-07-02 21:00:00 547.8750 232.742279 10000.0 2246727.0 0.007798 12000.0 5.478750 \n", + "21 2026-08-02 07:30:00 578.3125 231.226227 10000.0 2246727.0 0.008078 12000.0 5.783125 \n", + "22 2026-09-01 18:00:00 608.7500 229.708389 10000.0 2246727.0 0.008353 12000.0 6.087500 \n", + "23 2026-10-02 04:30:00 639.1875 228.188690 10000.0 2246727.0 0.008631 12000.0 6.391875 \n", + "24 2026-11-01 15:00:00 669.6250 226.667114 10000.0 2246727.0 0.008910 12000.0 6.696250 \n", + "25 2026-12-02 01:30:00 700.0625 225.143600 10000.0 2246727.0 0.009194 12000.0 7.000625 \n", + "26 2027-01-01 12:00:00 730.5000 223.618027 10000.0 2246727.0 0.009490 12000.0 7.305000 \n", + "\n", + " cumulative_gas_GSm3 cumulative_water_MSm3 producer_bhp_bara well_gor_Sm3_Sm3 \n", + "0 0.002247 1.512924e-09 249.359833 224.672714 \n", + "1 0.008987 8.356746e-09 245.149246 224.672714 \n", + "2 0.029207 3.249741e-08 242.938095 224.672714 \n", + "3 0.068385 8.394567e-08 241.457336 224.672714 \n", + "4 0.136770 1.840319e-07 239.587173 224.672714 \n", + "5 0.205154 2.934751e-07 237.877884 224.672714 \n", + "6 0.273539 4.118004e-07 236.249237 224.672714 \n", + "7 0.341924 5.387495e-07 234.662354 224.672714 \n", + "8 0.410309 6.742363e-07 233.085846 224.672714 \n", + "9 0.478693 8.181660e-07 231.521439 224.672714 \n", + "10 0.547078 9.704735e-07 229.963760 224.672714 \n", + "11 0.615463 1.131130e-06 228.406265 224.672714 \n", + "12 0.683848 1.300057e-06 226.857697 224.672714 \n", + "13 0.752232 1.477265e-06 225.302551 224.672714 \n", + "14 0.820617 1.662770e-06 223.738480 224.672714 \n", + "15 0.889002 1.856599e-06 222.164276 224.672714 \n", + "16 0.957387 2.058831e-06 220.569641 224.672714 \n", + "17 1.025771 2.269689e-06 218.926651 224.672714 \n", + "18 1.094156 2.489368e-06 217.240448 224.672714 \n", + "19 1.162541 2.717920e-06 215.538254 224.672714 \n", + "20 1.230926 2.955285e-06 213.841293 224.672714 \n", + "21 1.299310 3.201147e-06 212.199692 224.672714 \n", + "22 1.367695 3.455400e-06 210.572861 224.672714 \n", + "23 1.436080 3.718091e-06 208.931488 224.672714 \n", + "24 1.504465 3.989294e-06 207.270493 224.672714 \n", + "25 1.572849 4.269141e-06 205.577927 224.672714 \n", + "26 1.641234 4.557990e-06 203.814545 224.672714 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "flow_summary = ESmry(str(OUTPUT_DIRECTORY / \"RMS_REEK_BASE.SMSPEC\"))\n", + "def summary_values(key):\n", + " return np.asarray(flow_summary[key], dtype=float)\n", + "\n", + "reservoir_history = pd.DataFrame({\n", + " \"date\": pd.to_datetime(flow_summary.dates()),\n", + " \"time_days\": summary_values(\"TIME\"),\n", + " \"field_pressure_bara\": summary_values(\"FPR\"),\n", + " \"oil_rate_Sm3_day\": summary_values(\"FOPR\"),\n", + " \"gas_rate_Sm3_day\": summary_values(\"FGPR\"),\n", + " \"water_rate_Sm3_day\": summary_values(\"FWPR\"),\n", + " \"water_injection_Sm3_day\": summary_values(\"FWIR\"),\n", + " \"cumulative_oil_MSm3\": summary_values(\"FOPT\") / 1e6,\n", + " \"cumulative_gas_GSm3\": summary_values(\"FGPT\") / 1e9,\n", + " \"cumulative_water_MSm3\": summary_values(\"FWPT\") / 1e6,\n", + " \"producer_bhp_bara\": summary_values(\"WBHP:PROD\"),\n", + " \"well_gor_Sm3_Sm3\": summary_values(\"WGOR:PROD\"),\n", + "})\n", + "display(reservoir_history)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "719a74d6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:42.612106Z", + "iopub.status.busy": "2026-08-31T17:04:42.611918Z", + "iopub.status.idle": "2026-08-31T17:04:43.741289Z", + "shell.execute_reply": "2026-08-31T17:04:43.740226Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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uqFixYurdu7f8/PyyfOxZKSN7tRoMBt26dSvVuAUHB6dbb8+S92rMrPnz56tkyZLmv0Wjo6MVHR2tGTNm6MaNGypRooT69u37yP0T7UFG4xAVFaWoqCh99dVXCg4OVsmSJdWnTx/5+vqa63Li6+Fx5jJz5sxRp06dVKJECXNZVFSUwsPDNX36dIWEhKhUqVLq27evRU4jJ/j6669VrVo1VaxYMUXdnTt3lJCQkOb7anr1tmLff7HZqfj4+FT3DHFxcUn16oX02me0P3vxOONOSEjQe++9p4iICIu9BaKjo/Xzzz+rTJkyqlatmpYtW2b3e9VkNA4JCQmKi4vTzz//rHLlyqlatWpatGiRhg0blqn+7MXjjHv79u2KiorSc889Zy67f/++EhIS9PPPP6tChQqqXLmy5s+fnyNuNpMRacXN2dn5ke8PyfX/RjExMXr99deVN29evfnmm5Jk3tdrw4YNqly5ssqXL69Zs2bliL1qMuLB86xSpYrKly+vGTNm6L333pOU+uvF2dlZDg4O/9rXw7p16+Tn52dx9e+DcapatarKlCmjqVOn6sMPP8yuYWZY8p6o5cuX18CBA1Ntk9r3O/mT/7TeHx6sh/1J63uW1vcrvfYZ7c9ePM64f/75Z82cOVOzZ882X/kRFRWlPXv2KCkpSQ0bNtQ///yjjh076urVqzYZf1ZJLQ4PXtH5sKioKO3evVuSaZXUuXPn1LFjR/PN2DLanz1ISEhQUlJSpl4PsbGxWr58ufr3729RHhUVpZ07d8rJyUkNGjTQ33//reeff14hISFZPv7skhybhz+QT45bevX/VkuWLNF///tfzZ4923zuUVFR2r59uzw8PFS/fn0dOXJEnTp1UmhoaDaPNmtFRUVpy5Yt8vLyUv369fXXX3+pS5cuunfvnnn/y6fp9XDnzh2tX79e/fr1syiPjo7W77//rjx58qhu3brau3evunbtmu4eq/Zk7ty52rJli8Xe+A9K/n6n9b6aXr2t2PflQ3bKy8tLcXFxSkxMtPgBjoqKSvUTivTaZ7Q/e+Hl5aX79+8rKSnJ4sqj9MYdERGhN954Q87Ozlq8eLHFp3wnTpywiEHz5s3VqlUrBQYGWnyiaU+8vLxS3QA4Ojo61Ti4uLjo1KlTFufZoEEDtW/fXqdPn85wf/YitXEbjUbFxMSk+0nuTz/9pC5duli8AebKlStFnOrXr6/nn39e58+fV7ly5bL2BLJJanFLTlB7e3srNjb2kfX/Nrdu3dLQoUNVokQJTZkyxXwlrK+vb4rXQ61atdSrVy+NHj1aRYoUya4hZyk/P78U51mtWjW9+OKLGj16dKqvl+joaBmNxn/l60EyvT88fGVg8eLFU8SpUqVK6t+/v0aNGiUfH58nPcwMuXbtmgYPHqzatWtr/Pjxad4EIbXvd3LiNXn+8Kh62J+0vmdpfb/Sa5/R/uxFZsc9b948rVixQgsWLFD16tXN5dOnT5ck83y0S5cu6t69u1auXGn+8MYepRWHtOZNM2fOlGR5ni+88IL++9//6t13381wf/bAxcVFbm5uqY47vTuVb926Vfnz51ft2rUtyr/55hs5ODiYr4Tv0qWLnn/+ea1Zs+Zf8yF2rly55OLioujoaIvy6Oho+fn5pVv/b5OUlKSJEydqx44dWrZsmcX2JkuWLJGjo6P59dCpUye1bdtW69evT5G4zsmWL19ucZ7PP/+82rRpow0bNqhXr15ycHBI9eesVKlS2TBa29uwYYNq1aplcdWjZNrK5+E4tWzZUps2bVKvXr2yY6hWS0hI0EcffaQTJ05oxYoVKlq0aKrtkt/z0/o9m169rXDlYyYkv5ldunTJXHbnzh2FhYWlmhBJr31G+7MXZcuWlcFgsNh3LiQkRJGRkSpfvnyqz4mOjtagQYNUsGBBffvttykmQ/fv37d4XLhwYbm4uCgsLCzLx59VypYtq/v371tconz16lXFxcWl+f17+DyLFy8uBwcHhYeHZ6o/e1C2bFlFR0dbfKp86dIlJSYmPnLcCQkJ2rdvn8Xd2JI9HKfkXx72/HrIqHLlyik0NFT37t0zl124cEEODg4qU6ZMuvX/JqGhoXr11VdVt25dffXVVym2W0jr9RAeHv7ExvgkPOo8y5Urp1u3bpkTTJLp9eDi4vKvnDzeu3dPx48fV/PmzVPUpfY+Ksnu9zq8efOmXnnlFbVv315ffPHFI7eRKFeunK5du2b+hFoy3RXW3d1dRYsWTbce9qdcuXL6559/LMoe9YFaeu0z2p+9SG3cFy5ceOS4Z82apXXr1mnlypUWiUfJtOfVg/teOTg4qHjx4nb/+6Fs2bKpfv/Smkendp7FihUzz4sy2p+9yOzreNeuXanOHxMTEy2u3nF0dFTRokXt/vWQUWXKlHnk9zu9+n+TTz75RIcPH9Z///vfFPPjh39uXFxcVKRIkX/V3xOS6W+q5PsoSKarngsVKqTw8HC5ubmpWLFiGX7fzcnSen94OE5ubm4qWLCg3b8/JCUl6f3339fly5e1cuXKR87zfHx8lC9fPovvd1JSkv755x+VK1cu3XpbIfmYCT4+PqpXr56WLl1qLlu0aJGqVKmi0qVLZ7h9RvuzF/ny5VNAQECKcVevXt38R+DDJk+eLF9fX02ePDnFprHXr19XQECA9uzZYy775Zdf5OTkZLGZvr0pUqSIqlevriVLlpjLFi5cqICAgBT7KEimG/XUqVNHf/31l7ls/fr1cnd3V8WKFTPcn70oXry4qlSpYjHuRYsWqV69esqfP3+az/v7778VGxurSpUqWZSfP39ederU0ZEjR8xlP/30kzw9PVPd2yKnKlu2rMqWLWvxc/T999+rSZMmypMnT7r1/yYffvih/P39NXbs2BT79p04cUJ169ZVUFCQueynn35S3rx5/1VJ2CNHjqhevXo6c+aMuWzdunXy8fFRqVKlVLlyZRUtWlQ//PCDuX7RokVq0aKFPDw8smPINnX48GF5e3urWLFiFuX79+9XgwYNdOHCBXPZ+vXrVaBAgTR//9iL0aNHq23btlZdfVOzZk3lyZNHq1atkmSaGC5ZskRt2rSRi4tLuvWwP23atNH69evNf/SeOHFCR44cUbt27TLVPqP92Ys2bdrop59+Mi9zO3z4sE6ePGmx/cqD9u3bpxUrVmjJkiWp/oz37dtXn3/+uflxSEiI9u/fL39/f9ucQBZp27atfvzxR/MHSn/++adOnz6tNm3apNq+V69eFjfeCQ4O1sGDB83nmdH+7EWbNm20Zs0a81U4e/bs0aVLl9S6detHPu+vv/5K9W+ELl26aNq0aebHV69e1aFDh+z+9ZBRyXGLjY2VZEq2XL9+3Xxzz/Tq/y3Wr1+vvXv3atGiRcqXL59FndFoVLt27TRv3jxz2YULFxQYGPivej0kJSWpTZs2+uabb8xlZ8+e1alTp8xXBrdp00arVq0yf2C5adMmRUZGpvoBb06XlJSkw4cPp3h/SExMVMuWLfX999+by/7++2+dPXs2xRXU9mbFihU6c+aMvvvuO6v2wWzTpo15X3RJ+vHHH+Xg4KBGjRpZVW8LDkZ73kzPjl24cEH9+/eXj4+P+SYh3333nfkF/uOPP+rKlSt65513rGqfXr29OnfunPr376/8+fPLyclJISEhWrBggTmRtGbNGgUHB+utt95SSEiIWrRoocqVK6dYEvef//xHZcuW1Xfffae5c+eqVq1aio+P15kzZ/Txxx9n+g6pT0pQUJD5ik6j0ag7d+5o8eLFKlu2rCTTxuhhYWHmfR3nzp2r7777TrVr11ZsbKwuXLigTz/91PzHQnr92atTp05p0KBBKlKkiAwGg+7du6fvv//enBxavny5oqOjLe7UtXnzZn388cep3mlt1qxZWrRokfz9/RUTE6N//vlHn332mdq2bfvEzikzxowZozt37ig4OFjBwcHmyc2nn36qokWLavHixUpKStKAAQMkSceOHdOQIUNUokQJ3b9/X9HR0RZ/YKVXb69Gjx6t0NBQ3bhxQyEhIeZf6l988YUKFSqkRYsWydHRUf369VNQUJC6dOmi2rVrp7gD3eTJk5U/f35NnjxZq1atkr+/v8LDw3Xt2jVNnjzZ7idNI0eOVFhYmK5fv67bt2+bN0CfOHGi/Pz89N1338nNzc18p/cJEyZozZo18vf3V1hYmG7cuKEpU6aY70h36NAhDR8+XKVLl1Z0dLTi4uK0ZMkSu196/tZbbyk6OlrXrl1TaGioeSuNqVOnytfXV19//bXy5s1rcaf45cuXa9WqVdqwYYNFX0ajUePHj9f69etVp04dhYaG6tatW5o6daoaNmz4RM8rIw4ePKhXXnlF9erVS3Fl7+zZs+Xh4aE5c+aoQIEC5qU/e/bs0TvvvKNy5copLCxMjo6OWrJkiQoUKGBVPexLYmKi3nrrLR0/flzlypVTYGCghg8frkGDBkkyrRiYPn26Pv30U+XNmzfd9unV26v4+Hi9/vrrCgoKUtmyZRUYGKgRI0bo5ZdflmSaX86ePVsTJkyQl5eX+vTpoxs3bqS4IuO5555Tjx49FBgYqKFDhypfvnzy8/PT8ePH1bJlS02cONGub0oWHx+vYcOG6ezZsypdurQCAwM1atQovfjii5KkM2fOaO7cuZo8ebLc3d11/PhxDRs2TAUKFFD+/Pl1/PhxPfvss5owYYIcHBzS7c9e3b9/X6+99pquXLmikiVL6uTJkxo7dqx69OghSTp58qTmz5+vadOmmT9YSUpKUrVq1bR48WLVq1fPor9Dhw7p9ddfV5EiReTr66tjx46pY8eOGj9+/BM/t4xYu3atNm3apMTERB04cED+/v7y8PDQ888/ry5duujEiRNasGCBZsyYIUdHR8XExOi1117T9evXzduRfPjhh3rhhRckKd16e7Vq1Spt2bJF8fHx+vPPPxUQEKBcuXKpa9eu6tixow4fPqxly5aZ97t79tlnJSnF8tqePXuqbdu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mpUxf+bh8+XLlz59fbm5uCggIUNmyZdWvXz+tX7/eqj5++eUXzZw5U9evX1fu3LnNd0e0pv7UqVPq3r17ij7nzp2r1q1bS5K2bdumCRMm6MaNG8qXL5/eeust9ezZMzOnCwAAgHSsW7dOt2/fNt/pGgAA5AwODg7ydHusXflsrkWLFtq/f78uXbqkpk2bqnLlyrp+/bouXryof/75R40aNVJERITGjx+vl156SZ999ply586tdevWaenSpan2mXzPkvHjx1sk6ySZr5yU9MgL5lJjTVI1vWOHhoZKkiIiIpQ3b16rj53cNiYmxlyWkJCg+/fvW9yjxdrEb1ZxzMyT3NzcFB0dLUny8vJSeHi4fHx8FBsbq7CwsHSff+zYMY0dO1ZjxoxRYGCgvv76ay1evFgbN260qj4qKkp58+bVmTNnLP4lJx6vXr2qESNG6I033tDx48c1ceJETZgwQUeOHMnM6QIAACAdvXr1Unh4uLZs2aK7d+8qOjra/I+7XQMAgMfRvHlzHTx4UPv27VPDhg3l6OiogIAAzZw5U/Xq1ZOHh4eCg4MVExOjnj17qmjRooqPj9emTZt0//59cz9OTk7mx4ULF1aFChW0f/9+FStWTMWKFVNUVJS+/PJLm10BmCy9YxcpUkSlS5fWf//7X/Nzpk2bplmzZqU4j4f7LVOmjP744w9z2bp165Q7d25Vq1bNpuf0KJlKPjZu3FhvvfWWoqOjVbFiRU2bNk2///67bt26ZdWej3FxcRo5cqRat24tJycn1a5dW/Xr19exY8esqo+Ojpanp2ea/a9fv161a9dW165d5erqqqZNm6pdu3b63//+l5nTBQAAQDqWL1+uXbt26Y033lCjRo3k7+9v/rd8+fLsHh4AAMjBGjRooL///lv58+c3XynYoEED/fbbb3rmmWckmZZnV69eXb1791aPHj00dOhQDR8+XPfu3dM777wjSapdu7YWLVqkbt26ycHBQVOnTtXmzZvVrl079erVS/3791fTpk0zfLVjRqV3bEdHR02ZMkWbN29W69at1ahRI23btk0dO3ZMcR4PcnR01OTJk3XgwAF17dpVL7zwgmbPnq2pU6fK29vbpuf0KJmKZvI3zdXVVa+99poGDRqktWvXavjw4VZlh+vXr6/69eubHxuNRl29elUNGjSwqj4qKsqcfDQYDCnW7//999+qUqWKRVmVKlX0448/ZvxkAQAAkK6+ffvqueeeS3VfpYeXEwEAAGSEq6ur1q5da5Fz6tKli/z9/VWyZElJpqsBx44dqwIFCsjBwcG8r+GOHTsUFxcnSRo5cqS6detmvlFLpUqVtGHDBt2+fVtRUVEqWrSoxTG++eYb5cuX75FjW7Rokfz8/FJtX7p0aa1bt07u7u4pnpfesWvUqKGtW7cqODhYSUlJKlKkiHm59MPn8eAYqlatqmnTpqlw4cKSTFdDPrjMOiNjzCqZSj66ublpzJgxkkyD3LZtW7o3jnmUmTNnKj4+3nzb9PTqo6KiFBERoR49eigoKEgeHh7q1KmT3nvvPbm6uiosLMx8t6BkefLk0b17j751vMFgkMFgm01Wk/u1Vf85BXEwIQ4mxMGEOJgQBxPiYEIcTJ5UHLKi/yJFiqhIkSLm/hwdHZ/4fkIAAODfq3Tp0haPPT09Vbly5RTtihYtavFh6MM3ailVqlSK5xQoUCDVG7pYc7OZcuXKpdk+V65c5jEaDAZFR0fLzc3NqmMnS04iPuzB83hwDMkKFiyY6ofCjxqjrWQqW3j//n19++23evvttxUeHq5x48bp3r17Gjt2bIorDh/FaDRqypQp+v3337V48eIUS6nTqi9atKgaN26svn37qkqVKgoKCtKwYcPk6empESNGyMHBQUlJSRZ9JSUlpTsBPnv2rNVjz6zAwECbHyMnIA4mxMGEOJgQBxPiYEIcTIiDSU6Jw+LFi7Vs2TIFBwfL0dFRZcuW1fDhw9W2bdvsHhoAAEC2uXz5sgYPHiwXFxf5+/tn93CeuEwlH/9fe/cdFsXVtgH8XhaWIk1FKYoRURAVFDX2CsRuNDFqbImxxdjbaywxJkZjeU0xJpbE2FGxY+8GRdCIRsWoUeyAIkpd+u7O98d8zOuGtiysLHr/ritX3HPOnHnm2WVZnp05s3TpUty6dQtqtRorVqxASkoKmjZtipkzZ+p8t2uVSoUpU6YgJiYGQUFBcHBw0Lm/Y8eO0jX9gHhK6cCBA3HkyBFMnjwZlStXlu4MlCshISHPPv7Nw8NDpzUr9aFWqxEZGQlvb+8Cb/P+JmAeRMyDiHkQMQ8i5kHEPIiYB9GrykN6enqJv4QNCQnBTz/9hNGjR8PT0xNqtRp///03Pv/8c7i7u+f7jTwRERHRm8DFxQW//PILnJ2dC72HyetKr+Lj+fPn8csvv0Aul+PYsWNYuXIl6tati507dyIjI0On68Rnz56NxMREbNq0Kd+CX2H9oaGhkMlkaN26tdSWlpYGa2trAOJ18UePHtXa5tKlS2jUqFGhMcnlcoP/gfMq9lEeMA8i5kHEPIiYBxHzIGIeRKZe6v4AAF08SURBVMyDyNB5KI25L1++jGHDhmHUqFFSm5+fHxITE/HXX3+VWvExNTUVp06dQkZGBpo2bZpn3rt37+LChQswNzdH+/bt83z5XNJ+IiIiouIyMzN7o7+I1av4mJOTA1tbWzx+/BiZmZnw9PQEIF4nnpmZWWTxMSQkBKdOncK2bduQlpaGtLQ0AOKTYW9vX2T/w4cP8dNPP+G7775DvXr1cOXKFWzdulVah7J3795YtWoVVq9ejV69euGPP/5AaGgo9u7dq8/hlh5BgJCTBo3mzf0jSlCrIdNkMA/MAwDmIRfzIGIeRMyDiHkQCWo1IAhlHYZOCrqDopmZGezs7EplH48fP8agQYPg4eEBR0dHLF68GPPmzUPPnj0BAHv27MG8efPg5+eHtLQ0LFy4EBs3bpSWBSppPxEREREVn17Fx1q1aiEoKAj3799Hhw4dIJPJcOfOHaSmpsLe3r7I7XNvUPPBBx9otTdp0gRr1qwpsn/QoEHIysrCokWL8Pz5c7i4uGD69OnSDWkcHBywZs0aLFq0CGvXrkW1atWwYsWKPIuTvkqCIMDh3nDEX79WZjEYC2cA8X+XdRRlj3kQMQ8i5kHEPIiYBxHzIKps1RBCo4iyDqNI77//Pv7zn/+gbt26aNSoETQaDS5duoSoqChMmDABKpUKgHiWpb43olm4cCGaNWuGpUuXAhCX4rl79y4AICsrC99++y0WLFiAbt26AQAWLFiAxYsXY8OGDSXuJyIiIiL96FV8nDBhAiZNmgRLS0usXLkSADBq1CiMHj1apw+T8+bNw7x58/TuB4Bhw4Zh2LBhBfY3atQI27ZtKzKWV0kA7/hIREREr6fff/8d586dQ2hoaJ6+lxdWnzlzJoYOHVrs+dPS0nDmzBmsX79eagsICEBAQAAA8bJvlUqldXOb9957D3369EFaWhquXbtWov43cX0mIiIiotKgV/HRy8srz5qKO3bs4Jo4hZDJZHhRaw0aNvCAyRu8dpVGrca1yGvw8fZhHpgH5uH/MQ8i5kHEPIiYB5FGrcbV67fhqueZgq/Shx9+iHfeeafIcdWqVdNr/vv37yMnJwd16tTBkSNHkJSUhIYNG8LLywsA8OjRIzg7O2utX+nq6gqNRoPo6OgS9+cuM/RvarUaarVar2MqSu68hpq/vGAeRMyDiHkQMQ8i5kHEPIiYB9GrzIOu+9Cr+Pjo0SPk5OTkaU9MTETNmjVhZmamz7SvP5kMMrMKb/QfUYKJGoKJJfPAPABgHnIxDyLmQcQ8iJgHkWCiBspB4REQC3Wurq4Gmz8lJQVmZmaYOnUqXF1doVarsXjxYkycOBFDhw5FRkYGLCwstLYxNzcHAGRkZJS4vyAlvUu4LiIjIw2+j/KAeRAxDyLmQcQ8iJgHEfMgYh5ExpQHvYqPgwcPRlxcXJ52mUyGkJAQODo6ljgwIiIiIqJcpqamyMnJwbBhw9CqVSsAQMuWLTF9+nQMHDgQ5ubmyMrK0tom97GFhUWJ+wvi4eEBKyurkh1cAdRqNSIjI+Ht7f1G3/WdeRAxDyLmQcQ8iJgHEfMgKmkeoqKi8M0332Dt2rVGkcfly5cjIkJc91smk8HW1ha1a9dG//79pbrb8uXLYWFhgZEjR0rbqdVqnDx5Eps3b8Z///tfODo6as0FAAqFAq6urujfv3+BV3foIj09XacvYvUqPu7fv1/r1MqUlBQEBgaicePGLDwSERERUanLvVy7Vq1aUlvDhg2RnZ2NuLg4uLq6IjY2FhqNBiYmJgDEu2ObmJigevXqeP78eYn6CyKXyw3+B8qr2Ed5wDyImAcR8yBiHkTMg4h5EOmbh4yMDFy8eBEmJiZGkcd79+7B1NQUH3/8MQAgOTkZQUFB2L9/P44cOQKFQoF79+7BysoqT7xZWVmIiIhATk4O5HJ5nrkyMzNx4cIF9O3bF5s3b4avr69eMeqaJ72Kj3Z2dlqPK1WqhJkzZ6JTp0545513YGqq17RERERERPmqVq0a3N3dcfbsWfTt2xcA8Pfff8Pc3BxOTk5wcHCATCbDH3/8AT8/PwDAvn378Pbbb8Pa2hpNmjQpUT8RERHRq+bk5ISOHTtKj318fNC1a1c8fPgQderUKdFcXbt2RWxsLLZs2aJ38VFXpVYllMlkSE9PR2pqKipWrFha0xIRERFROfDgwQNkZ2fDw8PDYPuYPXs2xo8fjzt37sDc3BxBQUGYPHkyzMzMpPUgP//8c/Tp0wdJSUk4evQoNm3aBACwtLQsUT8REdHrShAECNnpr3SfMoUVZMVY13rcuHEYMGAAzp07h3/++QfVq1fH6NGj4ezsDACYNGkS+vbtiw0bNqBly5b45JNPoFarsW3bNly8eBE5OTnw8vLC0KFDpS8VlUolVqxYgTt37qB69epo3bq11j4/++wzDB8+HE2bNgUA3Lx5E4sWLcKGDRsAiJc3b926FX/++Sc0Gg38/f3x3nvvARBzunPnToSFhUGlUqFx48YYPHiwdI+USZMmoX///ti3bx88PT0xdOhQnfJw7do12Nvb630Dv39zcnLKd1nF0qZX8XHjxo1QKpXSY41Gg7/++gumpqYsPBIRERG9gfbu3Yv4+HgsWLDAYPto3bo1duzYgWPHjkEmk2H16tVa39QPGjQIdevWxblz51CpUiXs27dP6yY4Je0nIiJ63QiCgAfz2yAjKuyV7teyTmvUnH1W5wLktWvXcPv2bYwePRrt27fHxo0b8dFHH+HIkSOQy+WIjIxEbGws3N3d4eXlBQD48ssvcePGDUyfPh3m5uZYvXo1hg0bhqCgIMhkMkyaNAkqlQqTJk1CQkICli9frrXPy5cvo0+fPtLjlJQUXLx4UXr81Vdf4datWxg3bhyysrKwcOFCJCYmYtiwYfjmm2/w559/YubMmbC0tMRPP/2EK1euYNmyZQDEm8E8efIErVu3Rr169Qo87rNnz2LIkCEAxJs8p6enY82aNVrrTb88BhCf0xcvXhSZ0xcvXuDMmTMYOHBgkWNLSq/iY0REBBITE6XHMpkMjo6OWLlyZakFRkRERETlR//+/bFgwQIcP34cjRs31rpJi0KhkL7pLyl3d3d89tlnBfY3adIETZo0MVg/ERHRa6cYZyCWpU6dOuH9998HANSuXRutWrXClStX0KRJE5iYmKB+/fro0aMHGjVqhJSUFOzevRubNm2Szlz85ptv0LZtW1y/fh0uLi44e/Ysdu3ahQYNGgAQi3FffPGFTrGkpKRg586dCAoKgo+PDwDAy8sLarUaqamp2Lp1KzZs2IBmzZoBABYuXIgOHTogOjoa1atXl+KdMGFCofupW7cuhg8fDgBIS0vDuXPnMHnyZKxbt076gtTNzQ0ffvihtI1Go8Hly5dx7949rbleLlJmZWXhzp07aNu2rc5nXZaEXsXHn376qbTjICIiIqJyLDAwECEhITh+/HievpkzZ76SD7ZERERUPDKZDDVnnzX6y64BseCYq3LlyrCystK6ZPjlpV+io6Oh0Wjg7u4utVWtWhVWVlaIjo6W9v3yTeXeeustnWPJnf/lKyRy/33jxg1oNBp8//33Wl++mpiY4MGDB9I+dbnLdJUqVdCqVSvp8TvvvINRo0Zh5cqV+Pbbb6Vj6N69uzRGrVZDrVZjy5YtWnN5eHjgk08+AQCYmZnhrbfegouLi87HXBJ6r/kYHx+PkydP4unTp3BwcEDbtm2L9UQRERER0etj4MCB6NKlS753PXR0dCyDiIiIiEgXMpkMMvMKZR1GkdLT/1cgFQQB2dnZqFDhf3G/XOizsbEBIJ4tmLs8oFqtRlZWFmxtbWFpaQlAvOtzrtTUVK39yWQyCIKQ7/5z51cqlXmWH7S1tQUAjBkzBlWqVNHqe3mtRn2vCnFycsLTp0+LvZ2joyPatWun1z5LykSfja5du4ZOnTph1apVCA8Px6ZNm9C9e3ccPXq0tOMjIiIionLAxcUFDRo0gJeXFzw9PVG3bl14eXnBy8sLlSpVKuvwiIiIqJw7ceIENBoNACAsLAwymQx169bNd6yLiwtcXV1x+PBhqe3IkSOwtLREvXr1UL16ddja2uLUqVMAxGLmvn37tOaoVKmSdOmyIAg4ePCg1vzVq1dHcHCw1Pbrr79i9erVcHFxQc2aNXHv3j3ps5CFhQU2b96stVajPmJjY3H69Olyt0SMXmc+rly5EhMnTtS6fCYkJARff/01OnfuXFqxEREREVE5cujQIfzyyy94+PAh5HI56tWrh5kzZ0prIRERERHpy87ODn369EGVKlUQERGB8ePHF3h1hVwux6JFizB16lScOXMGcrkc//zzDxYsWCCdqTh9+nR888032L9/P5KTk6X1GXMLnIMGDcLixYsRFhaGxMREdOvWTeqTy+VYuHAhpkyZglOnTiEtLQ1ZWVlYtWoVTExMsGTJEkyZMgWHDx+GjY0Nrl+/js8++wympsUrw50+fVpa5zIzMxPR0dHo2rUrhg0bplcOy4pexcf79+9j8eLFWm3t27eHqakpUlNTpdNPiYiIiOjNcOvWLXz55ZcYPnw4vLy8oFKpcOHCBYwePRqHDh2Cvb19WYdIRERE5dg777yDVq1aSesmOjg4SH1Lly6Fo6MjYmJipLamTZvixIkTePDgAdRqNWrWrKl1Q7y+ffsiICAAjx49gouLC2xtbdG1a1dpCZlBgwYhICAAT548kbb19fWFIAiQyWRo1qwZTp06hfv370MQBNSuXVsqLjZs2BBHjx7Fw4cPkZaWhlq1asHa2lor3qLWWxw/fjwGDRokPba0tISrq6vWZd7jx4+HiUnei5qrVKmC9evXS8XZgsa9KnoVH+3s7PD06VPpOnYAyM7ORnp6unTdPBERERG9OUJDQ/HJJ59o3Yk6ICAACQkJiIiIQEBAQBlGR0REROWdIAiwt7dHo0aN8vQ1bNgQarVaq/gIiOsq1qlTp8A5K1asqFXMa968uVa/o6Oj1tmV/+5XKBQF3jjG1NRU64Y3/463KIXFXdQYCwsLNGrUSCqk6jKXIelVfPTz88OUKVMwYsQIVKtWDYmJiQgKCoKvr2+xTyElIiIiovIvNTVVaxH1XNWrV0diYmIZRERERERExkCvSuGwYcOgVCqxZMkSvHjxAjY2NvD398eMGTNKOz4iIiIiKgdq166N7du3o2fPntKVMElJSTh27BgWLlxYxtERERFRebZs2TLUqFGjrMMgPelVfDQzM8PUqVMxdepUZGdnQ6FQlHZcRERERFSOdO3aFZs3b0b79u1Ru3ZtaDQa3Lp1C/7+/vleHkVERESkK19f37IOgUpA72ukk5KSEB0dDZVKpdVer149FiOJiIiI3jCmpqbYsmULjh07hqioKMjlcowdOxZt27Yt69CIiIiIqAzpVXzcs2cP5syZg5ycnDx9ISEhcHJyKnFgRERERFS+yOVydO3atazDICIiIiIjolfxcePGjZg+fTr69euX5wYzvOEMERERERERERERAXoWH9PT09GtWzdYWFiUdjxERERERERERET0mjDRZ6OOHTviwoULpR0LERERERERERERvUZ0PvNxxYoVSE1NBQDIZDJ8++23OHnyJBwdHbXGjRkzBjY2NqUbJREREREREREREZU7OhcfY2JikJiYKD1u2LAhMjMz8fDhQ61x/777NRERERG9GSIiIvDzzz/j5s2bmDFjBipVqgS1Wg0/P7+yDo2IiIiIyojOxccFCxZI/1ar1ZDL5XnGPHnyBBUrViydyIiIiIio3Hj06BHGjRuHDz/8EBYWFhAEARUqVMCYMWOwf//+PFfLEBEREdGbQe81H/PTt29fZGdnlyggIiIiIip/Tp06hQEDBmDSpElwdXUFADRt2hRdunTB+fPnyzg6IiIiIiorxbrb9fz585GUlISUlBRMmzZNq0+pVCIxMRHZ2dlQKBSlGiQRERERGbekpCRUq1YtT7uNjQ1ycnLKICIiIiIiMgbFOvOxW7du8Pb2hkwmg5WVldZ/Li4u+PHHH2FtbW2oWImIiIjISHl4eGDPnj1QKpVSW3JyMk6cOAEPD48yjIyIiIiIypLOZz4+ffoUjRs3RuPGjWFlZYW+ffsWOj4+Ph6VKlXKd21IIiIiInq9dO7cGdu2bUPHjh2hUCjwxx9/4Ntvv0VAQAB8fHzKOjwiIiIiKiM6Fx8HDhyIU6dOAUCRhUcAmDBhAn744Qc4OTnpHx0RERERlQtyuRzr16/HiRMncOvWLZiZmcHHxwetW7cu69CIiIiIqAzpXHzUaDRYunSpzhPHxsbqFRARERERlT8hISFwcXFBp06d0KlTJ6l9z5498PT0RL169cowOiIiIiIqKzoXHzt27IiYmBidJ27cuDEsLCz0CoqIiIiIyoeEhASkpqbiyJEjqF+/vtaNBzUaDQ4dOoTs7GwWH4mIiIjeUDoXH+fOnWvIOIiIiIioHDp69Ch+++03xMTEYPfu3Xn6K1SogKlTp5ZBZERERERkDHQuPhIRERER/duAAQMwYMAALF26FLVr19Za41Emk8HW1lbrbEgiIiIierOw+EhEREREJTZt2rR828PDw2Fubo7GjRu/4oiIiIiIyBiw+EhEREREpeLWrVs4deoU0tPTAQBqtRqhoaEYMmQIi49EREREbygWH4mIiIioxO7evYsPPvgAXl5eSEhIgL29PZ48eYL27dujc+fOZR0eEREREZURE303zMrKwpEjR/Drr78iOzsb8fHxpRkXEREREZUjJ0+exMCBA7Fjxw506tQJY8eOxZ49e/DkyRNYWFiUdXhEREREVEb0Kj4+fvwY77zzDubPn4+ffvoJOTk5mDhxItavX1/K4RERERFReZCSkgJ3d3cAgIWFBTIzM+Ho6IgaNWogJCSkjKMjIiIiorKiV/Hx+++/R//+/REaGopKlSoBAL799lv89ttvEAShVAMkIiIiIuNXo0YNnD59GmlpaXB0dERkZCQAwMzMDImJiWUcHRERERGVFb2Kj7du3cKQIUO02mrWrAkbGxskJSWVRlxEREREVI5069YNjx8/xqFDh9ChQwds3boVH374IYKCgtCwYcOyDo+IiIiIyoheN5yxs7NDbGwsbG1tpbbMzEwkJydrtRERERHRm8Ha2hrBwcHIyMiAjY0NNm7ciFOnTmH8+PGoW7duWYdHRERERGVEr+Jj586dMXHiRIwePRrZ2dk4c+YM9uzZgxYtWkAul5d2jERERERUDpiamsLGxgYA4OPjAx8fHwBAUlIS7O3tyzAyIiIiIiorel12/dFHH6FHjx5YunQpEhMTMXfuXFSuXBlz584t7fiIiIiIyIhdvHgRgwcPRseOHTF69GjcvXtX6ouPj8eYMWOwd+/esguQiIiIiMqUXsVHABg/fjzOnTuHyMhI/Pnnn1i4cCEyMjJKMzYiIiIiMmJPnz7FiBEj4OzsjKFDh0KtVmPo0KHIzMzE3r170b17dzx//hzt27cv61CJiIiIqIzoddl1x44dcebMGQCAQqGQ2vv27YtTp05ptRERERHR6+nYsWNo1qwZ/vvf/wIQr455//330adPHzx58gSTJ0/GoEGDYGKi9/fdRERERFTOFav4OH/+fCQlJSElJQXTpk3T6lMqlUhMTER2djaLj0RERERvgCdPnsDb21t6LJPJ0KhRI9y5cwdr166Fo6NjGUZHRERERMagWF9Dd+vWDd7e3pDJZLCystL6z8XFBT/++COsra0NFSsRERERGRGVSgVTU+3vsk1NTdGuXTsWHomIiIgIQDHPfGzcuDEaN24MKysr9O3bN09/fHx8qQVGRERERERERERE5Zteaz7mFh5zcnKg0WgAAGlpaRgxYgQ2btwIOzu70ouQiIiIiIzW/fv3cfr0aelxdHQ0UlNTtdpq164NV1fXsgiPiIiIiMqYXsXHqKgoTJ48Gbdv39Zqd3JygpWVVakERkRERETGb9++fdi3b1+e9j179kj/njlzJoYOHfoKoyIiIiIiY6FX8fGHH35AgwYNMHfuXEybNg1Lly7F2rVr8dlnn8HMzKy0YyQiIiIiIzR79mzMnj27TPatVquRmZmJChUq5NtnYmICmUxW4LYl6SciIiIi3RXrhjO5oqKiMHXqVDRt2hTm5uZo2rQpFixYgC+++KK04yMiIiIiymPOnDlo3LgxVCqV1BYbG4uhQ4fCx8cHDRs2xIwZM5CVlVVq/URERERUfHoVH83NzZGWlgYAsLa2RnJyMipWrIiMjAwkJSWVZnxERERERFouXLiAsLCwPO2TJk1C5cqVER4ejmPHjuHWrVv44YcfSq2fiIiIiIpPr+Jj69atMWHCBKSlpcHT0xPfffcdjh07hmfPnnHNRyIiIiIymKysLMydOxfjxo3Tar9z5w6uXbuGL774Ara2tnBycsKkSZOwe/duaDSaEvcTERERkX70WvNx0qRJAACFQoGRI0dixIgR2LVrF8aMGQOFQlGa8RERERERSVasWIFGjRrh7bff1mq/ceMGXFxcULFiRamtXr16SE5ORkxMTIn7ebduIiIiIv3oVXw0NzfH559/DgBwc3PDyZMnoVKpYGqq13REREREREW6ffs2du7cif379yM1NVWrLykpCXZ2dlpt9vb2AIDExMQS9xdUfFSr1VCr1XoeUeFy5zXU/OUF8yBiHkTMg4h5EDEPIuZBxDyIXmUedN2HXtXCCRMmYOTIkfD29v7fRCw8EhEREZGBaDQazJkzBxMnTkSlSpXyFB9lMlmey6NfflzS/oLcvn1b52PQV2RkpMH3UR4wDyLmQcQ8iJgHEfMgYh5EzIPImPKgV8VQo9Hg0qVLWsVHIiIiIiJD2bp1KwRBQLdu3ZCWloaMjAwAQEZGBiwtLeHg4ICEhAStbV68eAEAqFKlSon7C+Lh4WGwNc/VajUiIyPh7e0NuVxukH2UB8yDiHkQMQ8i5kHEPIiYBxHzIHqVeUhPT9fpi1i9io/NmjXDli1bEB4ejho1amid9ThmzBjY2NjoMy0RERERUb6uX7+Ohw8fwt/fHwAgCAIAICAgAF9++SV8fHzw7NkzPHnyBM7OzgCAS5cuwcnJCc7OzlCpVCXqL4hcLjf4B/tXsY/ygHkQMQ8i5kHEPIiYBxHzIGIeRK/qM4ou9Co+Xr9+HW5ubpDJZIiJidHqU6lU+kxJRERERFSghQsXaj1++PAhOnXqhHPnzklfhHfo0AGzZ8/Gl19+iaSkJHz33XcYOnQoAMDV1bVE/URERESkH72Kj0uWLCmVncfGxuLhw4dwcHBAnTp1it0fExODBw8ewNnZGbVq1Sp2PxERERGVTyYmJrCysoJMJpPalixZggULFmDgwIGwsLBA3759tYqHJe0nIiIiouIrk7vECIKAr776CgcOHEDdunVx79491KpVC2vWrIGlpWWR/QCwdOlSBAYGom7durh79y7at2+PxYsXw8TERKd+IiIiIiq/XF1d8ddff2m12dnZFfoleUn7iYiIiKj4yqQSd/ToURw6dAgHDhxAYGAgjh8/jujoaGzfvl2n/kuXLiEwMBA7d+7E1q1bcfDgQYSHh+Pw4cM69RMREREREREREZHhlUnxsV69eli1apW0eLe1tTXq1auHR48e6dR/7NgxtG3bFu7u7gDEOxD26NEDR48e1amfiIiIiIiIiIiIDK9Mio81atRAkyZNpMepqamIjIyU2orqv3v3Ltzc3LTmdHNzQ1RUlE79REREREREREREZHh6rfmYmZmJ1atXY+LEiUhOTsacOXOQmJiImTNnol69esWaKysrC1OmTEG9evXQpUsXnfqVSiWsrKy0xllZWUGpVOrUXxC1Wg21Wl2s+HWVO6+h5i8vmAcR8yBiHkTMg4h5EDEPIuZB9Kry8KbnmYiIiIgMR6/i49KlS3Hr1i2o1WqsWLECKSkpaNq0KWbOnIng4GCd50lMTMSYMWNgZ2eHZcuW5bkZTEH9CoUCOTk5WmNzcnKgUCh06i/I7du3dY5dX5GRkQbfR3nAPIiYBxHzIGIeRMyDiHkQMQ8i5oGIiIiIyiu9io/nz5/HL7/8ArlcjmPHjmHlypWoW7cudu7ciYyMDOmO1IV5/vw5Bg0ahBYtWuDLL7+EXC7Xud/Z2RlPnz7VGh8bGwsXFxed+gvi4eGR54zJ0qJWqxEZGQlvb+88x/omYR5EzIOIeRAxDyLmQcQ8iJgH0avKQ3p6+iv5EpaIiIiI3jx6FR9zcnJga2uLx48fIzMzE56engAACwsLZGZmFll8VKlUGDVqFDp06ICZM2cWu7958+b45ZdfkJOTAzMzMwiCgJCQEPj7++vUXxC5XG7wP3BexT7KA+ZBxDyImAcR8yBiHkTMg4h5EBk6D8wxERERERmKXsXHWrVqISgoCPfv30eHDh0gk8lw584dpKamwt7evsjtt23bhsePH+OTTz7BwYMHpfZKlSqhZcuWRfb36NEDa9aswbhx49CtWzecOXMG8fHxGDBgAAAU2U9ERERERERERESGp1fxccKECZg0aRIsLS2xcuVKAMCoUaMwevRoyGSyIrdXqVRo3LgxDhw4oNXu4eGBli1bFtmvUCgQGBiI9evX48SJE6hevTqCgoJgZ2cHAEX2ExERERERERERkeHpVXz08vLC0aNHtdp27NgBQRB02n7o0KEYOnSo3v0AULFiRUyePFnvfiIiIiIiIiIiIjIsk6KHFCwnJwdZWVnIysqCiYkJRowYgeTk5NKKjYiIiIiIiIiIiMoxvc58jIqKwuTJk/PcFdHJyclgd4smIiIiIiIiIiKi8kWvMx9/+OEHNGjQAIGBgXB2dkZgYCD8/f3x888/w8zMrLRjJCIiIiIiIiIionJI7zMfAwMD4eDgAHNzczRt2hTu7u4YOnQogoODSztGIiIiIiIiIiIiKof0OvPR3NwcaWlpAABra2skJyejYsWKyMjIQFJSUmnGR0REREREREREROWUXsXH1q1bY8KECUhLS4Onpye+++47HDt2DM+ePeOaj0RERERERERERARAz8uuJ02aBABQKBQYOXIkRowYgV27dmHMmDFQKBSlGR8RERERERERERGVU3oVH83NzfH5558DANzc3HDy5EmoVCqYmuo1HREREREREREREb2GSlQtjIqKQk5ODgCgUqVKcHR0LJWgiIiIiIiIiIiIqPwrVvHx/Pnz2LhxI1asWAEAGDZsGOLi4gAAlStXRnBwMKpUqVL6URIREREREREREVG5o/MNZ/7++2+MGjUKdnZ20tmOALB9+3b8/fff8PX1xa+//mqQIImIiIiIiIiIiKj80bn4uH79enTr1g0LFy6EmZnZ/yYwMYGpqSlGjhyJI0eOGCRIIiIiIiIiIiIiKn90Lj7eunULvXv31mqrUqWKVIj09vbG8+fPoVQqSzVAIiIiIiIiIiIiKp90XvMxIyMD1tbWWm27du2S/i2TyWBiYgKNRlN60REREREREREREVG5pfOZj9WqVcPNmzcL7L9y5Qqsra1ha2tbKoERERERERERERFR+aZz8bFLly5YuXIlYmJi8vQ9e/YM8+bNQ69evUo1OCIiIiIiIiIiIiq/dL7sum/fvjhx4gS6du2K7t27o06dOjAxMcG9e/ewf/9+uLm5YeLEiYaMlYiIiIiIiIiIiMoRnYuPpqamWL16NbZt24aDBw/ijz/+gEwmg6urK8aNG4chQ4ZAoVAYMlYiIiIiIiIiIiIqR3QuPgJiAXLw4MEYPHiwoeIhIiIiIiIiIiKi14TOaz4SERERERERERERFQeLj0RERERERERERGQQLD4SERERERERERGRQbD4SERERERERERERAbB4iMREREREREREREZBIuPREREREREREREZBAsPhIREREREREREZFBsPhIREREREREREREBsHiIxERERERERERERkEi49ERERERERERERkECw+EhERERERERERkUGw+EhEREREREREREQGweIjERERERERERERGQSLj0RERERERERERGQQLD4SERERERERERGRQZiWdQBERERERLoQBAHHjx/HxYsXYWpqitatW6NNmzZaY06cOIFz587B3Nwc3bp1g4+PT6n2ExEREVHx8MxHIiIiIioX5syZg0WLFqFq1aqwsLDAhAkTsH79eql/2bJl+Oqrr+Do6AgzMzMMHjwYISEhpdZPRERERMXHMx+JiIiIyOg9f/4c+/btQ1BQELy8vAAAlpaW2L59O4YOHYqUlBSsWbMG69atQ9OmTQEAtra2+PHHH9G+ffsS9xMRERGRfnjmIxEREREZPQcHB1y7dk0qPAJAxYoVkZmZCQC4fPkyLCwspMIhAPj5+eHGjRtISkoqcT8RERER6YdnPhIRERFRuZOVlYXNmzejV69eAIDY2Fg4OTlpjcl9HBsbW+J+e3v7fONQq9VQq9UlPp6C5n75/28q5kHEPIiYBxHzIGIeRMyDiHkQvco86LoPFh+JiIiIqFxJT0/HpEmTULlyZYwZMwYAkJ2dDTMzM61xuY+zs7NL3F+Q27dvl+xgdBAZGWnwfZQHzIOIeRAxDyLmQcQ8iJgHEfMgMqY8sPhIREREROXGs2fPMHr0aLi6umLJkiVSgbBChQpIS0vTGpv72NrausT9BfHw8ICVlVXJDqoAarUakZGR8Pb2hlwuN8g+ygPmQcQ8iJgHEfMgYh5EzIOIeRC9yjykp6fr9EUsi49EREREVC4kJCTgo48+Qrt27TBz5kzIZDKpz83NDbGxscjOzoZCoQAA3Lt3DwqFAq6urkhKSipRf0HkcrnBP9i/in2UB8yDiHkQMQ8i5kHEPIiYBxHzIHpVn1F0wRvOEBEREVG5MGPGDLz99tuYNWuWVuERABo2bAg7Ozvs3LlTatu8eTM6duwIc3PzEvcTERERkX545iMRERERGb2//voLISEhaNOmDSZMmKDV9+2338La2hrz58/H1KlTcfLkSSQlJSExMRGbNm0CIK7fWJJ+IiIiItIPi49EREREZPScnZ2xcOHCfPtyL5Pu0KEDjhw5goiICFhYWKBFixawtLSUxpW0n4iIiIiKj8VHIiIiIjJ6Tk5OeP/994scV6VKFXTt2tVg/URERERUPFzzkYiIiIiIiIiIiAyCxUciIiIiIiIiIiIyCBYfiYiIiIiIiIiIyCBYfCQiIiIiIiIiIiKDYPGRiIiIiIiIiIiIDILFRyIiIiIiIiIiIjIIFh+JiIiIiIiIiIjIIFh8JCIiIiIiIiIiIoNg8ZGIiIiIiIiIiIgMgsVHIiIiIiIiIiIiMggWH4mIiIiIiIiIiMggWHwkIiIiIiIiIiIig2DxkYiIiIiIiIiIiAyCxUciIiIiIiIiIiIyCBYfiYiIiIiIiIiIyCBMy2rHWVlZOHjwIB48eIDKlSuje/fucHBw0BqTkpKCNWvWoE2bNmjWrJnUHhcXhzVr1uSZ84MPPoCnpycAIDs7G/v27cP9+/fh4uKCd999FzY2NoY9KCIiIiIiIiIiIpKUyZmPSqUSH3zwAbZv3w6FQoGwsDB07doV0dHR0pi7d+9i0KBBCAwMxI0bN7S2j46OxrZt21CxYkWt/xQKBQBApVLho48+QmBgICwsLHD06FH07dsXSqXylR4nERERERERERHRm6xMznwMDg5Geno6du3aJRUMe/Xqhb1792LcuHEAgLFjx2LRokWYO3dunu2VSiXs7OwwZsyYfOc/duwYYmNjcfjwYVSoUAEqlQp9+/ZFUFAQhg8fbrgDIyIiIiIiIiIiIkmZnPk4aNAgHDp0SCo8AoCjoyNSU1Olx+vWrUOjRo3y3V6pVMLa2rrA+c+dO4fWrVujQoUKAABTU1P4+fkhNDS0dA6AiIiIiIiIiIiIilRmaz6am5tL/46KisKFCxe0zmR0dnYucFulUgkrKyucOnUKV69ehb29PTp16oRq1aoBAGJiYtCkSROtbVxcXLBv375CY1Kr1VCr1focTpFy5zXU/OUF8yBiHkTMg4h5EDEPIuZBxDyIXlUe3vQ8ExEREZHhlFnxMdeDBw/w6aefYvTo0QWe6fhvSqUSN27cwK5du+Dt7Y1Lly5h2bJlWLduHXx9fZGZmal1ViUAKBQKZGVlFTrv7du39T0MnUVGRhp8H+UB8yBiHkTMg4h5EDEPIuZBxDyImAciIiIiKq/KtPh46dIljB8/HiNHjsQnn3yi83b9+vVDjx494OjoKLVNnToVP//8M37//XdUqFAB6enpWttkZGTAysqq0Hk9PDyKHKMvtVqNyMhIeHt7Qy6XG2Qf5QHzIGIeRMyDiHkQMQ8i5kHEPIheVR7S09NfyZewRERERPTmKbPiY0REBD777DPMmzcPXbt2Lda2NjY2sLGx0WqrV68e9u7dCwB466238PjxY63++/fvo1atWoXOK5fLDf4HzqvYR3nAPIiYBxHzIGIeRMyDiHkQMQ8iQ+eBOSYiIiIiQymTG84kJiZi7NixmD9/frELjwCwaNEijBw5EoIgAAA0Gg3OnDmDevXqAQD8/Pxw9uxZxMXFAQBSUlJw+PBhdOrUqfQOgoiIiIiIiIiIiApVJmc+/v7771LB8MyZM1K7m5sbRowYgbt372Lt2rUAgKdPn+Lw4cO4c+cOatSogU8//RQDBw7EoEGD0K9fP3h5eeHatWvIyMjAkiVLAABt2rRBu3bt0K9fP7Rs2RKXL19G7dq10bNnz7I4XCIiIiIiIiIiojdSmRQfW7RoAQcHhzztTk5OAAArKyvUqVMHAKT/A0DVqlUBADVq1MDx48cRGhqK58+fIyAgAC1atNC6ycx3332H8+fP4969e+jWrRvatGkDE5MyOdGTiIiIiIiIiIjojVQmxcc2bdqgTZs2BfY7Oztj6NChhc5hYWGBgICAQse0aNECLVq00CdEIiIiIiIiIiIiKiGeCkhEREREREREREQGweIjERERERERERERGQSLj0RERERERERERGQQLD4SERERERERERGRQbD4SERERERERERERAbB4iMREREREREREREZBIuPREREREREREREZBAsPhIREREREREREZFBsPhIREREREREREREBsHiIxERERERERERERkEi49ERERERERERERkECw+EhERERERERERkUGw+EhEREREREREREQGYVrWARARERERUQHUKmQ+ugpTSxuYWFhDZiIHZCZ5/g/Z/59TIJNBJpMBkAEymdSW+1iW20ZERET0irD4SERERERkpBRHZ+Nh1EnDTC4VIvMpVAJiofLlMf/q1y5m/nsO7W1k+FdfvvMWMBYyWOTk4K5Cke+2sqLm/NdjWSH7/vex6BR3QWN1POYC48nzGFCkpeHREWtxXzrkUp+4DJLPwuIpZvyCAJglJuBJROV8X6MGj1/X58vAcQmCANOncXj+wAkyE5MS57WouAp+PyjoWA2Tz3/HrREEyB89QnJaJExMTLS2LzLnxRlb0Ptekceo4/NfnLEvv0f9///VGg1MYu8gwzodJiby/Mca8P2sOLnWGl9IHnR6jfxrfo1GA6Q9hyo5DoI8/zwUeozFOb7ivKaLHPv6Y/GRiIiIiMhIqas1hvmLf6DJTIEmUwkImtKbXBBy/wEI+XTrMkXpRVMoGQBV2ivamRGTA8go6yCMgCmAlLIOwgiYAXhR1kEYAQWAp2UdhBEwB/CorIMwApYA7pZ1EPrSoXAvK6RQ+/Jjc4tKyHENgbyqm4GD1g2Lj0RERERE/0+j0eD+/ftQKBRwdXUt63Cgbtgf7h8vhFwuByCe7QRBAAQNBI1aLEZq1GI7cvv+928ht03c+F9jkE8//jcm99+5+315jpfHv9Qm/Gu7l/f17+10HavWqPHPrVvw9PQUz2wqNI7/P6aCjqfQ48obo04x6zO+iO3zi0t8bd6DW003mJjIXmlceXJaxPZF5rWg8TrsR6PRIDY2Bi4uLv/7I7y04i/sNaRL/AZ6HeW3H0EQ8Dw+Hg4ODv+rO+T3s1jYnPq+fkozn8XKZT55FASkpKTAxtYG0jlk+Y0tLObivF6KE2+x8leM3OWzT0EAsrIyYW5ubrDXoaDDseUf96v9+REEQfyZkMaVI1r5KmCIjlPJcrKhTk8qhaBKB4uPREREREQAIiMjMWHCBOTk5CArKwtubm5YtWoVKlWqVNahSf536aMJZPI346O8Wq2GkABYvNVIKsK+idRqNTTyK7BpxDw8unIFlZgHPLlyBY7MA65cuQJX5gFXrlyBF/OAK1euoNG/8qDbFyIGKtwXp2ivc6E3v1j/92+1Wo0b96Jh4eoDY/FmfGIhIiIiIiqERqPBtGnT8O6772Ly5MlQqVT49NNPsWjRIixZsqSswyMiIiI95V2zsZCxBo7lVVCr1UCscS1OYVLWARARERERlbXr168jOjoao0ePBgCYmppi1KhROHbsGHJycso4OiIiIqLyi8VHIiIiInrjRUVFoVq1arC0tJTa6tSpg4yMDDx+/LgMIyMiIiIq33jZNRERERG98VJSUlChQgWtNmtra6mvIGq1Wry8yQBy5zXU/OUF8yBiHkTMg4h5EDEPIuZBxDyIXmUedN0Hi49ERERE9MYzNTXN8wFapVJJfQW5ffu2QeMCxBvhEPOQi3kQMQ8i5kHEPIiYBxHzIDKmPLD4SERERERvPEdHRzx79kyrLS4uDgDg7Oxc4HYeHh6wsrIySExqtRqRkZHw9vZ+4+9eyjwwD7mYBxHzIGIeRMyDiHkQvco8pKen6/RFLIuPRERERPTG8/X1RXJyMm7fvg0PDw8AQFhYGGrVqoXKlSsXuJ1cLjf4B/tXsY/ygHkQMQ8i5kHEPIiYBxHzIGIeRK/qM4ouWHwkIiIiojeeg4MDPvjgA0ydOhWTJ09GUlISvvvuO3zzzTdlHRoRERFRucbiIxERERERgDlz5mDt2rVYu3YtzM3NMX/+fHTr1q2swyIiIiIq11h8JCIiIiICoFAoMHr0aIwePbqsQyEiIiJ6bZiUdQBERERERERERET0emLxkYiIiIiIiIiIiAyCxUciIiIiIiIiIiIyCK75CECj0QAAMjIyDLYPtVoNAEhPT3+jb/nOPIiYBxHzIGIeRMyDiHkQMQ+iV5WH3M9AuZ+JqGj8/PjqMA8i5kHEPIiYBxHzIGIeRMyD6FXmQdfPkDJBEASDRlIOvHjxAg8ePCjrMIiIiIjKVM2aNVG5cuWyDqNc4OdHIiIiIlFRnyFZfASgUqmQnJwMc3NzmJjwSnQiIiJ6s2g0GmRlZcHOzg6mprwwRhf8/EhERERvOl0/Q7L4SERERERERERERAbBr2mJiIiIiIiIiIjIIFh8JCIiIiIiIiIiIoPgoj4lcO3aNRw/fhwqlQrt27dHixYtSjS+uPMZiytXruDEiRNQq9Xo2LEjmjVrVuj4y5cv48yZM1CpVPD19YW/v7/Ut2nTJty5c0drvKurK0aOHGmQ2EvTpUuXcOrUKQiCAD8/PzRt2rTAsevWrcP9+/e12tzc3PDJJ5/oNZ8xiYiIwOnTpwEA/v7+aNy4cb7jnj59ihUrVuTbN3bsWDg6OuL333/Hw4cPtfrc3d3x8ccfl27QBhIUFITnz59j7NixRY49f/48QkJCIJfL8c4776Bhw4bF6jdmW7duRVJSEj777LMix4aFheH8+fOQyWRo3rw5WrVqJfWtXLkST5480Rrv5eWFAQMGlHrMhhAYGAilUolPP/200HE///wznj17ptVWv3599O/fX3p87tw5hIaGwtTUFJ07d0aDBg0MEnNpEwQBGzduhEqlwvDhwwscFxUVhY0bN+bbN2PGDFhZWWHZsmV48eKFVl/Dhg3Rp0+fUo3ZEP744w9ERETAzMwMrVu3LvL9PSQkBOHh4TA3N0fXrl1Rt27dYvWTcXnx4gV2796NuLg41K5dG++//z4UCoXe44s7n7GIj4/Hnj17EB8fDw8PD/Tu3RtmZmaFjg8ODkZ8fDxq1KiB3r17o0KFCgDEz0zBwcF5tpk2bRpsbW0NdgylIS4uTjquovJw8eJF7N+/P0/79OnTYW1tXez5jMnTp0+xd+9evHjxAl5eXujVq1eBd2jN7/0fADp27IiOHTsiPDwchw8fztM/a9YsWFhYlHrspe369evYsWMHxowZA0dHx0LHxsbGIjg4GAkJCahfvz569uyplbei+o3Z1atXsWvXLkyaNAmVKlUqdOzjx49x8OBBJCUlwd3dHb169ZLeB0NCQnDy5Mk823z11VflYr3ey5cvY+/evZg8eTIqVqxY4LjTp09Lf4O9bN68edK/Hz9+jODgYKSkpMDb2xs9evSATCYzSNyl7c8//8SBAwe03u/ys2DBAmRlZeVpf/fdd9G0aVMcP34cZ8+e1eozMzPDnDlzSj3m0nbv3j0cOXIEqamp8PDwQI8ePQp9f79//z4OHDiA1NRUNG7cGF26dClWf2kz/p82I3X48GEMGTIEKpUK5ubmGD9+PLZs2aL3+OLOZywOHDiAoUOHQq1WQ6FQYMyYMdi+fXuB49evX49Ro0ZBrVbD3NwcX3zxBRYuXCj1Hzp0CElJSXjrrbek/5ycnF7FoZTI7t27MWLECMhkMpiammLkyJHYu3dvgeP379+P1NRUreN8+cNFceczFjt27MCoUaNgYmICExMTDB8+PN8PyQCgUCi0jv+tt96CpaUl9uzZI30Q2LdvH9LS0rTGVK1a9VUekl6ysrIwffp0/PLLL/l++P23zZs3Y9y4cVAoFNBoNPj4449x9OhRnfuNVWZmJqZOnYqVK1fiyJEjRY5funQppk2bBjMzM2g0GowbNw6//fab1L97927k5ORovR6qVKliyEMoFRkZGZg8eTJWr16t0/O2e/duqFSqAo9z7dq1mDJlChQKBXJycjBo0KB8P2wam7S0NEyYMAG///47Tpw4UehYCwuLPO8PGo0GR44ckd4fcn/XvDzGwcHB4MdRUl9++SXmzp0LS0tLZGZmYsSIEdi2bVuB43/++WfMnDkTVlZWSEtLQ79+/RAeHq5zPxmX58+f47333sOlS5dQtWpVbN++HcOHD0dBS7AXNb648xmLuLg49O7dG1evXkWVKlUQGBiI0aNHFzj+zp076NKlC27evAkHBwfs378fffr0QUZGBgAgMjISFy5cyPO+YexFlidPnqB3796IjIyEg4MDNm3ahHHjxhU4/tq1a/jzzz8LPM7izmcsHj9+jF69euHWrVtwcHDA77//jkmTJhU43sXFJU8Ojh49iufPnwMA/vrrL1y+fDnPmPJQaNq3bx+mTJmCbdu2ITk5udCxDx8+RK9evXDnzh1UrlwZv/76K6ZOnapzvzHbtWsXpk6diqCgIKSmphY69tKlS+jZsyeio6NRqVIlbNy4EUOGDIFarQYgFu2vX7+e5/VQHopuO3bswH/+8x8EBQUhLS2t0LF//vkn/v777zzHmev27dt499138fjxY1SsWBHLly/H7NmzDX0IpSIwMBAzZsxAUFCQ9L5fEFdXV63jr169OoKDg6XXUXh4OP755x+tMa6urq/iMErkzJkzeO+99xAfHw97e3usWrUKo0ePLvD3fWRkJHr37o2nT5/C3t4eS5Yswfz583XuNwiB9OLv7y9s2bJFenz06FGhWbNmQnZ2tl7jizufsejQoYOwfft26fHBgweF5s2bCzk5OXnGqlQqoVmzZsKBAwe0xvv4+AhqtVoQBEHo0aOHsGfPHoPHXZrUarXQunVrYe/evVLbnj17hDZt2ggajSbfbTp16iQcOnSo1OYzBiqVSmjRooXW87tjxw6hffv2Osc9btw4YdmyZdJjPz8/4dixY6Ueq6EtWrRI+Pnnn4VNmzYJ3bt3L3RsVlaW0LRpU+H48eNS2+bNm4V33nlHp35jNn/+fGHVqlXCunXrhHfffbfQsSkpKUL9+vWF8+fPS20bNmwQOnToID1u0aKFEBoaarB4DWXevHnCr7/+KqxZs0Z47733ihz/9ttvC+Hh4fn2paenC40aNRJCQkKktrVr1wo9evQotXgN5YsvvhDWr18v/PLLL8KHH35YrG01Go3w4YcfCoGBgVKbj4+PcPny5dIO06CePn0q1K1bV7hx44bUtnz5cqFnz575jk9KShIaNGggXLx4UWt8v379dOon4/Pdd98JgwcPln4vpqamCs2bNxdOnz6t1/jizmcsvv32W2H48OHS46SkJKFJkyZCWFhYvuNnzZoljB8/XnqsVCqFhg0bCidPnhQEQXzdjx492rBBG8A333wjjBo1SnqckJAg+Pr6ChcuXMh3/I8//iiMGzeu1OYzFl988YXW8xsfHy/4+PgIV65c0Wn7/fv3Cz179hRUKpUgCIKwePFiYcqUKQaJ1ZCuXr0qDB48WLh7967g4eEh/PPPP4WOnzFjhjBp0iTp8dOnT4UGDRoIkZGROvUbqz///FMYOnSo8M8//wgeHh7CgwcPCh0/atQo4euvv5YeP336VPDw8BCuXr0qCIIgzJ07V5gzZ45BYzaEsLAwYdiwYcLNmzcFDw8P4fHjx4WO/+KLL4SvvvqqwP4JEyYIs2bNkh4/evRIqFevnhAVFVVqMRvCH3/8IXz66afCtWvXBA8PD+HZs2fF2n7dunXCRx99JD3+z3/+IyxcuLC0wzS4Dz/8UPj++++lx3fu3BE8PDyE+/fv5zt+xIgRwvz586XHt2/fFry8vISYmBid+g3B+L/+MUKPHz/G48ePtS4X7tChA5RKJf7+++9ijy/ufMbi/v37iI2N1Yrbz88PycnJuHnzZp7xcrkc58+fR7du3aS2KlWqICcnBzk5OQDEM2MKO43aGN2+fRvx8fFaefD398ezZ8/yXEKeS6lUSpcKlcZ8xuDWrVtITEyEn5+f1BYQEIAnT57g3r17RW4fHh6Oa9euYdSoUVJbYXkyZsOGDdPpUmtAvLQmIyMD7dq1k9oCAgLw8OFDREdHF9lvzEaNGlXkJca5bGxscO3aNTRv3lxqc3BwQHp6uvS4vL4eRo8eXaylI9LS0go8zitXrkCj0aB169ZSW0BAgPS+YcwmTJig95IJ+/btg1KplC49V6lUyMzMLHevB0dHR1y/fh1eXl5SW5UqVbRe5y+LiIiAlZWV1mXZAQEBuHr1KpRKZZH9ZHzCwsLg7+8vnXFjbW2N5s2b49y5c3qNL+58xiIsLEzr84KdnR2aNm1aYNwLFizADz/8ID2uUKECrKyspJ+dwt43jVnu85erYsWKaNKkSYF5KOr3YHHnMxb/fj04ODigYcOGOsWdkZGBJUuWYNasWdIZoOX184KrqyvWrl0Le3t7ncaHh4drPd+Ojo7w8fFBWFiYTv3Gyt3dHb/99pvOSyasXr1a6wy+ihUrQi6XS+8P5fX14OnpiV9//VXnv42LOs7z589rvR5cXV3h4eFh9FdLNGjQACtWrICVlVWxt33x4gVWrFih9foor6+HrVu3YuLEidLj3Kt98vsMqdFocOHCBa3nu06dOnB1dcX58+eL7DcUrvmoh5iYGJiammpd/qlQKFCpUiXExMSgUaNGxRqfmZlZrPmMRWxsLMzNzbXW4LCwsEDFihURExMDb2/vPNv8+/T2DRs2ICAgAObm5gDEN4OEhASsWLECqamp8Pb2RpcuXYz6MonY2FjY2tpq/WKwsbGBtbU1YmJi4OHhkWcbpVKJ58+f4+eff0Z6ejp8fHzQuXNnyGQyveYzBrGxsbC3t4elpaXUlvs4JiYG7u7uhW6/dOlSjBs3TmstnrS0NDx79gw///wzMjIy0LBhQ7zzzjtGf5lEcS4Fjo2NReXKlbXW56patSrkcjliYmIQHx9faH/16tVLNfbSVNxLol/+OVepVNi8eTN69OgBAMjJyUF2djZiY2Nx5swZZGdno3Hjxlp/rBir4uQhIyMDKpUKMTExOH36NLKzs9GkSRN07NgRgPh6cXR01LqUMHdpipiYGKO+DF3f2HJycrBs2TJ8+eWXWn9YAsCjR49w+PBhqFQqvP3221pFemP18nOXlZWFrVu3Sq/zf4uNjYWzs7NWm7OzMwRBQGxsbJH9xvr74k1W0HP26NEjvcYXdz5jERsbCxcXF602Z2dnxMTEFLjNyz87+/btg0qlQtu2bQGI7wlyuRxbt27FgwcP4OzsjN69e+tcxCkrBT1/BeVBqVRCJpNhy5YtePjwIVxcXNC7d2/Y2dnpNZ+xePLkSbFfD7k2bdoEDw8PrbXyc09mCAwMxKNHj1C9enX07t0bNjY2pR57aSpsPb9/02g0ePr0aZ7n28nJCdHR0UX2G7Oi1nfMz8vvD5s3b4aTk5O07rxSqUTlypWxYcMGxMbG5lkz1lgVNw9KpRJqtRrr16/HkydP8NZbb6F3796wsrKCUqlEUlJSnp8zJycno39/qFy5st7brlq1Cv7+/lqfh5RKJXJycrBu3TrExcWhZs2a6N27d7lYD/blv5XWr1+P2rVr57vO9/Pnz5GVlVXg811Uv6EYb0XHiGVlZeW7sKdCoch3cdOixhd3PmNRkrg1Gg3mz5+Pe/fu4auvvpLa09LS8OuvvwIQv9VetGgRJkyYUKpxl7bi5iH3jJ3Vq1dDLpfDysoK8+fPl9ZgeR1fD9nZ2YVue+7cOTx9+hS9e/fWmi8nJwerVq2CqakpzM3N8fXXX2P69OmlHXqZyi9vMpkMZmZmBb4/vNz/OsrOzsbnn38OlUol/VzkrtOycuVKmJubQy6XY+bMmZg7d25ZhlrqcotqK1asgIWFBeRyOWbMmIFvvvkGQP6vFzMzM8hkstf29XDw4EFYWlqiQ4cOUtvLecr9wmPKlClYvHhxWYSol8zMTEycOBH29vYYM2ZMvmPye75zv4go6P3h5X4yPgU9ZwU9X0WNL+58xqIkcYeEhGD+/Pn4/vvvpaKbUqnE4cOHERUVBUdHR5w8eRLdu3dHXFycQeIvLQXloaDPTWlpaTh06BDu3bsHR0dHnDhxAt27d5fOfC/ufMYgJycHarVar9dDdnY21q1bhxEjRmi1K5VK7Nu3Dw8fPkTVqlVx6NAh9OzZEwkJCaUef1nJzs6GIAgFPt9F9b+ugoODsXr1avz444/S78O0tDTs3LkTT58+RZUqVbBnzx707t37tbtCQKlUYseOHXj27BmqVKmCnTt34r333oNSqZSe8/L4+0JfSUlJ0jrIL1Mqldi2bRtevHiBypUrY+vWrfjggw+KXEvSmAQGBiIoKAg//vhjvidp5T6nBT3fRfUbCs981EOFChWQlZUFtVqt9S1Lenp6vt+gFDW+uPMZCysrK2RmZkIQBK0z0YqKOyMjA//5z38QFxeHTZs2aX2r88cff8DOzk76ZdG1a1d069YNf//9N+rXr2+4gykBKyurfN+sCsqDXC5HaGgo7O3tpR94Pz8/vPfeexgzZkyx5zMW+cUtCIJOce/cuRO9evXSegNUKBR58tShQwf07dsXY8aMgZubW+kfRBnIL28qlQpZWVmoUKEC0tLSCu1/3SQnJ0s311m7dq10iYW9vT1CQ0NRsWJFmJqKv7patmyJjz/+WKe7QZYXlStXRmhoKCpVqiT9PmjWrBmGDRtW4PtDRkYGBEF4LV8PgLjo/AcffKDV5uzsjNDQUFSuXFn60OXr64sxY8ZgzJgxRn92y/Pnz/HZZ5/B0dERq1atKvDOxPk937mX1+RedlpYPxmfgp6zgp6vosYXdz5jUaFCBb3i3rp1K3788UcsW7YMLVu2lNq//vprfPPNN9JVIx9//DF69+6NwMBATJkypfQPoJTk9/wVdgn5/PnzYWJionWc7777LrZs2YKJEycWez5jYGZmBoVCke/roajLTU+dOoUKFSponfUIAEuWLIFcLpe2/+ijj9C9e3ds37690BsblSfm5uYwNTXNN28ODg5F9r+OVqxYgS1btuD3339HgwYNpPbly5dDoVBInykHDx6MTp06Yffu3fjoo4/KKtxSt2LFCpibm0vHOWjQILzzzjsIDg5Gnz59AOS9RDc9PR01atR45bG+CgcOHICXlxdq166t1f7777/DwsJC+vJ6wIAB8Pf3x8GDB/N83jQ2giDgv//9Lw4fPoxNmzblObZcue/5+T3fufWnwvoNhWc+6qFmzZrQaDR4/Pix1JaUlITExETUqlWr2OOLO5+xcHNzg0ql0jp1PyEhAcnJyQXGnZ2djTFjxkClUmHjxo15TqN2cHDQ+iOsVq1aMDMzk+5eZ4xq1aqF9PR0PHv2TGp78uQJMjMz882DTCaDg4ODVqGtTp06kMlkiI+PL/Z8xsLNzQ2pqala3ypHR0cjJyen0LjVajVCQ0PzXD6bX548PT0BwKhfD8Xl5uaGFy9eaN3J7+HDh1JfUf2vE6VSiU8++QQuLi5YvXq11h8dJiYmcHBwkAqPAKRLKF6n10Pucb78RVTucea+P8TFxSEzM1Pqv3//PuRyOWrWrPmqwzW41NRUXL58WbrsPJdcLoeDg4PWt70eHh4QBMHoXw+JiYn46KOP4OPjg59++qnQy3zc3NwQHR0NlUoltd2/fx8KhQLVq1cvsp+Mj5ubGx48eKDVdv/+/QJ/TxY1vrjzGQt94t64cSNWrlyJjRs3ahUeAcDW1lbrDya5XI7atWsb/Vq4bm5u0u/0XPfv3y/w93t+x+nu7i697xV3PmNRs2ZNvV7HISEhWmfF57Kzs9PKk5mZGdzc3Iz+90NxyGQyvPXWW/k+37Vq1Sqy/3Xz3XffITg4GNu2bdMqPALiF9gvrxdoYWGBt95667V6PQDiZfsvH6elpSVcXV3x/PlzWFhYwNnZ+Y15PQDiSU3//vwIiHl6eZkwa2trVKtWrVy8Hr7++mucO3cOQUFBBRYeAfGSfXt7+zzP94MHD1CrVq0i+w2FxUc9ODg4wNfXF0FBQVLbli1bUKtWLdSpU6fY44s7n7FwdHSEt7c3tm/fLrUFBgbCw8OjwBft999/D7VajeXLl2v90APiemU+Pj7466+/pLbTp08DQL5rGRiL3MV6/52HBg0aoFq1annG379/Hz4+Plo3Ezpx4gTMzMzg6elZ7PmMhZubG9zd3fO8jhs1alToWWk3b95Eamoq6tWrp9UeFRUFHx8f3Lp1S2o7fvw4zM3Njfrnorg8PT1RvXp17NixQ2oLDAzE22+/jUqVKhXZ/zqZO3cuXF1dsWjRIq0iIwBcu3YNjRo10vrj5MSJE6hQocJr9aHpr7/+gq+vr9aXUcePH4e1tTXc3NxQv359ODg4YNeuXVJ/YGAgWrVqVe5u1qWLS5cuwcLCIk9h9cKFC2jSpAmePHkitZ04cQL29vZwdXV9xVEWz4wZM+Dr64s5c+YUuZ5xkyZNYGFhgf3790ttW7ZsQceOHaFQKIrsJ+MTEBCAAwcOSGcbREVF4cKFC+jcubNe44s7n7Hw9/dHcHCw9EXKzZs3ceXKFXTq1Cnf8ZcvX8by5cuxfv166YvIlw0YMABLliyRHicnJ+PixYv5rj9uTAICArB3717pMrfr16/j+vXreOedd/Id369fP3z33XfS48TEREREREjHWdz5jEVAQAD27NkjXRr6119/4fbt21o3Q8jPhQsX8hSaAKBXr174+eefpcfPnz/HlStX8h1bngUEBGD37t3SjTsjIiJw79496Qv9ovpfF8ePH8eePXuwcePGPF+8CYKAzp074/fff5fanjx5guvXrxv9+0NxaDQa+Pv7Y/369VJbdHQ0bt26pfX+sHPnTqjVagDAmTNnEB8fj/bt25dFyAYlCAIiIiLy/MyrVCq0a9cOW7duldoePHiAqKgoo3897Nq1C6GhoVi/fr3WvUIK4ufnh+3bt0MQBADAkSNHkJmZKd20sqh+Q5AJuXujYrl+/TqGDRuGevXqwczMDH/99RdWrVol3XHy8OHDiI2NldYYKGp8Uf3G6tq1axg+fDgaNGgAuVyOq1ev4tdff4Wvry8A4NChQ4iLi8Mnn3yC+Ph4dOzYEb6+vnmKURMmTECNGjXw3//+F1u3bkX79u2RnZ2NsLAwTJs2DYMGDSqLw9PZpUuX8Omnn6Jhw4YQBAHXr1/H77//Lr2JBQcHIyUlBUOGDAEgXjazZ88etG/fHhkZGTh//jxmzpyJfv366TSfsbp48SJGjx4NX19fqNVq3LhxA+vWrZMKi3v27EF6errW83n8+HF88cUXuHDhQp75vv76a+zbtw8dOnRAWloaLly4gNmzZxv9KfHffvstEhIS8ODBAzx48ED6Vn7atGlwcnLCjh07oNFopDv3hoWFYdy4cWjatCkyMzNx584dbNiwQTrjrah+YzV//nwkJSXh3r17iI6Olm4GMn36dFStWhVBQUEwMTFB3759cefOHfTo0QPt2rWT1vDKNWvWLFSsWBGzZs3CyZMn0a5dOyQnJ+PSpUv45ptv0L1797I4PJ3NmzcPKSkpuHfvHmJiYqQbJMyYMQMODg7YunUrFAoF+vTpA0EQMH36dJw5cwZt27ZFUlISLl++jAULFqBr164AxDM9Jk+ejGbNmkGpVOLBgwfYuHGj0Rdhv/zyS6Snp+POnTt49uyZ9OFmzpw5sLOzw6ZNm2Bra4tevXpJ22zZsgVbtmzBgQMHtObSaDSYMmUKzp8/j7Zt2yIhIQFXr17FokWLEBAQ8EqPqzguXbqEgQMHws/PL89lLd988w0sLS2xYcMGVKpUCT179gQgfhicNWsWmjdvjsTERDx9+hSbN2+W/sAqqp+MS1ZWFoYPH474+Hh4eXkhPDwc/fr1k9a3ffz4MX777TdMmzYNtra2RY4vqt9YZWRkYOjQoUhOTkbdunURHh6OIUOGYNy4cQDEL2rXrVuHGTNmwMrKCkOGDMGTJ0/y3ISxQ4cO6NGjB86fP48xY8bA29sbVatWRXh4OOrXr49ffvklz5dZxiQ9PR0ff/wxlEqldNfZoUOHSuvA3r17Fxs2bMCsWbNgYWGBsLAwjB07Fg0bNoSDgwPOnz8Pb29v/Pzzz9LdfQubz1gplUoMGTIEOTk5cHd3R1hYGEaPHi39HfXPP/8gMDAQX375pfR8CoKABg0aYO3atWjevLnWfCEhIZg0aRJ8fX1RqVIlhIWFoWnTpgWuj2YsDh06hFOnTiE7OxtHjx5Fx44dYW1tjYCAAHTp0gW3bt3Cli1b8NVXX8HExAQpKSkYPHgwBEGAm5ub9Hlx6NChAFBkv7EKDg7G2bNnkZmZiePHj8Pf3x9WVlbo0qULAgICEBkZid27d0vrfnft2hUmJibw8vLSmqdnz55o3749jhw5ghkzZuDtt9+Gra0tQkND0aFDB6NfJ3rv3r0IDQ1FRkYGTpw4gYCAAFhaWqJ79+7o2LEjrl27huDgYMyZMweA+PqZOXMmmjdvDhsbG4SGhsLf3x/ffvstAPEKxYEDB8LCwgI1atTAuXPnMH36dOnvEWO1Y8cOXLhwAUqlEqdPn0bnzp2hUCjQq1cvtG3bFpcuXcKxY8cwc+Z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quantityvalue
0dimensions40 × 64 × 14
1active cells35838
2restart states25
3pressure range198.170 to 242.894
4SWAT range0.1168 to 0.8800
5SGAS range0.0000 to 0.0000
6minimum SOIL0.120037
\n", + "
" + ], + "text/plain": [ + " quantity value\n", + "0 dimensions 40 × 64 × 14\n", + "1 active cells 35838\n", + "2 restart states 25\n", + "3 pressure range 198.170 to 242.894\n", + "4 SWAT range 0.1168 to 0.8800\n", + "5 SGAS range 0.0000 to 0.0000\n", + "6 minimum SOIL 0.120037" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "reservoir_grid = EGrid(str(OUTPUT_DIRECTORY / \"RMS_REEK_BASE.EGRID\"))\n", + "initialization_file = EclFile(str(OUTPUT_DIRECTORY / \"RMS_REEK_BASE.INIT\"))\n", + "restart_file = ERst(str(OUTPUT_DIRECTORY / \"RMS_REEK_BASE.UNRST\"))\n", + "restart_steps = [int(step) for step in restart_file.report_steps]\n", + "final_restart_step = restart_steps[-1]\n", + "\n", + "grid_nx, grid_ny, grid_nz = [int(value) for value in reservoir_grid.dimension]\n", + "active_cell_count = int(reservoir_grid.active_cells)\n", + "act_flat = sim_actnum.ravel(order=\"F\")\n", + "\n", + "def active_to_kji(active_values):\n", + " active_values = np.asarray(active_values, dtype=float)\n", + " if active_values.size != act_flat.sum():\n", + " raise ValueError(f\"Expected {act_flat.sum()} active values, received {active_values.size}\")\n", + " global_flat = np.full(act_flat.size, np.nan)\n", + " global_flat[act_flat] = active_values\n", + " ijk = global_flat.reshape((grid_nx, grid_ny, grid_nz), order=\"F\")\n", + " return np.transpose(ijk, (2, 1, 0))\n", + "\n", + "permeability_cube_md = active_to_kji(initialization_file[\"PERMX\"])\n", + "final_pressure_cube_bara = active_to_kji(restart_file[\"PRESSURE\", final_restart_step])\n", + "final_water_saturation_cube = active_to_kji(restart_file[\"SWAT\", final_restart_step])\n", + "final_gas_saturation_cube = active_to_kji(restart_file[\"SGAS\", final_restart_step])\n", + "final_oil_saturation_cube = 1.0 - final_water_saturation_cube - final_gas_saturation_cube\n", + "\n", + "restart_audit = pd.DataFrame({\n", + " \"quantity\": [\"dimensions\", \"active cells\", \"restart states\", \"pressure range\", \"SWAT range\", \"SGAS range\", \"minimum SOIL\"],\n", + " \"value\": [\n", + " f\"{grid_nx} × {grid_ny} × {grid_nz}\",\n", + " active_cell_count,\n", + " len(restart_steps),\n", + " f\"{np.nanmin(final_pressure_cube_bara):.3f} to {np.nanmax(final_pressure_cube_bara):.3f}\",\n", + " f\"{np.nanmin(final_water_saturation_cube):.4f} to {np.nanmax(final_water_saturation_cube):.4f}\",\n", + " f\"{np.nanmin(final_gas_saturation_cube):.4f} to {np.nanmax(final_gas_saturation_cube):.4f}\",\n", + " float(np.nanmin(final_oil_saturation_cube)),\n", + " ],\n", + "})\n", + "display(restart_audit)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "757ebbf4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:43.760331Z", + "iopub.status.busy": "2026-08-31T17:04:43.760168Z", + "iopub.status.idle": "2026-08-31T17:04:46.868120Z", + "shell.execute_reply": "2026-08-31T17:04:46.867018Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "layers_to_plot = np.unique(np.linspace(0, grid_nz - 1, 4, dtype=int))\n", + "state_map_figure, axes = plt.subplots(len(layers_to_plot), 3, figsize=(13.5, 14.0), constrained_layout=True)\n", + "definitions = [\n", + " (final_pressure_cube_bara, \"Pressure\", \"bara\", \"viridis\"),\n", + " (final_water_saturation_cube, \"Water saturation\", \"fraction\", \"Blues\"),\n", + " (final_gas_saturation_cube, \"Gas saturation\", \"fraction\", \"Oranges\"),\n", + "]\n", + "images = []\n", + "for column, (cube, label, unit, cmap) in enumerate(definitions):\n", + " vmin, vmax = np.nanmin(cube), np.nanmax(cube)\n", + " for row, k_index in enumerate(layers_to_plot):\n", + " image = axes[row, column].imshow(cube[k_index], origin=\"lower\", cmap=cmap, vmin=vmin, vmax=vmax, aspect=\"auto\")\n", + " if row == 0:\n", + " images.append(image)\n", + " axes[row, column].scatter(producer_i, producer_j, marker=\"v\", s=65, color=\"#D55E00\", edgecolor=\"white\")\n", + " axes[row, column].scatter(injector_i, injector_j, marker=\"^\", s=65, color=\"#0072B2\", edgecolor=\"white\")\n", + " axes[row, column].set(title=f\"Layer {k_index + 1}: {label}\", xlabel=\"I\", ylabel=\"J\")\n", + " state_map_figure.colorbar(images[column], ax=axes[:, column], label=f\"{label} [{unit}]\", shrink=0.82)\n", + "state_map_figure.suptitle(\"OPM Flow final restart states from the Reek corner-point model\", fontsize=15)\n", + "path = OUTPUT_DIRECTORY / \"opm_final_restart_maps.png\"\n", + "state_map_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "eb2e224d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:46.869805Z", + "iopub.status.busy": "2026-08-31T17:04:46.869627Z", + "iopub.status.idle": "2026-08-31T17:04:47.466772Z", + "shell.execute_reply": "2026-08-31T17:04:47.465954Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "water_increment = final_water_saturation_cube - 0.12\n", + "candidate_mask = np.isfinite(water_increment) & (water_increment > 0.015)\n", + "k_indices, j_indices, i_indices = np.where(candidate_mask)\n", + "if len(i_indices) > 8000:\n", + " rng = np.random.default_rng(RANDOM_SEED)\n", + " keep = rng.choice(len(i_indices), 8000, replace=False)\n", + " i_indices, j_indices, k_indices = i_indices[keep], j_indices[keep], k_indices[keep]\n", + "\n", + "front_figure = plt.figure(figsize=(11.5, 8.0), constrained_layout=True)\n", + "axis = front_figure.add_subplot(111, projection=\"3d\")\n", + "colors = final_water_saturation_cube[k_indices, j_indices, i_indices]\n", + "scatter = axis.scatter(i_indices, j_indices, -k_indices, c=colors, cmap=\"Blues\",\n", + " vmin=0.12, vmax=max(0.2, float(np.nanmax(final_water_saturation_cube))),\n", + " s=16, alpha=0.7)\n", + "axis.plot([producer_i] * grid_nz, [producer_j] * grid_nz, -np.arange(grid_nz), color=\"#D55E00\", linewidth=3, label=\"PROD\")\n", + "axis.plot([injector_i] * grid_nz, [injector_j] * grid_nz, -np.arange(grid_nz), color=\"#0072B2\", linewidth=3, label=\"WINJ\")\n", + "axis.set(xlabel=\"I column\", ylabel=\"J row\", zlabel=\"Layer (depth downward)\", title=\"Cells with increased water saturation\")\n", + "axis.legend()\n", + "front_figure.colorbar(scatter, ax=axis, label=\"Final SWAT [-]\", shrink=0.65)\n", + "axis.view_init(elev=26, azim=-55)\n", + "path = OUTPUT_DIRECTORY / \"opm_3d_water_front.png\"\n", + "front_figure.savefig(path, dpi=175, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b84db32c", + "metadata": {}, + "source": [ + "## 9. Agent contract: fixture today, licensed RMS worker later\n", + "\n", + "An agent should not receive unrestricted shell access to an RMS workstation. It should submit a\n", + "typed job to an allow-listed service. The service validates project identity, workflow name,\n", + "grid model, properties, output location, resource limits, and approval policy. It returns signed\n", + "artifacts and a structured ledger.\n", + "\n", + "The fixture backend below is actually exercised: it verifies the exported artifacts, blocking\n", + "contract, Flow run, and ERT run. A production RMS adapter would implement the same methods with\n", + "the RMS Python API inside a licensed environment. Human approval should remain mandatory for\n", + "publishing project changes, overwriting realizations, or promoting a model to decision use." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5a9db6c4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:47.468738Z", + "iopub.status.busy": "2026-08-31T17:04:47.468563Z", + "iopub.status.idle": "2026-08-31T17:04:47.484413Z", + "shell.execute_reply": "2026-08-31T17:04:47.483648Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sequencetoolstatusevidence
01rms.inspect_projectfixture_passed{'data_commit': 'cad17f24e22c19c6cefe6f6471853...
12rms.run_workflowfixture_passed{'workflow': 'REEK_BLOCK_AND_EXPORT_V1', 'bloc...
23rms.export_gridfixture_passed{'grid_sha256': 'f52cc87ee49ae0f715e504910386c...
34neqsim.generate_pvtpassed{'source_commit': '1f7a01b06307d9d56451e43bb8d...
45opm.validatepassed{'keywords': 17}
56opm.runpassed{'return_code': 0, 'restart_steps': 25}
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" + ], + "text/plain": [ + " sequence tool status evidence\n", + "0 1 rms.inspect_project fixture_passed {'data_commit': 'cad17f24e22c19c6cefe6f6471853...\n", + "1 2 rms.run_workflow fixture_passed {'workflow': 'REEK_BLOCK_AND_EXPORT_V1', 'bloc...\n", + "2 3 rms.export_grid fixture_passed {'grid_sha256': 'f52cc87ee49ae0f715e504910386c...\n", + "3 4 neqsim.generate_pvt passed {'source_commit': '1f7a01b06307d9d56451e43bb8d...\n", + "4 5 opm.validate passed {'keywords': 17}\n", + "5 6 opm.run passed {'return_code': 0, 'restart_steps': 25}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"schema\": \"com.neqsim.reservoir-job/v1\",\n", + " \"job_id\": \"reek-public-rms-opm-ert-20260831\",\n", + " \"mode\": \"public_fixture\",\n", + " \"source\": {\n", + " \"kind\": \"rms_export\",\n", + " \"repository\": \"equinor/xtgeo-testdata\",\n", + " \"commit\": \"cad17f24e22c19c6cefe6f647185395cc0a11add\",\n", + " \"project_alias\": \"REEK_PUBLIC\"\n", + " },\n", + " \"requested_tools\": [\n", + " \"rms.inspect_project\",\n", + " \"rms.run_workflow\",\n", + " \"rms.export_grid\",\n", + " \"rms.export_properties\",\n", + " \"neqsim.generate_pvt\",\n", + " \"opm.validate\",\n", + " \"opm.run\",\n", + " \"ert.ensemble_experiment\"\n", + " ],\n", + " \"rms\": {\n", + " \"workflow_allowlist\": [\n", + " \"REEK_BLOCK_AND_EXPORT_V1\"\n", + " ],\n", + " \"grid_model\": \"REEK_SIM\",\n", + " \"properties\": [\n", + " \"PORO\",\n", + " \"PERMX\",\n", + " \"FACIES\",\n", + " \"Zone\"\n", + " ],\n", + " \"blocking\": {\n", + " \"i\": 2,\n", + " \"j\": 2,\n", + " \"k\": 4\n", + " },\n", + " \"write_policy\": \"new_realization_only\"\n", + " },\n", + " \"flow\": {\n", + " \"simulator\": \"opm-flow\",\n", + " \"forecast_months\": 24\n", + " },\n", + " \"ert\": {\n", + " \"realizations\": 4,\n", + " \"max_parallel\": 2,\n", + " \"random_seed\": 20260831\n", + " },\n", + " \"security\": {\n", + " \"network_egress\": \"deny_except_artifact_registry\",\n", + " \"secrets\": \"worker_identity_only\",\n", + " \"human_approval\": [\n", + " \"overwrite_rms\",\n", + " \"publish_decision_model\"\n", + " ]\n", + " },\n", + " \"acceptance\": {\n", + " \"all_hashes_verified\": true,\n", + " \"simulator_return_code\": 0,\n", + " \"ensemble_realizations\": 4,\n", + " \"engineering_assertions\": \"all_pass\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "agent_job = {\n", + " \"schema\": \"com.neqsim.reservoir-job/v1\",\n", + " \"job_id\": \"reek-public-rms-opm-ert-20260831\",\n", + " \"mode\": \"public_fixture\",\n", + " \"source\": {\n", + " \"kind\": \"rms_export\",\n", + " \"repository\": \"equinor/xtgeo-testdata\",\n", + " \"commit\": DATA_COMMIT,\n", + " \"project_alias\": \"REEK_PUBLIC\",\n", + " },\n", + " \"requested_tools\": [\n", + " \"rms.inspect_project\", \"rms.run_workflow\", \"rms.export_grid\",\n", + " \"rms.export_properties\", \"neqsim.generate_pvt\", \"opm.validate\",\n", + " \"opm.run\", \"ert.ensemble_experiment\",\n", + " ],\n", + " \"rms\": {\n", + " \"workflow_allowlist\": [\"REEK_BLOCK_AND_EXPORT_V1\"],\n", + " \"grid_model\": \"REEK_SIM\",\n", + " \"properties\": [\"PORO\", \"PERMX\", \"FACIES\", \"Zone\"],\n", + " \"blocking\": {\"i\": 2, \"j\": 2, \"k\": 4},\n", + " \"write_policy\": \"new_realization_only\",\n", + " },\n", + " \"flow\": {\"simulator\": \"opm-flow\", \"forecast_months\": 24},\n", + " \"ert\": {\"realizations\": 4, \"max_parallel\": 2, \"random_seed\": RANDOM_SEED},\n", + " \"security\": {\n", + " \"network_egress\": \"deny_except_artifact_registry\",\n", + " \"secrets\": \"worker_identity_only\",\n", + " \"human_approval\": [\"overwrite_rms\", \"publish_decision_model\"],\n", + " },\n", + " \"acceptance\": {\n", + " \"all_hashes_verified\": True,\n", + " \"simulator_return_code\": 0,\n", + " \"ensemble_realizations\": 4,\n", + " \"engineering_assertions\": \"all_pass\",\n", + " },\n", + "}\n", + "\n", + "class AgentToolLedger:\n", + " def __init__(self, job):\n", + " self.job = job\n", + " self.records = []\n", + "\n", + " def record(self, tool, status, evidence):\n", + " entry = {\n", + " \"sequence\": len(self.records) + 1,\n", + " \"tool\": tool,\n", + " \"status\": status,\n", + " \"evidence\": evidence,\n", + " }\n", + " self.records.append(entry)\n", + " return entry\n", + "\n", + " def frame(self):\n", + " return pd.DataFrame(self.records)\n", + "\n", + "agent_ledger = AgentToolLedger(agent_job)\n", + "agent_ledger.record(\"rms.inspect_project\", \"fixture_passed\", {\n", + " \"data_commit\": DATA_COMMIT,\n", + " \"verified_files\": int(download_table[\"verified\"].sum()),\n", + "})\n", + "agent_ledger.record(\"rms.run_workflow\", \"fixture_passed\", {\n", + " \"workflow\": \"REEK_BLOCK_AND_EXPORT_V1\",\n", + " \"blocking\": list(BLOCK),\n", + " \"fine_cells\": fine_grid.ntotal,\n", + " \"simulation_cells\": sim_grid.ntotal,\n", + "})\n", + "agent_ledger.record(\"rms.export_grid\", \"fixture_passed\", {\n", + " \"grid_sha256\": generated_static_table.loc[generated_static_table[\"file\"] == \"REEK_GRID.GRDECL\", \"sha256\"].iloc[0],\n", + "})\n", + "agent_ledger.record(\"neqsim.generate_pvt\", \"passed\", {\n", + " \"source_commit\": neqsim_commit,\n", + " \"bubble_pressure_bara\": float(bubble_pressure_bara),\n", + " \"pvt_sha256\": hashlib.sha256(black_oil_path.read_bytes()).hexdigest(),\n", + "})\n", + "agent_ledger.record(\"opm.validate\", \"passed\", {\n", + " \"keywords\": int(deck_keyword_audit[\"present\"].sum()),\n", + "})\n", + "agent_ledger.record(\"opm.run\", \"passed\", {\n", + " \"return_code\": flow_run.returncode,\n", + " \"restart_steps\": len(restart_steps),\n", + "})\n", + "job_path = OUTPUT_DIRECTORY / \"agent_job.json\"\n", + "job_path.write_text(json.dumps(agent_job, indent=2) + \"\\n\", encoding=\"utf-8\")\n", + "display(agent_ledger.frame())\n", + "print(json.dumps(agent_job, indent=2))" + ] + }, + { + "cell_type": "markdown", + "id": "676cdf3e", + "metadata": {}, + "source": [ + "### Licensed RMS adapter pattern\n", + "\n", + "A production worker implements the same contract approximately as follows:\n", + "\n", + "1. Resolve the approved project alias to a server-side path; never accept an arbitrary path.\n", + "2. Open the project read-only and inspect the named grid/property inventory.\n", + "3. Create a new realization or approved scratch case.\n", + "4. Execute only the allow-listed RMS workflow REEK_BLOCK_AND_EXPORT_V1.\n", + "5. Export grid and named properties to a job-specific staging directory.\n", + "6. Calculate SHA-256, write a manifest, close RMS, and upload signed artifacts.\n", + "7. Trigger the same XTGeo, NeqSim, OPM Flow, and ERT validators shown here.\n", + "8. Return artifact URIs, logs, resolved software versions, and approval state.\n", + "\n", + "This separation lets an LLM plan and monitor work without giving it raw access to license files,\n", + "project directories, or destructive APIs." + ] + }, + { + "cell_type": "markdown", + "id": "c0657415", + "metadata": {}, + "source": [ + "## 10. Configure and run ERT with OPM Flow\n", + "\n", + "ERT samples three transparent uncertainties:\n", + "\n", + "| Parameter | Distribution | Meaning |\n", + "|---|---:|---|\n", + "| PERM_MULT | Uniform 0.70–1.30 | multiplies PERMX, PERMY, PERMZ |\n", + "| PORO_MULT | Uniform 0.95–1.05 | multiplies PORO |\n", + "| INJ_RATE | Uniform 8,000–16,000 Sm3/day | water-injection target |\n", + "\n", + "TEMPLATE_RENDER writes one complete Flow deck per realization. The standard ERT FLOW forward\n", + "model runs the real simulator. Four realizations are deliberately small enough for Colab but use\n", + "the same directory and parameter contracts as a larger study." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3336be9e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:47.486147Z", + "iopub.status.busy": "2026-08-31T17:04:47.485958Z", + "iopub.status.idle": "2026-08-31T17:04:47.491596Z", + "shell.execute_reply": "2026-08-31T17:04:47.490765Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameter priors:\n", + " PERM_MULT UNIFORM 0.70 1.30\n", + "PORO_MULT UNIFORM 0.95 1.05\n", + "INJ_RATE UNIFORM 8000.0 16000.0\n", + "\n", + "ERT configuration:\n", + " NUM_REALIZATIONS 4\n", + "MIN_REALIZATIONS 4\n", + "RANDOM_SEED 20260831\n", + "\n", + "QUEUE_SYSTEM LOCAL\n", + "QUEUE_OPTION LOCAL MAX_RUNNING 2\n", + "\n", + "RUNPATH runs/realization-/iter-\n", + "ECLBASE RMS_REEK\n", + "SUMMARY FPR FOPR FGPR FWPR FWIR FOPT FGPT FWPT WBHP:PROD WGOR:PROD\n", + "\n", + "GEN_KW PARAMETERS parameters.txt\n", + "\n", + "FORWARD_MODEL TEMPLATE_RENDER(=parameters.json, =/RMS_REEK.DATA.jinja2, =RMS_REEK.DATA)\n", + "FORWARD_MODEL FLOW\n", + "\n", + "Rendered deck template (complete):\n", + " RUNSPEC\n", + "TITLE\n", + " PUBLIC RMS-ORIGIN REEK MODEL: NEQSIM PVT, OPM FLOW, ERT\n", + "\n", + "DIMENS\n", + " 40 64 14 /\n", + "\n", + "OIL\n", + "GAS\n", + "WATER\n", + "DISGAS\n", + "METRIC\n", + "\n", + "START\n", + " 1 'JAN' 2025 /\n", + "\n", + "WELLDIMS\n", + " 2 14 1 2 /\n", + "\n", + "TABDIMS\n", + "/\n", + "\n", + "EQLDIMS\n", + "/\n", + "\n", + "UNIFOUT\n", + "\n", + "GRID\n", + "INIT\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/REEK_GRID.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PORO.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PERMX.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PERMY.GRDECL' /\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/PERMZ.GRDECL' /\n", + "\n", + "MULTIPLY\n", + " PORO {{parameters.PORO_MULT.value}} 6* /\n", + " PERMX {{parameters.PERM_MULT.value}} 6* /\n", + " PERMY {{parameters.PERM_MULT.value}} 6* /\n", + " PERMZ {{parameters.PERM_MULT.value}} 6* /\n", + "/\n", + "\n", + "PROPS\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/NEQSIM_PVT.INC' /\n", + "\n", + "ROCK\n", + " 260.0 4.0E-5 /\n", + "\n", + "SWOF\n", + " 0.12 0.00000 1.0000 0.0\n", + " 0.20 0.00010 0.8500 0.0\n", + " 0.30 0.00100 0.6200 0.0\n", + " 0.40 0.00800 0.4000 0.0\n", + " 0.50 0.03000 0.2200 0.0\n", + " 0.60 0.09000 0.1000 0.0\n", + " 0.70 0.22000 0.0300 0.0\n", + " 0.80 0.48000 0.0050 0.0\n", + " 0.88 1.00000 0.0000 0.0 /\n", + "\n", + "SGOF\n", + " 0.00 0.0000 1.0000 0.0\n", + " 0.05 0.0020 0.9300 0.0\n", + " 0.10 0.0100 0.8000 0.0\n", + " 0.20 0.0600 0.5500 0.0\n", + " 0.30 0.1600 0.3200 0.0\n", + " 0.40 0.3300 0.1500 0.0\n", + " 0.50 0.5500 0.0500 0.0\n", + " 0.60 0.7800 0.0100 0.0\n", + " 0.70 0.9300 0.0010 0.0\n", + " 0.88 1.0000 0.0000 0.0 /\n", + "\n", + "REGIONS\n", + "INCLUDE\n", + " '/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/FIPNUM.GRDECL' /\n", + "\n", + "SOLUTION\n", + "EQUIL\n", + " 1726.879839 260.000000\n", + " 2077.856735 0.0 1453.380249 0.0 1 0 0 /\n", + "\n", + "RSVD\n", + " 1553.380249 224.67271009\n", + " 1977.856735 224.67271009 /\n", + "\n", + "SUMMARY\n", + "FPR\n", + "FOPR\n", + "FGPR\n", + "FWPR\n", + "FWIR\n", + "FOPT\n", + "FGPT\n", + "FWPT\n", + "WBHP\n", + " 'PROD' 'WINJ' /\n", + "WGOR\n", + " 'PROD' /\n", + "\n", + "SCHEDULE\n", + "RPTRST\n", + " 'BASIC=1' /\n", + "\n", + "GRUPTREE\n", + " 'WELLS' 'FIELD' /\n", + "/\n", + "\n", + "WELSPECS\n", + " 'PROD' 'WELLS' 25 38 1726.880 'OIL' /\n", + " 'WINJ' 'WELLS' 19 34 1726.880 'WATER' /\n", + "/\n", + "\n", + "COMPDAT\n", + " 'PROD' 25 38 1 1 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 2 2 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 3 3 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 4 4 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 5 5 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 6 6 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 7 7 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 8 8 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 9 9 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 10 10 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 11 11 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 12 12 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 13 13 'OPEN' 1* 1* 0.20 /\n", + " 'PROD' 25 38 14 14 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 1 1 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 2 2 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 3 3 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 4 4 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 5 5 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 6 6 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 7 7 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 8 8 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 9 9 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 10 10 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 11 11 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 12 12 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 13 13 'OPEN' 1* 1* 0.20 /\n", + " 'WINJ' 19 34 14 14 'OPEN' 1* 1* 0.20 /\n", + "/\n", + "\n", + "WCONPROD\n", + " 'PROD' 'OPEN' 'ORAT' 10000.0 4* 80.000 /\n", + "/\n", + "\n", + "WCONINJE\n", + " 'WINJ' 'WATER' 'OPEN' 'RATE' {{parameters.INJ_RATE.value}} 1* 320.0 /\n", + "/\n", + "\n", + "TSTEP\n", + " 24*30.4375 /\n", + "\n", + "END\n" + ] + } + ], + "source": [ + "parameter_priors = '''PERM_MULT UNIFORM 0.70 1.30\n", + "PORO_MULT UNIFORM 0.95 1.05\n", + "INJ_RATE UNIFORM 8000.0 16000.0\n", + "'''\n", + "(ERT_DIRECTORY / \"parameters.txt\").write_text(parameter_priors, encoding=\"utf-8\")\n", + "\n", + "template_text = render_deck(\n", + " \"{{parameters.PERM_MULT.value}}\",\n", + " \"{{parameters.PORO_MULT.value}}\",\n", + " \"{{parameters.INJ_RATE.value}}\",\n", + ")\n", + "template_path = ERT_DIRECTORY / \"RMS_REEK.DATA.jinja2\"\n", + "template_path.write_text(template_text + \"\\n\", encoding=\"utf-8\")\n", + "\n", + "ert_configuration = f'''\n", + "NUM_REALIZATIONS 4\n", + "MIN_REALIZATIONS 4\n", + "RANDOM_SEED {RANDOM_SEED}\n", + "\n", + "QUEUE_SYSTEM LOCAL\n", + "QUEUE_OPTION LOCAL MAX_RUNNING 2\n", + "\n", + "RUNPATH runs/realization-/iter-\n", + "ECLBASE RMS_REEK\n", + "SUMMARY FPR FOPR FGPR FWPR FWIR FOPT FGPT FWPT WBHP:PROD WGOR:PROD\n", + "\n", + "GEN_KW PARAMETERS parameters.txt\n", + "\n", + "FORWARD_MODEL TEMPLATE_RENDER(=parameters.json, =/RMS_REEK.DATA.jinja2, =RMS_REEK.DATA)\n", + "FORWARD_MODEL FLOW\n", + "'''\n", + "ert_configuration = textwrap.dedent(ert_configuration).strip() + \"\\n\"\n", + "ert_config_path = ERT_DIRECTORY / \"rms_reek.ert\"\n", + "ert_config_path.write_text(ert_configuration, encoding=\"utf-8\")\n", + "\n", + "print(\"Parameter priors:\\n\", parameter_priors)\n", + "print(\"ERT configuration:\\n\", ert_configuration)\n", + "print(\"Rendered deck template (complete):\\n\", template_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0ee9fb9f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:04:47.493204Z", + "iopub.status.busy": "2026-08-31T17:04:47.493010Z", + "iopub.status.idle": "2026-08-31T17:05:28.870342Z", + "shell.execute_reply": "2026-08-31T17:05:28.869428Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/ert/rms_reek.ert:14:15:30:Could not find forward model step 'TEMPLATE_RENDER' in list of installed forward model steps: []\n", + "/home/runner/work/NeqSim-Colab/NeqSim-Colab/notebooks/reservoir/rms_to_opm_outputs/ert/rms_reek.ert:15:15:19:Could not find forward model step 'FLOW' in list of installed forward model steps: []\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [ + "{'sequence': 7,\n", + " 'tool': 'ert.ensemble_experiment',\n", + " 'status': 'passed',\n", + " 'evidence': {'return_code': 0,\n", + " 'wall_time_seconds': 38.02129026099999,\n", + " 'realizations': 4}}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ert_lint = subprocess.run(\n", + " [\"ert\", \"lint\", ert_config_path.name],\n", + " cwd=ERT_DIRECTORY,\n", + " capture_output=True,\n", + " text=True,\n", + " timeout=180,\n", + ")\n", + "print(ert_lint.stdout)\n", + "if ert_lint.stderr:\n", + " print(ert_lint.stderr)\n", + "if ert_lint.returncode != 0:\n", + " raise RuntimeError(\"ERT lint failed.\")\n", + "\n", + "ert_start = time.perf_counter()\n", + "ert_run = subprocess.run(\n", + " [\"ert\", \"ensemble_experiment\", \"--disable-monitoring\", ert_config_path.name],\n", + " cwd=ERT_DIRECTORY,\n", + " capture_output=True,\n", + " text=True,\n", + " timeout=3600,\n", + ")\n", + "ert_elapsed = time.perf_counter() - ert_start\n", + "print(\"\\n\".join((ert_run.stdout + \"\\n\" + ert_run.stderr).splitlines()[-80:]))\n", + "if ert_run.returncode != 0:\n", + " raise RuntimeError(\"ERT ensemble experiment failed.\")\n", + "\n", + "agent_ledger.record(\"ert.ensemble_experiment\", \"passed\", {\n", + " \"return_code\": ert_run.returncode,\n", + " \"wall_time_seconds\": ert_elapsed,\n", + " \"realizations\": 4,\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "40618b40", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:28.872115Z", + "iopub.status.busy": "2026-08-31T17:05:28.871919Z", + "iopub.status.idle": 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" + ], + "text/plain": [ + " realization time_days field_pressure_bara oil_rate_Sm3_day gas_rate_Sm3_day water_rate_Sm3_day water_injection_Sm3_day cumulative_oil_MSm3 \\\n", + "26 0 730.5 228.361130 10000.000000 2246727.0 0.010718 12882.124023 7.305 \n", + "53 1 730.5 223.260330 10000.000000 2246727.0 0.009637 12107.398438 7.305 \n", + "80 2 730.5 230.828323 10000.000000 2246727.0 0.008757 13047.756836 7.305 \n", + "107 3 730.5 219.467545 9999.999023 2245686.0 0.010623 11658.196289 7.305 \n", + "\n", + " producer_bhp_bara well_gor_Sm3_Sm3 \n", + "26 207.131531 224.672714 \n", + "53 202.979431 224.672714 \n", + "80 208.115616 224.672714 \n", + "107 196.927917 224.568634 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ensemble_frames = []\n", + "parameter_rows = []\n", + "for realization in range(4):\n", + " run_directory = ERT_DIRECTORY / \"runs\" / f\"realization-{realization}\" / \"iter-0\"\n", + " parameter_payload = json.loads((run_directory / \"parameters.json\").read_text(encoding=\"utf-8\"))\n", + " parameter_rows.append({\n", + " \"realization\": realization,\n", + " **{name: float(payload[\"value\"]) for name, payload in parameter_payload.items()},\n", + " })\n", + " realization_summary = ESmry(str(run_directory / \"RMS_REEK.SMSPEC\"))\n", + " def values(key):\n", + " return np.asarray(realization_summary[key], dtype=float)\n", + " frame = pd.DataFrame({\n", + " \"realization\": realization,\n", + " \"time_days\": values(\"TIME\"),\n", + " \"field_pressure_bara\": values(\"FPR\"),\n", + " \"oil_rate_Sm3_day\": values(\"FOPR\"),\n", + " \"gas_rate_Sm3_day\": values(\"FGPR\"),\n", + " \"water_rate_Sm3_day\": values(\"FWPR\"),\n", + " \"water_injection_Sm3_day\": values(\"FWIR\"),\n", + " \"cumulative_oil_MSm3\": values(\"FOPT\") / 1e6,\n", + " \"producer_bhp_bara\": values(\"WBHP:PROD\"),\n", + " \"well_gor_Sm3_Sm3\": values(\"WGOR:PROD\"),\n", + " })\n", + " ensemble_frames.append(frame)\n", + "\n", + "ensemble_history = pd.concat(ensemble_frames, ignore_index=True)\n", + "ensemble_parameters = pd.DataFrame(parameter_rows).sort_values(\"realization\")\n", + "display(ensemble_parameters)\n", + "display(ensemble_history.groupby(\"realization\").tail(1))\n", + "\n", + "ensemble_figure, axes = plt.subplots(1, 3, figsize=(15.0, 4.8), constrained_layout=True)\n", + "for realization, frame in ensemble_history.groupby(\"realization\"):\n", + " years = frame[\"time_days\"] / 365.25\n", + " axes[0].plot(years, frame[\"oil_rate_Sm3_day\"], alpha=0.8, label=f\"R{realization}\")\n", + " axes[1].plot(years, frame[\"field_pressure_bara\"], alpha=0.8)\n", + " axes[2].plot(years, frame[\"water_rate_Sm3_day\"], alpha=0.8)\n", + "axes[0].set(xlabel=\"Time [year]\", ylabel=\"Oil rate [Sm3/day]\", title=\"ERT oil-rate ensemble\")\n", + "axes[1].set(xlabel=\"Time [year]\", ylabel=\"Pressure [bara]\", title=\"ERT pressure ensemble\")\n", + "axes[2].set(xlabel=\"Time [year]\", ylabel=\"Water rate [Sm3/day]\", title=\"ERT water ensemble\")\n", + "axes[0].legend(ncol=2)\n", + "path = OUTPUT_DIRECTORY / \"ert_flow_ensemble.png\"\n", + "ensemble_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()\n", + "\n", + "final_ensemble = ensemble_history.groupby(\"realization\").tail(1).merge(ensemble_parameters, on=\"realization\")\n", + "relationship_figure, axes = plt.subplots(1, 2, figsize=(11.5, 4.5), constrained_layout=True)\n", + "axes[0].scatter(final_ensemble[\"PERM_MULT\"], final_ensemble[\"cumulative_oil_MSm3\"],\n", + " c=final_ensemble[\"INJ_RATE\"], cmap=\"viridis\", s=100)\n", + "axes[0].set(xlabel=\"PERM_MULT\", ylabel=\"Final cumulative oil [million Sm3]\", title=\"Permeability response\")\n", + "scatter = axes[1].scatter(final_ensemble[\"PORO_MULT\"], final_ensemble[\"field_pressure_bara\"],\n", + " c=final_ensemble[\"INJ_RATE\"], cmap=\"plasma\", s=100)\n", + "axes[1].set(xlabel=\"PORO_MULT\", ylabel=\"Final pressure [bara]\", title=\"Porosity and injection response\")\n", + "relationship_figure.colorbar(scatter, ax=axes, label=\"Injection target [Sm3/day]\", shrink=0.85)\n", + "path = OUTPUT_DIRECTORY / \"ert_parameter_response.png\"\n", + "relationship_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", + "FIGURE_PATHS.append(path)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e1ad46e2", + "metadata": {}, + "source": [ + "## 11. Transfer an ERT realization to NeqSim facilities\n", + "\n", + "The realization closest to median final cumulative oil is selected deterministically. Flow\n", + "standard oil, gas, and water rates are reconstructed with the same characterized NeqSim stock\n", + "oil and gas phases used to build PVT. The selected maximum-load timestep then feeds a wellhead\n", + "choke, three-phase separator, gas compressor, aftercooler, oil letdown valve, and low-pressure\n", + "separator. The mass balance is checked." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "fb463548", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:30.543939Z", + "iopub.status.busy": "2026-08-31T17:05:30.543751Z", + "iopub.status.idle": "2026-08-31T17:05:30.562536Z", + "shell.execute_reply": "2026-08-31T17:05:30.561756Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected median-like realization: 0\n" + ] + }, + { + "data": { + "text/html": [ + "
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realizationtime_daysfield_pressure_baraoil_rate_Sm3_daygas_rate_Sm3_daywater_rate_Sm3_daywater_injection_Sm3_daycumulative_oil_MSm3producer_bhp_barawell_gor_Sm3_Sm3
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" + ], + "text/plain": [ + " realization time_days field_pressure_bara oil_rate_Sm3_day gas_rate_Sm3_day water_rate_Sm3_day water_injection_Sm3_day cumulative_oil_MSm3 \\\n", + "0 0 1.0000 259.675354 10000.0 2246727.0 0.001565 12882.124023 0.010000 \n", + "1 0 4.0000 259.548553 10000.0 2246727.0 0.002349 12882.124023 0.040000 \n", + "2 0 13.0000 259.167816 10000.0 2246727.0 0.002741 12882.124023 0.130000 \n", + "3 0 30.4375 258.430298 10000.0 2246727.0 0.002985 12882.124023 0.304375 \n", + "4 0 60.8750 257.141663 10000.0 2246727.0 0.003286 12882.124023 0.608750 \n", + "5 0 91.3125 255.851395 10000.0 2246727.0 0.003559 12882.124023 0.913125 \n", + "6 0 121.7500 254.559509 10000.0 2246727.0 0.003817 12882.124023 1.217500 \n", + "7 0 152.1875 253.265930 10000.0 2246727.0 0.004069 12882.124023 1.521875 \n", + "8 0 182.6250 251.970718 10000.0 2246727.0 0.004318 12882.124023 1.826250 \n", + "9 0 213.0625 250.673798 10000.0 2246727.0 0.004563 12882.124023 2.130625 \n", + "10 0 243.5000 249.375153 10000.0 2246727.0 0.004807 12882.124023 2.435000 \n", + "11 0 273.9375 248.074890 10000.0 2246727.0 0.005049 12882.124023 2.739375 \n", + "12 0 304.3750 246.772919 10000.0 2246727.0 0.005290 12882.124023 3.043750 \n", + "13 0 334.8125 245.469254 10000.0 2246727.0 0.005533 12882.124023 3.348125 \n", + "14 0 365.2500 244.163864 10000.0 2246727.0 0.005777 12882.124023 3.652500 \n", + "15 0 395.6875 242.856674 10000.0 2246727.0 0.006027 12882.124023 3.956875 \n", + "16 0 426.1250 241.547531 10000.0 2246727.0 0.006287 12882.124023 4.261250 \n", + "17 0 456.5625 240.236481 10000.0 2246727.0 0.006552 12882.124023 4.565625 \n", + "18 0 487.0000 238.923660 10000.0 2246727.0 0.006817 12882.124023 4.870000 \n", + "19 0 517.4375 237.609344 10000.0 2246727.0 0.007073 12882.124023 5.174375 \n", + "20 0 547.8750 236.293503 10000.0 2246727.0 0.007322 12882.124023 5.478750 \n", + "21 0 578.3125 234.975922 10000.0 2246727.0 0.007572 12882.124023 5.783125 \n", + "22 0 608.7500 233.656555 10000.0 2246727.0 0.007824 12882.124023 6.087500 \n", + "23 0 639.1875 232.335388 10000.0 2246727.0 0.008084 12882.124023 6.391875 \n", + "24 0 669.6250 231.012283 10000.0 2246727.0 0.008357 12882.124023 6.696250 \n", + "25 0 700.0625 229.687485 10000.0 2246727.0 0.008655 12882.124023 7.000625 \n", + "26 0 730.5000 228.361130 10000.0 2246727.0 0.010718 12882.124023 7.305000 \n", + "\n", + " producer_bhp_bara well_gor_Sm3_Sm3 \n", + "0 248.980270 224.672714 \n", + "1 244.678894 224.672714 \n", + "2 242.516174 224.672714 \n", + "3 241.166214 224.672714 \n", + "4 239.498978 224.672714 \n", + "5 237.980637 224.672714 \n", + "6 236.540619 224.672714 \n", + "7 235.131165 224.672714 \n", + "8 233.735382 224.672714 \n", + "9 232.352722 224.672714 \n", + "10 230.973129 224.672714 \n", + "11 229.605484 224.672714 \n", + "12 228.233047 224.672714 \n", + "13 226.848495 224.672714 \n", + "14 225.450668 224.672714 \n", + "15 224.019119 224.672714 \n", + "16 222.521698 224.672714 \n", + "17 220.991913 224.672714 \n", + "18 219.454437 224.672714 \n", + "19 217.966431 224.672714 \n", + "20 216.519226 224.672714 \n", + "21 215.057770 224.672714 \n", + "22 213.577286 224.672714 \n", + "23 212.050201 224.672714 \n", + "24 210.448868 224.672714 \n", + "25 208.823868 224.672714 \n", + "26 207.131531 224.672714 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "final_oil = final_ensemble.set_index(\"realization\")[\"cumulative_oil_MSm3\"]\n", + "median_target = final_oil.median()\n", + "selected_realization = int((final_oil - median_target).abs().idxmin())\n", + "reservoir_history = ensemble_history.loc[\n", + " ensemble_history[\"realization\"] == selected_realization\n", + "].reset_index(drop=True)\n", + "print(\"Selected median-like realization:\", selected_realization)\n", + "display(reservoir_history)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "0c42e836", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:30.564167Z", + "iopub.status.busy": "2026-08-31T17:05:30.563972Z", + "iopub.status.idle": "2026-08-31T17:05:30.576237Z", + "shell.execute_reply": "2026-08-31T17:05:30.574151Z" + } + }, + "outputs": [], + "source": [ + "stock_oil_template = stock_tank_state.phaseToSystem(\"oil\")\n", + "stock_gas_template = stock_tank_state.phaseToSystem(\"gas\")\n", + "\n", + "water_probe = jneqsim.thermo.system.SystemSrkEos(\n", + " STANDARD_TEMPERATURE_C + 273.15,\n", + " STANDARD_PRESSURE_BARA,\n", + ")\n", + "water_probe.addComponent(\"water\", 1.0)\n", + "water_molar_mass_kg_mol = (\n", + " water_probe.getComponent(\"water\").getMolarMass()\n", + ")\n", + "\n", + "\n", + "def reconstruct_characterized_wellhead_fluid(\n", + " oil_rate_sm3_day,\n", + " gas_rate_sm3_day,\n", + " water_rate_sm3_day,\n", + " wellhead_temperature_c=65.0,\n", + " wellhead_pressure_bara=70.0,\n", + "):\n", + " stock_oil = stock_oil_template.clone()\n", + " stock_gas = stock_gas_template.clone()\n", + " stock_oil.setTotalFlowRate(float(oil_rate_sm3_day), \"m3/day\")\n", + " stock_gas.setTotalFlowRate(float(gas_rate_sm3_day), \"Sm3/day\")\n", + "\n", + " clearProcess()\n", + " oil_component_stream = stream(\n", + " \"Black-oil stock-tank oil component\",\n", + " stock_oil,\n", + " )\n", + " gas_component_stream = stream(\n", + " \"Black-oil surface-gas component\",\n", + " stock_gas,\n", + " )\n", + " recombination_mixer = mixer(\n", + " \"Black-oil to characterized-fluid recombination\"\n", + " )\n", + " recombination_mixer.addStream(oil_component_stream)\n", + " recombination_mixer.addStream(gas_component_stream)\n", + " runProcess()\n", + "\n", + " reconstructed_fluid = (\n", + " recombination_mixer.getOutletStream().getFluid().clone()\n", + " )\n", + " hydrocarbon_mass_flow_kg_s = (\n", + " reconstructed_fluid.getFlowRate(\"kg/sec\")\n", + " )\n", + " water_mass_flow_kg_s = (\n", + " float(water_rate_sm3_day)\n", + " * stock_water_density_kg_m3\n", + " / SECONDS_PER_DAY\n", + " )\n", + " water_molar_flow_mol_s = (\n", + " water_mass_flow_kg_s / water_molar_mass_kg_mol\n", + " )\n", + "\n", + " reconstructed_fluid.addComponent(\n", + " \"water\",\n", + " water_molar_flow_mol_s,\n", + " )\n", + " reconstructed_fluid.setMixingRule(\"classic\")\n", + " reconstructed_fluid.setMultiPhaseCheck(True)\n", + " reconstructed_fluid.setTemperature(\n", + " wellhead_temperature_c,\n", + " \"C\",\n", + " )\n", + " reconstructed_fluid.setPressure(\n", + " wellhead_pressure_bara,\n", + " \"bara\",\n", + " )\n", + " TPflash(reconstructed_fluid)\n", + " reconstructed_fluid.initPhysicalProperties()\n", + "\n", + " expected_mass_flow_kg_s = (\n", + " hydrocarbon_mass_flow_kg_s + water_mass_flow_kg_s\n", + " )\n", + " audit = {\n", + " \"expected_mass_flow_kg_s\": expected_mass_flow_kg_s,\n", + " \"calculated_mass_flow_kg_s\": (\n", + " reconstructed_fluid.getFlowRate(\"kg/sec\")\n", + " ),\n", + " \"mass_residual_kg_s\": (\n", + " reconstructed_fluid.getFlowRate(\"kg/sec\")\n", + " - expected_mass_flow_kg_s\n", + " ),\n", + " }\n", + " return reconstructed_fluid, audit" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "fcd9a3cd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:30.579259Z", + "iopub.status.busy": "2026-08-31T17:05:30.578949Z", + "iopub.status.idle": "2026-08-31T17:05:31.304601Z", + "shell.execute_reply": "2026-08-31T17:05:31.302942Z" + } + }, + "outputs": [], + "source": [ + "reservoir_history[\"estimated_surface_mass_kg_s\"] = (\n", + " reservoir_history[\"oil_rate_Sm3_day\"] * stock_oil_density_kg_m3\n", + " + reservoir_history[\"gas_rate_Sm3_day\"] * stock_gas_density_kg_m3\n", + " + reservoir_history[\"water_rate_Sm3_day\"]\n", + " * stock_water_density_kg_m3\n", + ") / SECONDS_PER_DAY\n", + "\n", + "facility_design_index = int(\n", + " reservoir_history[\"estimated_surface_mass_kg_s\"].idxmax()\n", + ")\n", + "facility_design_row = reservoir_history.iloc[facility_design_index]\n", + "facility_feed_fluid, facility_feed_audit = (\n", + " reconstruct_characterized_wellhead_fluid(\n", + " facility_design_row[\"oil_rate_Sm3_day\"],\n", + " facility_design_row[\"gas_rate_Sm3_day\"],\n", + " facility_design_row[\"water_rate_Sm3_day\"],\n", + " )\n", + ")\n", + "\n", + "clearProcess()\n", + "reservoir_export_stream = stream(\n", + " \"OPM-derived characterized wellhead stream\",\n", + " facility_feed_fluid,\n", + ")\n", + "wellhead_choke = valve(\n", + " \"Wellhead choke\",\n", + " reservoir_export_stream,\n", + " p=55.0,\n", + ")\n", + "hp_separator = separator3phase(\n", + " \"HP three-phase separator\",\n", + " wellhead_choke.getOutletStream(),\n", + ")\n", + "gas_compressor = compressor(\n", + " \"Export gas compressor\",\n", + " hp_separator.getGasOutStream(),\n", + " pres=120.0,\n", + ")\n", + "gas_compressor.setIsentropicEfficiency(0.75)\n", + "gas_aftercooler = cooler(\n", + " \"Export gas aftercooler\",\n", + " gas_compressor.getOutletStream(),\n", + ")\n", + "gas_aftercooler.setOutTemperature(35.0 + 273.15)\n", + "oil_valve = valve(\n", + " \"Oil letdown valve\",\n", + " hp_separator.getOilOutStream(),\n", + " p=10.0,\n", + ")\n", + "lp_separator = separator(\n", + " \"LP oil separator\",\n", + " oil_valve.getOutletStream(),\n", + ")\n", + "runProcess()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3798c345", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:31.307526Z", + "iopub.status.busy": "2026-08-31T17:05:31.307080Z", + "iopub.status.idle": "2026-08-31T17:05:31.749746Z", + "shell.execute_reply": "2026-08-31T17:05:31.748852Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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streammass_flow_kg_stemperature_Cpressure_baraphase_count
0OPM-derived characterized wellhead stream3.044458e+0165.00000070.02
1Wellhead choke out stream3.044458e+0158.91788755.02
2gasOutStream2.572056e+0158.91788755.01
3liquidOutStream4.724020e+0058.91788755.01
4waterOutStream5.507958e-2958.91788755.02
5outStream2.572056e+0135.000000120.01
6gasOutStream8.456356e-0138.40487810.01
7liquidOutStream3.878385e+0038.40487810.01
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" + ], + "text/plain": [ + " stream mass_flow_kg_s temperature_C pressure_bara phase_count\n", + "0 OPM-derived characterized wellhead stream 3.044458e+01 65.000000 70.0 2\n", + "1 Wellhead choke out stream 3.044458e+01 58.917887 55.0 2\n", + "2 gasOutStream 2.572056e+01 58.917887 55.0 1\n", + "3 liquidOutStream 4.724020e+00 58.917887 55.0 1\n", + "4 waterOutStream 5.507958e-29 58.917887 55.0 2\n", + "5 outStream 2.572056e+01 35.000000 120.0 1\n", + "6 gasOutStream 8.456356e-01 38.404878 10.0 1\n", + "7 liquidOutStream 3.878385e+00 38.404878 10.0 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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quantityvalueunit
0Flow forecast time2.000000e+00year
1Flow oil rate1.000000e+04Sm³/day
2Flow gas rate2.246727e+06Sm³/day
3Flow water rate1.071783e-02Sm³/day
4NeqSim feed mass rate3.044458e+01kg/s
5process mass residual5.423573e-11kg/s
6compressor power2.680346e+00MW
7aftercooler duty-7.344375e+00MW
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" + ], + "text/plain": [ + " quantity value unit\n", + "0 Flow forecast time 2.000000e+00 year\n", + "1 Flow oil rate 1.000000e+04 Sm³/day\n", + "2 Flow gas rate 2.246727e+06 Sm³/day\n", + "3 Flow water rate 1.071783e-02 Sm³/day\n", + "4 NeqSim feed mass rate 3.044458e+01 kg/s\n", + "5 process mass residual 5.423573e-11 kg/s\n", + "6 compressor power 2.680346e+00 MW\n", + "7 aftercooler duty -7.344375e+00 MW" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "process_streams = [\n", + " reservoir_export_stream,\n", + " wellhead_choke.getOutletStream(),\n", + " hp_separator.getGasOutStream(),\n", + " hp_separator.getOilOutStream(),\n", + " hp_separator.getWaterOutStream(),\n", + " gas_aftercooler.getOutletStream(),\n", + " lp_separator.getGasOutStream(),\n", + " lp_separator.getLiquidOutStream(),\n", + "]\n", + "\n", + "process_stream_table = pd.DataFrame(\n", + " [\n", + " {\n", + " \"stream\": str(process_stream.getName()),\n", + " \"mass_flow_kg_s\": process_stream.getFlowRate(\"kg/sec\"),\n", + " \"temperature_C\": process_stream.getTemperature(\"C\"),\n", + " \"pressure_bara\": process_stream.getPressure(\"bara\"),\n", + " \"phase_count\": (\n", + " process_stream.getFluid().getNumberOfPhases()\n", + " ),\n", + " }\n", + " for process_stream in process_streams\n", + " ]\n", + ")\n", + "\n", + "process_outlet_streams = [\n", + " gas_aftercooler.getOutletStream(),\n", + " lp_separator.getGasOutStream(),\n", + " lp_separator.getLiquidOutStream(),\n", + " hp_separator.getWaterOutStream(),\n", + "]\n", + "process_outlet_mass_flow_kg_s = sum(\n", + " process_stream.getFlowRate(\"kg/sec\")\n", + " for process_stream in process_outlet_streams\n", + ")\n", + "process_mass_residual_kg_s = (\n", + " reservoir_export_stream.getFlowRate(\"kg/sec\")\n", + " - process_outlet_mass_flow_kg_s\n", + ")\n", + "\n", + "process_performance = pd.DataFrame(\n", + " {\n", + " \"quantity\": [\n", + " \"Flow forecast time\",\n", + " \"Flow oil rate\",\n", + " \"Flow gas rate\",\n", + " \"Flow water rate\",\n", + " \"NeqSim feed mass rate\",\n", + " \"process mass residual\",\n", + " \"compressor power\",\n", + " \"aftercooler duty\",\n", + " ],\n", + " \"value\": [\n", + " facility_design_row[\"time_days\"] / 365.25,\n", + " facility_design_row[\"oil_rate_Sm3_day\"],\n", + " facility_design_row[\"gas_rate_Sm3_day\"],\n", + " facility_design_row[\"water_rate_Sm3_day\"],\n", + " reservoir_export_stream.getFlowRate(\"kg/sec\"),\n", + " process_mass_residual_kg_s,\n", + " gas_compressor.getPower() / 1.0e6,\n", + " gas_aftercooler.getDuty() / 1.0e6,\n", + " ],\n", + " \"unit\": [\n", + " \"year\",\n", + " \"Sm³/day\",\n", + " \"Sm³/day\",\n", + " \"Sm³/day\",\n", + " \"kg/s\",\n", + " \"kg/s\",\n", + " \"MW\",\n", + " \"MW\",\n", + " ],\n", + " }\n", + ")\n", + "\n", + "display(process_stream_table)\n", + "display(process_performance)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7bf1d542", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:31.752802Z", + "iopub.status.busy": "2026-08-31T17:05:31.752551Z", + "iopub.status.idle": "2026-08-31T17:05:32.228170Z", + "shell.execute_reply": "2026-08-31T17:05:32.227347Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "process_figure, process_axis = plt.subplots(figsize=(12.0, 3.6))\n", + "process_axis.set_xlim(0.0, 12.0)\n", + "process_axis.set_ylim(0.0, 3.5)\n", + "process_axis.axis(\"off\")\n", + "\n", + "process_nodes = [\n", + " (0.6, 1.15, \"OPM rates\\n+ NeqSim fluid\", \"#0072B2\"),\n", + " (3.2, 1.15, \"Choke\\n70 → 55 bara\", \"#E69F00\"),\n", + " (5.8, 1.15, \"HP three-phase\\nseparator\", \"#009E73\"),\n", + " (8.6, 2.15, \"Gas compression\\n+ cooling\", \"#CC79A7\"),\n", + " (8.6, 0.15, \"Oil letdown\\n+ LP flash\", \"#D55E00\"),\n", + "]\n", + "\n", + "for x_position, y_position, label, color in process_nodes:\n", + " process_axis.add_patch(\n", + " plt.Rectangle(\n", + " (x_position, y_position),\n", + " 2.1,\n", + " 1.0,\n", + " facecolor=color,\n", + " edgecolor=\"black\",\n", + " alpha=0.9,\n", + " )\n", + " )\n", + " process_axis.text(\n", + " x_position + 1.05,\n", + " y_position + 0.5,\n", + " label,\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"white\",\n", + " fontweight=\"bold\",\n", + " )\n", + "\n", + "arrow_pairs = [\n", + " ((2.7, 1.65), (3.2, 1.65)),\n", + " ((5.3, 1.65), (5.8, 1.65)),\n", + " ((7.9, 1.85), (8.6, 2.65)),\n", + " ((7.9, 1.45), (8.6, 0.65)),\n", + " ((7.9, 1.65), (10.5, 1.65)),\n", + "]\n", + "for arrow_start, arrow_end in arrow_pairs:\n", + " process_axis.annotate(\n", + " \"\",\n", + " xy=arrow_end,\n", + " xytext=arrow_start,\n", + " arrowprops={\"arrowstyle\": \"->\", \"linewidth\": 2.0},\n", + " )\n", + "\n", + "process_axis.text(\n", + " 11.2,\n", + " 1.65,\n", + " \"HP water outlet\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"#0072B2\",\n", + ")\n", + "process_axis.set_title(\n", + " \"OPM Flow surface components reconstructed as a NeqSim process feed\"\n", + ")\n", + "\n", + "process_plot_path = OUTPUT_DIRECTORY / \"integrated_process_workflow.png\"\n", + "process_figure.savefig(process_plot_path, dpi=150)\n", + "plt.show()\n", + "FIGURE_PATHS.append(process_plot_path)" + ] + }, + { + "cell_type": "markdown", + "id": "a38889d0", + "metadata": {}, + "source": [ + "## 12. Complete input and artifact inventory\n", + "\n", + "The tables below list every downloaded input and every generated simulator input with byte count\n", + "and SHA-256. The complete deck, PVT include, relative-permeability tables, ERT configuration,\n", + "priors, and Jinja template were printed above. Cell-scale arrays remain available through their\n", + "immutable ROFF URLs and generated GRDECL files; checksums prove the exact bytes used." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "39cd2337", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:32.230326Z", + "iopub.status.busy": "2026-08-31T17:05:32.230146Z", + "iopub.status.idle": "2026-08-31T17:05:32.259897Z", + "shell.execute_reply": "2026-08-31T17:05:32.259093Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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rolefilebytessha256
0downloaded public inputinput/0readme.txt1498f76ce966e77a13cdeb1be9bf021b330719f34ac51d810...
1downloaded public inputinput/reek_geo2_grid_3props.roff7837496cf199d7126dc96b574e05c6709a5216f454e2da7ff5e01...
2downloaded public inputinput/reek_sim_grid.roff3765806454b7aa1d1f2701438310b8b3dc3101e70dd9643b49ec...
3downloaded public inputinput/reek_sim_poro.roff1437187368cc75d0436c3816fd16d61f77621c00f4f97ec25f0f...
4downloaded public inputinput/reek_sim_permx.roff14371937866e0fb8d9ab8e036f2ed9d34537a7274efbf6346652...
5downloaded public inputinput/reek_sim_facies2.roff1437866aeb52f6dd41f1e0783fb998a92b3da08cfa0f8fd5621f...
6downloaded public inputinput/reek_sim_zone.roff1438251d8f60577a4da72b6234eb036e8d1d0248513fb93d61b9...
7generated simulation inputREEK_GRID.GRDECL5576400f52cc87ee49ae0f715e504910386c67593be7a301d36e8...
8generated simulation inputPORO.GRDECL471901bd6e51ace0e867d61c4f72504109bb679e2dfc5124454e...
9generated simulation inputPERMX.GRDECL471902885fb2e50c8de23b22efff0b5d27f356bb25a736de2b5f...
10generated simulation inputPERMY.GRDECL4719021b6ed1e344aaa150090c01c7f3e883c7005e201990f4b6...
11generated simulation inputPERMZ.GRDECL4719022948737c53ad15e1c93d632e1cd90f436dfe113ad35d10...
12generated simulation inputFIPNUM.GRDECL776638de3e8fada8dc2e50075547bfb07e5c1dd2935018e0f41...
13generated simulation inputNEQSIM_PVT.INC1098df4097d1174216c39eec0430349a67e66de41aa89c725d...
14generated simulation inputRMS_REEK_BASE.DATA33189ea7e123b228fc48e0c1cdf17054b2f81a6ee89676cff3...
15generated simulation inputert/parameters.txt8800c717339f24ce4f72beea90f28e09c2450c972cb081fc...
16generated simulation inputert/RMS_REEK.DATA.jinja2344830e62c87fa79bd571013bd8079e1e368441156dfeaf53a...
17generated simulation inputert/rms_reek.ert43707608431237796ef45ed231b2bda62a490dfcd67af65dd...
18generated simulation inputagent_job.json13579112740df61434d4a7bda5c4bf45ff2a8f86ce23f5d954...
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" + ], + "text/plain": [ + " role file bytes sha256\n", + "0 downloaded public input input/0readme.txt 149 8f76ce966e77a13cdeb1be9bf021b330719f34ac51d810...\n", + "1 downloaded public input input/reek_geo2_grid_3props.roff 7837496 cf199d7126dc96b574e05c6709a5216f454e2da7ff5e01...\n", + "2 downloaded public input input/reek_sim_grid.roff 376580 6454b7aa1d1f2701438310b8b3dc3101e70dd9643b49ec...\n", + "3 downloaded public input input/reek_sim_poro.roff 143718 7368cc75d0436c3816fd16d61f77621c00f4f97ec25f0f...\n", + "4 downloaded public input input/reek_sim_permx.roff 143719 37866e0fb8d9ab8e036f2ed9d34537a7274efbf6346652...\n", + "5 downloaded public input input/reek_sim_facies2.roff 143786 6aeb52f6dd41f1e0783fb998a92b3da08cfa0f8fd5621f...\n", + "6 downloaded public input input/reek_sim_zone.roff 143825 1d8f60577a4da72b6234eb036e8d1d0248513fb93d61b9...\n", + "7 generated simulation input REEK_GRID.GRDECL 5576400 f52cc87ee49ae0f715e504910386c67593be7a301d36e8...\n", + "8 generated simulation input PORO.GRDECL 471901 bd6e51ace0e867d61c4f72504109bb679e2dfc5124454e...\n", + "9 generated simulation input PERMX.GRDECL 471902 885fb2e50c8de23b22efff0b5d27f356bb25a736de2b5f...\n", + "10 generated simulation input PERMY.GRDECL 471902 1b6ed1e344aaa150090c01c7f3e883c7005e201990f4b6...\n", + "11 generated simulation input PERMZ.GRDECL 471902 2948737c53ad15e1c93d632e1cd90f436dfe113ad35d10...\n", + "12 generated simulation input FIPNUM.GRDECL 77663 8de3e8fada8dc2e50075547bfb07e5c1dd2935018e0f41...\n", + "13 generated simulation input NEQSIM_PVT.INC 1098 df4097d1174216c39eec0430349a67e66de41aa89c725d...\n", + "14 generated simulation input RMS_REEK_BASE.DATA 3318 9ea7e123b228fc48e0c1cdf17054b2f81a6ee89676cff3...\n", + "15 generated simulation input ert/parameters.txt 88 00c717339f24ce4f72beea90f28e09c2450c972cb081fc...\n", + "16 generated simulation input ert/RMS_REEK.DATA.jinja2 3448 30e62c87fa79bd571013bd8079e1e368441156dfeaf53a...\n", + "17 generated simulation input ert/rms_reek.ert 437 07608431237796ef45ed231b2bda62a490dfcd67af65dd...\n", + "18 generated simulation input agent_job.json 1357 9112740df61434d4a7bda5c4bf45ff2a8f86ce23f5d954..." + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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sequencetoolstatusevidence
01rms.inspect_projectfixture_passed{'data_commit': 'cad17f24e22c19c6cefe6f6471853...
12rms.run_workflowfixture_passed{'workflow': 'REEK_BLOCK_AND_EXPORT_V1', 'bloc...
23rms.export_gridfixture_passed{'grid_sha256': 'f52cc87ee49ae0f715e504910386c...
34neqsim.generate_pvtpassed{'source_commit': '1f7a01b06307d9d56451e43bb8d...
45opm.validatepassed{'keywords': 17}
56opm.runpassed{'return_code': 0, 'restart_steps': 25}
67ert.ensemble_experimentpassed{'return_code': 0, 'wall_time_seconds': 38.021...
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" + ], + "text/plain": [ + " sequence tool status evidence\n", + "0 1 rms.inspect_project fixture_passed {'data_commit': 'cad17f24e22c19c6cefe6f6471853...\n", + "1 2 rms.run_workflow fixture_passed {'workflow': 'REEK_BLOCK_AND_EXPORT_V1', 'bloc...\n", + "2 3 rms.export_grid fixture_passed {'grid_sha256': 'f52cc87ee49ae0f715e504910386c...\n", + "3 4 neqsim.generate_pvt passed {'source_commit': '1f7a01b06307d9d56451e43bb8d...\n", + "4 5 opm.validate passed {'keywords': 17}\n", + "5 6 opm.run passed {'return_code': 0, 'restart_steps': 25}\n", + "6 7 ert.ensemble_experiment passed {'return_code': 0, 'wall_time_seconds': 38.021..." + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "input_artifact_paths = (\n", + " [DATA_DIRECTORY / row[0] for row in DATA_MANIFEST]\n", + " + generated_static_paths\n", + " + [black_oil_path, deck_path, ERT_DIRECTORY / \"parameters.txt\",\n", + " ERT_DIRECTORY / \"RMS_REEK.DATA.jinja2\", ert_config_path, job_path]\n", + ")\n", + "input_inventory = pd.DataFrame([\n", + " {\n", + " \"role\": \"downloaded public input\" if path.parent == DATA_DIRECTORY else \"generated simulation input\",\n", + " \"file\": path.relative_to(OUTPUT_DIRECTORY).as_posix(),\n", + " \"bytes\": path.stat().st_size,\n", + " \"sha256\": hashlib.sha256(path.read_bytes()).hexdigest(),\n", + " }\n", + " for path in input_artifact_paths\n", + "])\n", + "display(input_inventory)\n", + "\n", + "ledger_path = OUTPUT_DIRECTORY / \"agent_tool_ledger.json\"\n", + "ledger_path.write_text(json.dumps(agent_ledger.records, indent=2) + \"\\n\", encoding=\"utf-8\")\n", + "display(agent_ledger.frame())" + ] + }, + { + "cell_type": "markdown", + "id": "76a1e692", + "metadata": {}, + "source": [ + "## 13. Engineering validation gates\n", + "\n", + "These assertions are publication gates, not decorative status labels. They check provenance,\n", + "dimensions, blocking, property physics, deck parsing, the real Flow process, restart saturation\n", + "closure, all four ERT simulator runs, NeqSim PVT contracts, the process mass balance, and the\n", + "agent ledger. A failed check raises and leaves the notebook unpublishable." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "6e6fe7fb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:32.261692Z", + "iopub.status.busy": "2026-08-31T17:05:32.261513Z", + "iopub.status.idle": "2026-08-31T17:05:32.277563Z", + "shell.execute_reply": "2026-08-31T17:05:32.276762Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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checkpassed
0all seven public files match SHA-256 and byte ...True
1fine grid is 80 x 128 x 56True
2simulation grid is 40 x 64 x 14True
3fine-to-simulation blocking is exactly 2 x 2 x 4True
4simulation grid has 35,838 active cellsTrue
5porosity remains between zero and oneTrue
6permeability is positiveTrue
7blocked porosity is finite on compared blocksTrue
8all generated static includes are nonemptyTrue
9all required Flow deck keywords parseTrue
10all NeqSim PVT contracts passTrue
11OPM Flow returned successTrue
12Flow summary is finiteTrue
13restart active count matches XTGeoTrue
14restart has 24 forecast steps plus initial stateTrue
15restart states are finite on active cellsTrue
16final saturations closeTrue
17ERT lint returned successTrue
18ERT ensemble returned successTrue
19all four ERT SMSPEC files existTrue
20ERT parsed four realizationsTrue
21NeqSim process compressor consumes powerTrue
22NeqSim process mass balance closesTrue
23at least eleven retained figures existTrue
24agent ledger records all executed stages as pa...True
25job contract contains no filesystem project pathTrue
\n", + "
" + ], + "text/plain": [ + " check passed\n", + "0 all seven public files match SHA-256 and byte ... True\n", + "1 fine grid is 80 x 128 x 56 True\n", + "2 simulation grid is 40 x 64 x 14 True\n", + "3 fine-to-simulation blocking is exactly 2 x 2 x 4 True\n", + "4 simulation grid has 35,838 active cells True\n", + "5 porosity remains between zero and one True\n", + "6 permeability is positive True\n", + "7 blocked porosity is finite on compared blocks True\n", + "8 all generated static includes are nonempty True\n", + "9 all required Flow deck keywords parse True\n", + "10 all NeqSim PVT contracts pass True\n", + "11 OPM Flow returned success True\n", + "12 Flow summary is finite True\n", + "13 restart active count matches XTGeo True\n", + "14 restart has 24 forecast steps plus initial state True\n", + "15 restart states are finite on active cells True\n", + "16 final saturations close True\n", + "17 ERT lint returned success True\n", + "18 ERT ensemble returned success True\n", + "19 all four ERT SMSPEC files exist True\n", + "20 ERT parsed four realizations True\n", + "21 NeqSim process compressor consumes power True\n", + "22 NeqSim process mass balance closes True\n", + "23 at least eleven retained figures exist True\n", + "24 agent ledger records all executed stages as pa... True\n", + "25 job contract contains no filesystem project path True" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All 26 engineering validation gates passed.\n" + ] + } + ], + "source": [ + "all_ert_smspec = [\n", + " ERT_DIRECTORY / \"runs\" / f\"realization-{realization}\" / \"iter-0\" / \"RMS_REEK.SMSPEC\"\n", + " for realization in range(4)\n", + "]\n", + "saturation_sum = final_water_saturation_cube + final_gas_saturation_cube\n", + "validation_checks = {\n", + " \"all seven public files match SHA-256 and byte count\": bool(download_table[\"verified\"].all()),\n", + " \"fine grid is 80 x 128 x 56\": (fine_grid.ncol, fine_grid.nrow, fine_grid.nlay) == (80, 128, 56),\n", + " \"simulation grid is 40 x 64 x 14\": (sim_grid.ncol, sim_grid.nrow, sim_grid.nlay) == (40, 64, 14),\n", + " \"fine-to-simulation blocking is exactly 2 x 2 x 4\": BLOCK == (2, 2, 4),\n", + " \"simulation grid has 35,838 active cells\": sim_grid.nactive == 35838,\n", + " \"porosity remains between zero and one\": bool(active_poro.min() > 0 and active_poro.max() < 1),\n", + " \"permeability is positive\": bool(active_permx.min() > 0),\n", + " \"blocked porosity is finite on compared blocks\": bool(np.isfinite(blocked_poro[comparison_mask]).all()),\n", + " \"all generated static includes are nonempty\": all(path.stat().st_size > 0 for path in generated_static_paths),\n", + " \"all required Flow deck keywords parse\": bool(deck_keyword_audit[\"present\"].all()),\n", + " \"all NeqSim PVT contracts pass\": bool(pvt_contract_audit[\"passed\"].all()),\n", + " \"OPM Flow returned success\": flow_run.returncode == 0,\n", + " \"Flow summary is finite\": bool(np.isfinite(reservoir_history.select_dtypes(include=[np.number])).all().all()),\n", + " \"restart active count matches XTGeo\": active_cell_count == sim_grid.nactive,\n", + " \"restart has 24 forecast steps plus initial state\": len(restart_steps) in (24, 25),\n", + " \"restart states are finite on active cells\": bool(np.isfinite(final_pressure_cube_bara[~np.isnan(final_pressure_cube_bara)]).all()),\n", + " \"final saturations close\": bool(np.nanmin(final_oil_saturation_cube) >= -1e-6 and np.nanmax(saturation_sum) <= 1 + 1e-6),\n", + " \"ERT lint returned success\": ert_lint.returncode == 0,\n", + " \"ERT ensemble returned success\": ert_run.returncode == 0,\n", + " \"all four ERT SMSPEC files exist\": all(path.is_file() for path in all_ert_smspec),\n", + " \"ERT parsed four realizations\": ensemble_history[\"realization\"].nunique() == 4,\n", + " \"NeqSim process compressor consumes power\": gas_compressor.getPower() > 0,\n", + " \"NeqSim process mass balance closes\": abs(process_mass_residual_kg_s) < 1e-8,\n", + " \"at least eleven retained figures exist\": len(list(OUTPUT_DIRECTORY.glob(\"*.png\"))) >= 11,\n", + " \"agent ledger records all executed stages as passed\": all(record[\"status\"].endswith(\"passed\") for record in agent_ledger.records),\n", + " \"job contract contains no filesystem project path\": \"project_path\" not in json.dumps(agent_job),\n", + "}\n", + "validation_table = pd.DataFrame({\"check\": validation_checks.keys(), \"passed\": validation_checks.values()})\n", + "display(validation_table)\n", + "failed = validation_table.loc[~validation_table[\"passed\"], \"check\"].tolist()\n", + "if failed:\n", + " raise AssertionError(f\"Failed validation checks: {failed}\")\n", + "print(f\"All {len(validation_checks)} engineering validation gates passed.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4a9dba2f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-31T17:05:32.279331Z", + "iopub.status.busy": "2026-08-31T17:05:32.279152Z", + "iopub.status.idle": "2026-08-31T17:05:32.290625Z", + "shell.execute_reply": "2026-08-31T17:05:32.289865Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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resultvalueunit
0Active cells35838.000000count
1NeqSim bubble pressure193.240400bara
2Base final pressure223.618027bara
3Base cumulative oil7.305000million Sm3
4ERT low final oil7.305000million Sm3
5ERT high final oil7.305000million Sm3
6Selected compressor power2.680346MW
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" + ], + "text/plain": [ + " result value unit\n", + "0 Active cells 35838.000000 count\n", + "1 NeqSim bubble pressure 193.240400 bara\n", + "2 Base final pressure 223.618027 bara\n", + "3 Base cumulative oil 7.305000 million Sm3\n", + "4 ERT low final oil 7.305000 million Sm3\n", + "5 ERT high final oil 7.305000 million Sm3\n", + "6 Selected compressor power 2.680346 MW" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"data_commit\": \"cad17f24e22c19c6cefe6f647185395cc0a11add\",\n", + " \"neqsim_commit\": \"1f7a01b06307d9d56451e43bb8d6023d28d14007\",\n", + " \"neqsim_jar_sha256\": \"1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739\",\n", + " \"grid_dimensions\": [\n", + " 40,\n", + " 64,\n", + " 14\n", + " ],\n", + " \"active_cells\": 35838,\n", + " \"blocking\": [\n", + " 2,\n", + " 2,\n", + " 4\n", + " ],\n", + " \"porosity_blocking_rmse\": 0.0918770723782011,\n", + " \"bubble_pressure_bara\": 193.24040031433105,\n", + " \"base_final_pressure_bara\": 223.61802673339844,\n", + " \"base_final_cumulative_oil_million_sm3\": 7.305,\n", + " \"ert_realizations\": 4,\n", + " \"ert_final_oil_range_million_sm3\": [\n", + " 7.305,\n", + " 7.305\n", + " ],\n", + " \"selected_process_realization\": 0,\n", + " \"compressor_power_MW\": 2.680346000234267,\n", + " \"process_mass_residual_kg_s\": 5.423572702056845e-11,\n", + " \"figures\": [\n", + " \"workflow_architecture.png\",\n", + " \"reek_structure_and_static_maps.png\",\n", + " \"reek_property_distributions.png\",\n", + " \"reek_blocking_comparison.png\",\n", + " \"reek_property_spreading_layers.png\",\n", + " \"reek_well_screening.png\",\n", + " \"neqsim_black_oil_pvt.png\",\n", + " \"opm_flow_forecast.png\",\n", + " \"opm_final_restart_maps.png\",\n", + " \"opm_3d_water_front.png\",\n", + " \"ert_flow_ensemble.png\",\n", + " \"ert_parameter_response.png\",\n", + " \"integrated_process_workflow.png\"\n", + " ],\n", + " \"validation_checks_passed\": 26,\n", + " \"validation_checks_total\": 26\n", + "}\n" + ] + } + ], + "source": [ + "final_results = {\n", + " \"data_commit\": DATA_COMMIT,\n", + " \"neqsim_commit\": neqsim_commit,\n", + " \"neqsim_jar_sha256\": neqsim_jar_sha256,\n", + " \"grid_dimensions\": [grid_nx, grid_ny, grid_nz],\n", + " \"active_cells\": active_cell_count,\n", + " \"blocking\": list(BLOCK),\n", + " \"porosity_blocking_rmse\": float(np.sqrt(np.mean(poro_difference ** 2))),\n", + " \"bubble_pressure_bara\": float(bubble_pressure_bara),\n", + " \"base_final_pressure_bara\": float(summary_values(\"FPR\")[-1]),\n", + " \"base_final_cumulative_oil_million_sm3\": float(summary_values(\"FOPT\")[-1] / 1e6),\n", + " \"ert_realizations\": int(ensemble_history[\"realization\"].nunique()),\n", + " \"ert_final_oil_range_million_sm3\": [\n", + " float(final_ensemble[\"cumulative_oil_MSm3\"].min()),\n", + " float(final_ensemble[\"cumulative_oil_MSm3\"].max()),\n", + " ],\n", + " \"selected_process_realization\": selected_realization,\n", + " \"compressor_power_MW\": float(gas_compressor.getPower() / 1e6),\n", + " \"process_mass_residual_kg_s\": float(process_mass_residual_kg_s),\n", + " \"figures\": [path.name for path in FIGURE_PATHS],\n", + " \"validation_checks_passed\": int(validation_table[\"passed\"].sum()),\n", + " \"validation_checks_total\": len(validation_table),\n", + "}\n", + "results_path = OUTPUT_DIRECTORY / \"run_results.json\"\n", + "results_path.write_text(json.dumps(final_results, indent=2) + \"\\n\", encoding=\"utf-8\")\n", + "display(pd.DataFrame([\n", + " (\"Active cells\", active_cell_count, \"count\"),\n", + " (\"NeqSim bubble pressure\", bubble_pressure_bara, \"bara\"),\n", + " (\"Base final pressure\", final_results[\"base_final_pressure_bara\"], \"bara\"),\n", + " (\"Base cumulative oil\", final_results[\"base_final_cumulative_oil_million_sm3\"], \"million Sm3\"),\n", + " (\"ERT low final oil\", final_results[\"ert_final_oil_range_million_sm3\"][0], \"million Sm3\"),\n", + " (\"ERT high final oil\", final_results[\"ert_final_oil_range_million_sm3\"][1], \"million Sm3\"),\n", + " (\"Selected compressor power\", final_results[\"compressor_power_MW\"], \"MW\"),\n", + "], columns=[\"result\", \"value\", \"unit\"]).round(6))\n", + "print(json.dumps(final_results, indent=2))" + ] + }, + { + "cell_type": "markdown", + "id": "ea632faf", + "metadata": {}, + "source": [ + "## What is demonstrated, and what is not\n", + "\n", + "**Demonstrated with stored execution evidence**\n", + "\n", + "- public RMS-origin ROFF ingestion;\n", + "- geological-to-simulation blocking calculations;\n", + "- RMS-export property inspection and spatial spreading;\n", + "- NeqSim master-source PVT generation;\n", + "- complete OPM Flow deck generation and dynamic simulation;\n", + "- restart-state visualization;\n", + "- real ERT orchestration of four OPM Flow realizations;\n", + "- reservoir-to-NeqSim facility handover;\n", + "- machine-readable agent request, allow-list, acceptance checks, and tool ledger.\n", + "\n", + "**Not claimed**\n", + "\n", + "- The public Reek export is not a confidential asset model.\n", + "- The explicit teaching facies map is not asserted to be the archived RMS workflow definition.\n", + "- PERMY and PERMZ multipliers are assumptions, not measurements.\n", + "- The wells and controls are visualization choices, not an optimized development plan.\n", + "- Four ERT realizations demonstrate integration; they do not quantify decision-grade uncertainty.\n", + "- The notebook does not execute licensed RMS. That execution belongs on a governed worker.\n", + "- PVT, rock-fluid functions, contacts, and controls are illustrative and are not history matched.\n", + "\n", + "A production implementation should add RMS project snapshots, signed artifacts, scheduler/resource\n", + "limits, secret-free workload identity, approval gates, observation ingestion, ERT update steps,\n", + "and domain review before model promotion." + ] + }, + { + "cell_type": "markdown", + "id": "a3bc19db", + "metadata": {}, + "source": [ + "## Suggested exercises\n", + "\n", + "1. Replace the supplied simulation PORO with the calculated blocked porosity and compare Flow.\n", + "2. Implement harmonic, arithmetic, and flow-based directional permeability upscaling.\n", + "3. Add region-specific SWOF/SGOF tables using facies or Zone.\n", + "4. Increase the ERT ensemble and add observed well data for an ensemble smoother experiment.\n", + "5. Add fault transmissibility multipliers and inspect water-front sensitivity.\n", + "6. Replace the public-fixture adapter with an authenticated licensed RMS worker in a test project.\n", + "7. Extend the agent contract with artifact signatures, approvals, and a model-promotion state machine.\n", + "\n", + "## References\n", + "\n", + "- NeqSim: https://github.com/equinor/neqsim\n", + "- NeqSim-Colab: https://github.com/EvenSol/NeqSim-Colab\n", + "- OPM Flow: https://opm-project.org\n", + "- ERT: https://ert.readthedocs.io\n", + "- XTGeo: https://xtgeo.readthedocs.io\n", + "- Public test data: https://github.com/equinor/xtgeo-testdata\n", + "- Reek source commit: cad17f24e22c19c6cefe6f647185395cc0a11add" + ] + } + ], + "metadata": { + "colab": { + "name": "rms_to_opm_flow_agent_ert.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1b867bda3b40a11b91562e10dd086324b9e01181 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 19:10:04 +0200 Subject: [PATCH 14/19] Improve ERT and restart visual interpretation --- .../generate_rms_agent_opm_ert_notebook.py | 34 ++++++++++++++----- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/scripts/generate_rms_agent_opm_ert_notebook.py b/scripts/generate_rms_agent_opm_ert_notebook.py index bafacc41..d858af3f 100644 --- a/scripts/generate_rms_agent_opm_ert_notebook.py +++ b/scripts/generate_rms_agent_opm_ert_notebook.py @@ -1211,6 +1211,9 @@ def active_to_kji(active_values): images = [] for column, (cube, label, unit, cmap) in enumerate(definitions): vmin, vmax = np.nanmin(cube), np.nanmax(cube) + if np.isclose(vmin, vmax): + vmin = max(0.0, float(vmin)) + vmax = vmin + 1.0e-6 for row, k_index in enumerate(layers_to_plot): image = axes[row, column].imshow(cube[k_index], origin="lower", cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto") if row == 0: @@ -1386,7 +1389,10 @@ def frame(self): TEMPLATE_RENDER writes one complete Flow deck per realization. The standard ERT FLOW forward model runs the real simulator. Four realizations are deliberately small enough for Colab but use -the same directory and parameter contracts as a larger study. +the same directory and parameter contracts as a larger study. Oil remains on its 10,000 Sm3/day +ORAT control in this short forecast, so the ensemble graphics focus on sampled injection, +pressure response, and the developing produced-water response rather than magnifying tiny +floating-point differences in the controlled oil rate. """)) cells.append(code(r""" @@ -1499,12 +1505,12 @@ def values(key): ensemble_figure, axes = plt.subplots(1, 3, figsize=(15.0, 4.8), constrained_layout=True) for realization, frame in ensemble_history.groupby("realization"): years = frame["time_days"] / 365.25 - axes[0].plot(years, frame["oil_rate_Sm3_day"], alpha=0.8, label=f"R{realization}") + axes[0].plot(years, frame["water_injection_Sm3_day"], alpha=0.8, label=f"R{realization}") axes[1].plot(years, frame["field_pressure_bara"], alpha=0.8) axes[2].plot(years, frame["water_rate_Sm3_day"], alpha=0.8) -axes[0].set(xlabel="Time [year]", ylabel="Oil rate [Sm3/day]", title="ERT oil-rate ensemble") -axes[1].set(xlabel="Time [year]", ylabel="Pressure [bara]", title="ERT pressure ensemble") -axes[2].set(xlabel="Time [year]", ylabel="Water rate [Sm3/day]", title="ERT water ensemble") +axes[0].set(xlabel="Time [year]", ylabel="Water injection [Sm3/day]", title="ERT sampled injection controls") +axes[1].set(xlabel="Time [year]", ylabel="Pressure [bara]", title="ERT pressure response") +axes[2].set(xlabel="Time [year]", ylabel="Produced water [Sm3/day]", title="ERT water-front response") axes[0].legend(ncol=2) path = OUTPUT_DIRECTORY / "ert_flow_ensemble.png" ensemble_figure.savefig(path, dpi=170, bbox_inches="tight") @@ -1513,9 +1519,9 @@ def values(key): final_ensemble = ensemble_history.groupby("realization").tail(1).merge(ensemble_parameters, on="realization") relationship_figure, axes = plt.subplots(1, 2, figsize=(11.5, 4.5), constrained_layout=True) -axes[0].scatter(final_ensemble["PERM_MULT"], final_ensemble["cumulative_oil_MSm3"], +axes[0].scatter(final_ensemble["PERM_MULT"], final_ensemble["water_rate_Sm3_day"], c=final_ensemble["INJ_RATE"], cmap="viridis", s=100) -axes[0].set(xlabel="PERM_MULT", ylabel="Final cumulative oil [million Sm3]", title="Permeability response") +axes[0].set(xlabel="PERM_MULT", ylabel="Final produced water [Sm3/day]", title="Permeability and water-front response") scatter = axes[1].scatter(final_ensemble["PORO_MULT"], final_ensemble["field_pressure_bara"], c=final_ensemble["INJ_RATE"], cmap="plasma", s=100) axes[1].set(xlabel="PORO_MULT", ylabel="Final pressure [bara]", title="Porosity and injection response") @@ -1653,6 +1659,14 @@ def values(key): float(final_ensemble["cumulative_oil_MSm3"].min()), float(final_ensemble["cumulative_oil_MSm3"].max()), ], + "ert_final_pressure_range_bara": [ + float(final_ensemble["field_pressure_bara"].min()), + float(final_ensemble["field_pressure_bara"].max()), + ], + "ert_final_water_rate_range_sm3_day": [ + float(final_ensemble["water_rate_Sm3_day"].min()), + float(final_ensemble["water_rate_Sm3_day"].max()), + ], "selected_process_realization": selected_realization, "compressor_power_MW": float(gas_compressor.getPower() / 1e6), "process_mass_residual_kg_s": float(process_mass_residual_kg_s), @@ -1667,8 +1681,10 @@ def values(key): ("NeqSim bubble pressure", bubble_pressure_bara, "bara"), ("Base final pressure", final_results["base_final_pressure_bara"], "bara"), ("Base cumulative oil", final_results["base_final_cumulative_oil_million_sm3"], "million Sm3"), - ("ERT low final oil", final_results["ert_final_oil_range_million_sm3"][0], "million Sm3"), - ("ERT high final oil", final_results["ert_final_oil_range_million_sm3"][1], "million Sm3"), + ("ERT minimum final pressure", final_results["ert_final_pressure_range_bara"][0], "bara"), + ("ERT maximum final pressure", final_results["ert_final_pressure_range_bara"][1], "bara"), + ("ERT minimum final water rate", final_results["ert_final_water_rate_range_sm3_day"][0], "Sm3/day"), + ("ERT maximum final water rate", final_results["ert_final_water_rate_range_sm3_day"][1], "Sm3/day"), ("Selected compressor power", final_results["compressor_power_MW"], "MW"), ], columns=["result", "value", "unit"]).round(6)) print(json.dumps(final_results, indent=2)) From 4c00aab5bf9dcb18c55483fc85cf358ba3b69ce0 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 19:10:33 +0200 Subject: [PATCH 15/19] Re-execute notebook after visual QA improvements --- .../bootstrap-rms-agent-opm-ert-notebook.yml | 182 ++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml diff --git a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml new file mode 100644 index 00000000..5db2db51 --- /dev/null +++ b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml @@ -0,0 +1,182 @@ +name: Bootstrap executed RMS OPM ERT notebook + +on: + push: + branches: + - codex/rms-agent-opm-ert-notebook + paths: + - scripts/generate_rms_agent_opm_ert_notebook.py + - .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml + +permissions: + contents: write + +jobs: + execute-publish-evidence: + runs-on: ubuntu-latest + timeout-minutes: 120 + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + cache: pip + - name: Install notebook runner + run: | + python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client ipykernel + python -m ipykernel install --user --name python3 --display-name 'Python 3' + - name: Generate notebook + run: python scripts/generate_rms_agent_opm_ert_notebook.py + - name: Execute every cell + run: >- + jupyter nbconvert --to notebook --execute --inplace + --ExecutePreprocessor.timeout=4200 + --ExecutePreprocessor.iopub_timeout=120 + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - name: Write maintenance and catalog evidence + run: | + python - <<'PY' + from datetime import datetime, timezone + import hashlib + import json + from pathlib import Path + import platform + + notebook_path = Path('notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb') + notebook = json.loads(notebook_path.read_text(encoding='utf-8')) + code_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'code'] + markdown_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'markdown'] + missing = [index for index, cell in enumerate(code_cells, 1) if cell.get('execution_count') is None] + errors = [output for cell in code_cells for output in cell.get('outputs', []) if output.get('output_type') == 'error'] + images = sum('image/png' in output.get('data', {}) for cell in code_cells for output in cell.get('outputs', [])) + if missing or errors or images < 11: + raise RuntimeError({'missing': missing, 'errors': errors, 'images': images}) + + result_candidates = list(Path('.').glob('**/rms_to_opm_outputs/run_results.json')) + if len(result_candidates) != 1: + raise RuntimeError(f'Expected one run_results.json, found {result_candidates}') + results = json.loads(result_candidates[0].read_text(encoding='utf-8')) + now = datetime.now(timezone.utc) + raw = notebook_path.read_bytes() + git_blob = hashlib.sha1(f'blob {len(raw)}\0'.encode() + raw).hexdigest() + + shard = { + 'schema_version': 1, + 'shard': 'rms-opm-ert-agent-20260831', + 'updated_at': now.strftime('%Y-%m-%dT%H:%M:%SZ'), + 'notebooks': [{ + 'path': notebook_path.as_posix(), + 'verified_date': now.strftime('%Y-%m-%d'), + 'verified_at_utc': now.strftime('%Y-%m-%dT%H:%M:%SZ'), + 'neqsim_version': '3.18.0 Python bridge with current source-built Java master', + 'neqsim_commit': results['neqsim_commit'], + 'neqsim_jar_sha256': results['neqsim_jar_sha256'], + 'python_version': platform.python_version(), + 'xtgeo_version': '4.25.1', + 'opm_version': '2026.4', + 'ert_version': '23.0.1', + 'execution_status': 'passed', + 'execution_method': 'Clean GitHub Actions Python 3.12 kernel; all cells executed top-to-bottom; NeqSim Java master built from source; OPM Flow base case and four ERT realizations completed.', + 'code_cells': len(code_cells), + 'substantive_code_cells': len(code_cells), + 'markdown_cells': len(markdown_cells), + 'git_blob_sha1': git_blob, + 'notebook_bytes': len(raw), + 'publication': { + 'branch': 'codex/rms-agent-opm-ert-notebook', + 'mode': 'focused draft pull request', + 'files': [ + notebook_path.as_posix(), + 'README.md', + 'notebooks/examples_of_NeqSim_in_Colab.ipynb', + 'notebooks/notebook_maintenance_ledger.json', + 'notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json', + '.github/workflows/rms-opm-ert-notebook-validation.yml', + 'scripts/generate_rms_agent_opm_ert_notebook.py', + ], + }, + 'open_data': { + 'repository': 'equinor/xtgeo-testdata', + 'commit': results['data_commit'], + 'license': 'LGPL-3.0', + 'dataset': 'Public Reek geological and simulation ROFF exports with RMS-origin provenance', + 'integrity': 'Seven immutable files verified by embedded SHA-256 and byte counts.', + }, + 'capabilities_demonstrated': [ + 'XTGeo ROFF ingestion, grid QC, 2x2x4 blocking, facies spreading, GRDECL export', + 'NeqSim master-source SRK characterization and black-oil PVT generation', + 'OPM Flow corner-point simulation with summary and restart-state inspection', + 'ERT four-realization ensemble experiment using the FLOW forward model', + 'NeqSim surface-process handover and governed RMS-agent job contract', + ], + 'engineering_validation': { + 'named_assertions_passed': results['validation_checks_passed'], + 'assertions_failed': 0, + 'retained_png_figures': images, + }, + 'result_summary': results, + 'rendered_visual_validation': { + 'renderer': 'nbconvert HTML plus retained Matplotlib PNG outputs', + 'figures_retained': images, + 'result': 'pending human visual inspection before draft PR', + }, + 'known_upstream_issue': None, + 'issue_handling': 'No upstream defect is claimed; limitations distinguish teaching assumptions from decision-grade RMS studies.', + }], + } + shard_path = Path('notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json') + shard_was_present = shard_path.exists() + shard_path.write_text(json.dumps(shard, indent=2) + '\n', encoding='utf-8') + + root_ledger_path = Path('notebooks/notebook_maintenance_ledger.json') + root_ledger = json.loads(root_ledger_path.read_text(encoding='utf-8')) + if not shard_was_present: + root_ledger['active_notebook_count'] = int(root_ledger['active_notebook_count']) + 1 + root_ledger['updated_at'] = now.strftime('%Y-%m-%dT%H:%M:%SZ') + root_ledger_path.write_text(json.dumps(root_ledger, indent=2) + '\n', encoding='utf-8') + + catalog_path = Path('notebooks/examples_of_NeqSim_in_Colab.ipynb') + catalog = json.loads(catalog_path.read_text(encoding='utf-8')) + target = 'reservoir/rms_to_opm_flow_agent_ert.ipynb' + if target not in json.dumps(catalog): + catalog['cells'].append({ + 'cell_type': 'markdown', + 'metadata': {}, + 'source': [ + '## RMS-origin reservoir automation\n', + '\n', + '- [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and an RMS-agent contract.\n', + ], + }) + catalog_path.write_text(json.dumps(catalog, indent=1) + '\n', encoding='utf-8') + print({'code_cells': len(code_cells), 'markdown_cells': len(markdown_cells), 'images': images, 'results': results}) + PY + - name: Validate repository and notebook contracts + run: | + python scripts/check_notebook.py notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb --require-main-source + - name: Render HTML for visual review + run: >- + jupyter nbconvert --to html + --output rms_to_opm_flow_agent_ert.html + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + - uses: actions/upload-artifact@v4 + with: + name: rms-opm-ert-bootstrap-evidence + path: | + notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + notebooks/reservoir/rms_to_opm_flow_agent_ert.html + if-no-files-found: error + retention-days: 14 + - name: Commit executed evidence + run: | + git config user.name 'github-actions[bot]' + git config user.email '41898282+github-actions[bot]@users.noreply.github.com' + git rm -- .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml + git add -- notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb + git add -- notebooks/examples_of_NeqSim_in_Colab.ipynb + git add -- notebooks/notebook_maintenance_ledger.json + git add -- notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json + git commit -m 'Add executed RMS to OPM Flow and ERT notebook' + git push origin HEAD:codex/rms-agent-opm-ert-notebook From 4fc243115be6b9c28b47080b27e135609acc45bb Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 31 Aug 2026 17:15:12 +0000 Subject: [PATCH 16/19] Add executed RMS to OPM Flow and ERT notebook --- .../bootstrap-rms-agent-opm-ert-notebook.yml | 182 ------ .../rms_opm_ert_agent_20260831.json | 20 +- notebooks/notebook_maintenance_ledger.json | 2 +- .../reservoir/rms_to_opm_flow_agent_ert.ipynb | 588 ++++++++++-------- 4 files changed, 328 insertions(+), 464 deletions(-) delete mode 100644 .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml diff --git a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml b/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml deleted file mode 100644 index 5db2db51..00000000 --- a/.github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml +++ /dev/null @@ -1,182 +0,0 @@ -name: Bootstrap executed RMS OPM ERT notebook - -on: - push: - branches: - - codex/rms-agent-opm-ert-notebook - paths: - - scripts/generate_rms_agent_opm_ert_notebook.py - - .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml - -permissions: - contents: write - -jobs: - execute-publish-evidence: - runs-on: ubuntu-latest - timeout-minutes: 120 - steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - uses: actions/setup-python@v5 - with: - python-version: '3.12' - cache: pip - - name: Install notebook runner - run: | - python -m pip install --quiet nbformat==5.10.4 nbconvert==7.16.6 jupyter-client ipykernel - python -m ipykernel install --user --name python3 --display-name 'Python 3' - - name: Generate notebook - run: python scripts/generate_rms_agent_opm_ert_notebook.py - - name: Execute every cell - run: >- - jupyter nbconvert --to notebook --execute --inplace - --ExecutePreprocessor.timeout=4200 - --ExecutePreprocessor.iopub_timeout=120 - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - - name: Write maintenance and catalog evidence - run: | - python - <<'PY' - from datetime import datetime, timezone - import hashlib - import json - from pathlib import Path - import platform - - notebook_path = Path('notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb') - notebook = json.loads(notebook_path.read_text(encoding='utf-8')) - code_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'code'] - markdown_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'markdown'] - missing = [index for index, cell in enumerate(code_cells, 1) if cell.get('execution_count') is None] - errors = [output for cell in code_cells for output in cell.get('outputs', []) if output.get('output_type') == 'error'] - images = sum('image/png' in output.get('data', {}) for cell in code_cells for output in cell.get('outputs', [])) - if missing or errors or images < 11: - raise RuntimeError({'missing': missing, 'errors': errors, 'images': images}) - - result_candidates = list(Path('.').glob('**/rms_to_opm_outputs/run_results.json')) - if len(result_candidates) != 1: - raise RuntimeError(f'Expected one run_results.json, found {result_candidates}') - results = json.loads(result_candidates[0].read_text(encoding='utf-8')) - now = datetime.now(timezone.utc) - raw = notebook_path.read_bytes() - git_blob = hashlib.sha1(f'blob {len(raw)}\0'.encode() + raw).hexdigest() - - shard = { - 'schema_version': 1, - 'shard': 'rms-opm-ert-agent-20260831', - 'updated_at': now.strftime('%Y-%m-%dT%H:%M:%SZ'), - 'notebooks': [{ - 'path': notebook_path.as_posix(), - 'verified_date': now.strftime('%Y-%m-%d'), - 'verified_at_utc': now.strftime('%Y-%m-%dT%H:%M:%SZ'), - 'neqsim_version': '3.18.0 Python bridge with current source-built Java master', - 'neqsim_commit': results['neqsim_commit'], - 'neqsim_jar_sha256': results['neqsim_jar_sha256'], - 'python_version': platform.python_version(), - 'xtgeo_version': '4.25.1', - 'opm_version': '2026.4', - 'ert_version': '23.0.1', - 'execution_status': 'passed', - 'execution_method': 'Clean GitHub Actions Python 3.12 kernel; all cells executed top-to-bottom; NeqSim Java master built from source; OPM Flow base case and four ERT realizations completed.', - 'code_cells': len(code_cells), - 'substantive_code_cells': len(code_cells), - 'markdown_cells': len(markdown_cells), - 'git_blob_sha1': git_blob, - 'notebook_bytes': len(raw), - 'publication': { - 'branch': 'codex/rms-agent-opm-ert-notebook', - 'mode': 'focused draft pull request', - 'files': [ - notebook_path.as_posix(), - 'README.md', - 'notebooks/examples_of_NeqSim_in_Colab.ipynb', - 'notebooks/notebook_maintenance_ledger.json', - 'notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json', - '.github/workflows/rms-opm-ert-notebook-validation.yml', - 'scripts/generate_rms_agent_opm_ert_notebook.py', - ], - }, - 'open_data': { - 'repository': 'equinor/xtgeo-testdata', - 'commit': results['data_commit'], - 'license': 'LGPL-3.0', - 'dataset': 'Public Reek geological and simulation ROFF exports with RMS-origin provenance', - 'integrity': 'Seven immutable files verified by embedded SHA-256 and byte counts.', - }, - 'capabilities_demonstrated': [ - 'XTGeo ROFF ingestion, grid QC, 2x2x4 blocking, facies spreading, GRDECL export', - 'NeqSim master-source SRK characterization and black-oil PVT generation', - 'OPM Flow corner-point simulation with summary and restart-state inspection', - 'ERT four-realization ensemble experiment using the FLOW forward model', - 'NeqSim surface-process handover and governed RMS-agent job contract', - ], - 'engineering_validation': { - 'named_assertions_passed': results['validation_checks_passed'], - 'assertions_failed': 0, - 'retained_png_figures': images, - }, - 'result_summary': results, - 'rendered_visual_validation': { - 'renderer': 'nbconvert HTML plus retained Matplotlib PNG outputs', - 'figures_retained': images, - 'result': 'pending human visual inspection before draft PR', - }, - 'known_upstream_issue': None, - 'issue_handling': 'No upstream defect is claimed; limitations distinguish teaching assumptions from decision-grade RMS studies.', - }], - } - shard_path = Path('notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json') - shard_was_present = shard_path.exists() - shard_path.write_text(json.dumps(shard, indent=2) + '\n', encoding='utf-8') - - root_ledger_path = Path('notebooks/notebook_maintenance_ledger.json') - root_ledger = json.loads(root_ledger_path.read_text(encoding='utf-8')) - if not shard_was_present: - root_ledger['active_notebook_count'] = int(root_ledger['active_notebook_count']) + 1 - root_ledger['updated_at'] = now.strftime('%Y-%m-%dT%H:%M:%SZ') - root_ledger_path.write_text(json.dumps(root_ledger, indent=2) + '\n', encoding='utf-8') - - catalog_path = Path('notebooks/examples_of_NeqSim_in_Colab.ipynb') - catalog = json.loads(catalog_path.read_text(encoding='utf-8')) - target = 'reservoir/rms_to_opm_flow_agent_ert.ipynb' - if target not in json.dumps(catalog): - catalog['cells'].append({ - 'cell_type': 'markdown', - 'metadata': {}, - 'source': [ - '## RMS-origin reservoir automation\n', - '\n', - '- [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and an RMS-agent contract.\n', - ], - }) - catalog_path.write_text(json.dumps(catalog, indent=1) + '\n', encoding='utf-8') - print({'code_cells': len(code_cells), 'markdown_cells': len(markdown_cells), 'images': images, 'results': results}) - PY - - name: Validate repository and notebook contracts - run: | - python scripts/check_notebook.py notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb --require-main-source - - name: Render HTML for visual review - run: >- - jupyter nbconvert --to html - --output rms_to_opm_flow_agent_ert.html - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - - uses: actions/upload-artifact@v4 - with: - name: rms-opm-ert-bootstrap-evidence - path: | - notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - notebooks/reservoir/rms_to_opm_flow_agent_ert.html - if-no-files-found: error - retention-days: 14 - - name: Commit executed evidence - run: | - git config user.name 'github-actions[bot]' - git config user.email '41898282+github-actions[bot]@users.noreply.github.com' - git rm -- .github/workflows/bootstrap-rms-agent-opm-ert-notebook.yml - git add -- notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb - git add -- notebooks/examples_of_NeqSim_in_Colab.ipynb - git add -- notebooks/notebook_maintenance_ledger.json - git add -- notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json - git commit -m 'Add executed RMS to OPM Flow and ERT notebook' - git push origin HEAD:codex/rms-agent-opm-ert-notebook diff --git a/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json b/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json index c5eb9722..9f8646b7 100644 --- a/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json +++ b/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json @@ -1,15 +1,15 @@ { "schema_version": 1, "shard": "rms-opm-ert-agent-20260831", - "updated_at": "2026-08-31T17:05:33Z", + "updated_at": "2026-08-31T17:15:07Z", "notebooks": [ { "path": "notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb", "verified_date": "2026-08-31", - "verified_at_utc": "2026-08-31T17:05:33Z", + "verified_at_utc": "2026-08-31T17:15:07Z", "neqsim_version": "3.18.0 Python bridge with current source-built Java master", "neqsim_commit": "1f7a01b06307d9d56451e43bb8d6023d28d14007", - "neqsim_jar_sha256": "1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739", + "neqsim_jar_sha256": "39e1de9738baaef18726c5d7a3f9cccf3a8af29a8e39761f90d92b870b0a857a", "python_version": "3.12.14", "xtgeo_version": "4.25.1", "opm_version": "2026.4", @@ -19,8 +19,8 @@ "code_cells": 42, "substantive_code_cells": 42, "markdown_cells": 20, - "git_blob_sha1": "160b7ffab7e3bf5d4f2dcfeaede8567d82b7d557", - "notebook_bytes": 2735505, + "git_blob_sha1": "1501daf688f19b19e6f65e777e5a3259c7184098", + "notebook_bytes": 2747005, "publication": { "branch": "codex/rms-agent-opm-ert-notebook", "mode": "focused draft pull request", @@ -56,7 +56,7 @@ "result_summary": { "data_commit": "cad17f24e22c19c6cefe6f647185395cc0a11add", "neqsim_commit": "1f7a01b06307d9d56451e43bb8d6023d28d14007", - "neqsim_jar_sha256": "1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739", + "neqsim_jar_sha256": "39e1de9738baaef18726c5d7a3f9cccf3a8af29a8e39761f90d92b870b0a857a", "grid_dimensions": [ 40, 64, @@ -77,6 +77,14 @@ 7.305, 7.305 ], + "ert_final_pressure_range_bara": [ + 219.46754455566406, + 230.8283233642578 + ], + "ert_final_water_rate_range_sm3_day": [ + 0.008757241070270538, + 0.010717831552028656 + ], "selected_process_realization": 0, "compressor_power_MW": 2.680346000234267, "process_mass_residual_kg_s": 5.423572702056845e-11, diff --git a/notebooks/notebook_maintenance_ledger.json b/notebooks/notebook_maintenance_ledger.json index 8f32d82c..2c48dd76 100644 --- a/notebooks/notebook_maintenance_ledger.json +++ b/notebooks/notebook_maintenance_ledger.json @@ -1,6 +1,6 @@ { "schema_version": 3, - "updated_at": "2026-08-31T17:05:33Z", + "updated_at": "2026-08-31T17:15:07Z", "active_notebook_count": 295, "shards_glob": "maintenance_ledger/*.json", "retired_notebooks": [ diff --git a/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb b/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb index 160b7ffa..1501daf6 100644 --- a/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb +++ b/notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "ea4385bb", + "id": "6d1e5309", "metadata": {}, "source": [ "# From an RMS-origin reservoir model to OPM Flow, ERT, and NeqSim\n", @@ -25,7 +25,7 @@ }, { "cell_type": "markdown", - "id": "2ebc0344", + "id": "35e51a84", "metadata": {}, "source": [ "## What this notebook proves\n", @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "9fc524d6", + "id": "cc548ceb", "metadata": {}, "source": [ "## End-to-end architecture and trust boundary\n", @@ -70,13 +70,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "6c9022b9", + "id": "dfc60c82", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:01:02.983408Z", - "iopub.status.busy": "2026-08-31T17:01:02.983235Z", - "iopub.status.idle": "2026-08-31T17:02:42.478459Z", - "shell.execute_reply": "2026-08-31T17:02:42.477471Z" + "iopub.execute_input": "2026-08-31T17:11:12.440898Z", + "iopub.status.busy": "2026-08-31T17:11:12.440679Z", + "iopub.status.idle": "2026-08-31T17:12:27.932257Z", + "shell.execute_reply": "2026-08-31T17:12:27.931388Z" } }, "outputs": [ @@ -167,7 +167,7 @@ }, { "cell_type": "markdown", - "id": "3635f1c5", + "id": "df2ea576", "metadata": {}, "source": [ "## Reproducible current-master NeqSim runtime\n", @@ -182,13 +182,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "ef84ab5f", + "id": "77072d13", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:02:42.480377Z", - "iopub.status.busy": "2026-08-31T17:02:42.480164Z", - "iopub.status.idle": "2026-08-31T17:04:11.331524Z", - "shell.execute_reply": "2026-08-31T17:04:11.330525Z" + "iopub.execute_input": "2026-08-31T17:12:27.934290Z", + "iopub.status.busy": "2026-08-31T17:12:27.934104Z", + "iopub.status.idle": "2026-08-31T17:13:46.541072Z", + "shell.execute_reply": "2026-08-31T17:13:46.540029Z" } }, "outputs": [ @@ -198,7 +198,7 @@ "text": [ "NeqSim source ref: master\n", "NeqSim resolved commit: 1f7a01b06307d9d56451e43bb8d6023d28d14007\n", - "NeqSim JAR SHA-256: 1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739\n", + "NeqSim JAR SHA-256: 39e1de9738baaef18726c5d7a3f9cccf3a8af29a8e39761f90d92b870b0a857a\n", "Loaded class source: file:/home/runner/work/_temp/neqsim-java-master/target/neqsim-3.18.0.jar\n" ] } @@ -308,13 +308,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "d4967baa", + "id": "a2ad05c4", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:11.333487Z", - "iopub.status.busy": "2026-08-31T17:04:11.333262Z", - "iopub.status.idle": "2026-08-31T17:04:15.279363Z", - "shell.execute_reply": "2026-08-31T17:04:15.278580Z" + "iopub.execute_input": "2026-08-31T17:13:46.543379Z", + "iopub.status.busy": "2026-08-31T17:13:46.543167Z", + "iopub.status.idle": "2026-08-31T17:13:49.505938Z", + "shell.execute_reply": "2026-08-31T17:13:49.505298Z" } }, "outputs": [ @@ -472,13 +472,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "64dd1ab4", + "id": "023cb4f3", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:15.281542Z", - "iopub.status.busy": "2026-08-31T17:04:15.281359Z", - "iopub.status.idle": "2026-08-31T17:04:15.622209Z", - "shell.execute_reply": "2026-08-31T17:04:15.621296Z" + "iopub.execute_input": "2026-08-31T17:13:49.508225Z", + "iopub.status.busy": "2026-08-31T17:13:49.508028Z", + "iopub.status.idle": "2026-08-31T17:13:49.755912Z", + "shell.execute_reply": "2026-08-31T17:13:49.755107Z" } }, "outputs": [ @@ -535,7 +535,7 @@ }, { "cell_type": "markdown", - "id": "6e1c2997", + "id": "1f048550", "metadata": {}, "source": [ "## 1. Immutable public RMS-origin inputs\n", @@ -553,13 +553,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "ef5c87c1", + "id": "ce94c80c", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:15.624231Z", - "iopub.status.busy": "2026-08-31T17:04:15.623918Z", - "iopub.status.idle": "2026-08-31T17:04:16.826876Z", - "shell.execute_reply": "2026-08-31T17:04:16.826071Z" + "iopub.execute_input": "2026-08-31T17:13:49.757800Z", + "iopub.status.busy": "2026-08-31T17:13:49.757596Z", + "iopub.status.idle": "2026-08-31T17:13:50.612081Z", + "shell.execute_reply": "2026-08-31T17:13:50.611311Z" } }, "outputs": [ @@ -717,7 +717,7 @@ }, { "cell_type": "markdown", - "id": "f76df784", + "id": "056488e8", "metadata": {}, "source": [ "## 2. Read the geological and simulation grids with XTGeo\n", @@ -731,13 +731,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "cb8ae552", + "id": "7d550f17", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:16.828684Z", - "iopub.status.busy": "2026-08-31T17:04:16.828501Z", - "iopub.status.idle": "2026-08-31T17:04:16.985609Z", - "shell.execute_reply": "2026-08-31T17:04:16.984781Z" + "iopub.execute_input": "2026-08-31T17:13:50.613903Z", + "iopub.status.busy": "2026-08-31T17:13:50.613724Z", + "iopub.status.idle": "2026-08-31T17:13:50.968927Z", + "shell.execute_reply": "2026-08-31T17:13:50.967977Z" } }, "outputs": [ @@ -853,13 +853,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "768725ab", + "id": "ad4b4044", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:16.987339Z", - "iopub.status.busy": "2026-08-31T17:04:16.987156Z", - "iopub.status.idle": "2026-08-31T17:04:17.043102Z", - "shell.execute_reply": "2026-08-31T17:04:17.042298Z" + "iopub.execute_input": "2026-08-31T17:13:50.970739Z", + "iopub.status.busy": "2026-08-31T17:13:50.970535Z", + "iopub.status.idle": "2026-08-31T17:13:51.022964Z", + "shell.execute_reply": "2026-08-31T17:13:51.022166Z" } }, "outputs": [ @@ -1108,13 +1108,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "619b7aab", + "id": "5561a0cf", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:17.044802Z", - "iopub.status.busy": "2026-08-31T17:04:17.044633Z", - "iopub.status.idle": "2026-08-31T17:04:18.882098Z", - "shell.execute_reply": "2026-08-31T17:04:18.881232Z" + "iopub.execute_input": "2026-08-31T17:13:51.024980Z", + "iopub.status.busy": "2026-08-31T17:13:51.024814Z", + "iopub.status.idle": "2026-08-31T17:13:52.761388Z", + "shell.execute_reply": "2026-08-31T17:13:52.760624Z" } }, "outputs": [ @@ -1159,13 +1159,13 @@ { "cell_type": "code", "execution_count": 9, - "id": "9f2d29e2", + "id": "bab9f21a", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:18.883948Z", - "iopub.status.busy": "2026-08-31T17:04:18.883773Z", - "iopub.status.idle": "2026-08-31T17:04:21.225611Z", - "shell.execute_reply": "2026-08-31T17:04:21.224848Z" + "iopub.execute_input": "2026-08-31T17:13:52.763486Z", + "iopub.status.busy": "2026-08-31T17:13:52.763301Z", + "iopub.status.idle": "2026-08-31T17:13:54.972238Z", + "shell.execute_reply": "2026-08-31T17:13:54.971327Z" } }, "outputs": [ @@ -1205,7 +1205,7 @@ }, { "cell_type": "markdown", - "id": "7589961c", + "id": "1be86e6c", "metadata": {}, "source": [ "## 3. Blocking and spreading properties\n", @@ -1236,13 +1236,13 @@ { "cell_type": "code", "execution_count": 10, - "id": "ada89394", + "id": "7c29d279", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:21.227626Z", - "iopub.status.busy": "2026-08-31T17:04:21.227434Z", - "iopub.status.idle": "2026-08-31T17:04:21.307410Z", - "shell.execute_reply": "2026-08-31T17:04:21.306600Z" + "iopub.execute_input": "2026-08-31T17:13:54.974056Z", + "iopub.status.busy": "2026-08-31T17:13:54.973878Z", + "iopub.status.idle": "2026-08-31T17:13:55.053328Z", + "shell.execute_reply": "2026-08-31T17:13:55.052550Z" } }, "outputs": [ @@ -1396,13 +1396,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "8ae29c6c", + "id": "21bf4655", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:21.309459Z", - "iopub.status.busy": "2026-08-31T17:04:21.309274Z", - "iopub.status.idle": "2026-08-31T17:04:23.122360Z", - "shell.execute_reply": "2026-08-31T17:04:23.121549Z" + "iopub.execute_input": "2026-08-31T17:13:55.055145Z", + "iopub.status.busy": "2026-08-31T17:13:55.054948Z", + "iopub.status.idle": "2026-08-31T17:13:56.772501Z", + "shell.execute_reply": "2026-08-31T17:13:56.771690Z" } }, "outputs": [ @@ -1447,13 +1447,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "399b1e7d", + "id": "1767a6a4", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:23.125226Z", - "iopub.status.busy": "2026-08-31T17:04:23.124998Z", - "iopub.status.idle": "2026-08-31T17:04:25.093781Z", - "shell.execute_reply": "2026-08-31T17:04:25.092866Z" + "iopub.execute_input": "2026-08-31T17:13:56.775740Z", + "iopub.status.busy": "2026-08-31T17:13:56.775557Z", + "iopub.status.idle": "2026-08-31T17:13:58.585851Z", + "shell.execute_reply": "2026-08-31T17:13:58.585048Z" } }, "outputs": [ @@ -1497,7 +1497,7 @@ }, { "cell_type": "markdown", - "id": "5085a236", + "id": "d77b4902", "metadata": {}, "source": [ "## 4. Screen an injector–producer pair\n", @@ -1512,13 +1512,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "6bdb7c62", + "id": "0a13c5cd", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:25.095650Z", - "iopub.status.busy": "2026-08-31T17:04:25.095473Z", - "iopub.status.idle": "2026-08-31T17:04:25.119018Z", - "shell.execute_reply": "2026-08-31T17:04:25.118290Z" + "iopub.execute_input": "2026-08-31T17:13:58.588008Z", + "iopub.status.busy": "2026-08-31T17:13:58.587826Z", + "iopub.status.idle": "2026-08-31T17:13:58.612224Z", + "shell.execute_reply": "2026-08-31T17:13:58.611452Z" } }, "outputs": [ @@ -1638,13 +1638,13 @@ { "cell_type": "code", "execution_count": 14, - "id": "a08c486b", + "id": "4e44cef4", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:25.120748Z", - "iopub.status.busy": "2026-08-31T17:04:25.120566Z", - "iopub.status.idle": "2026-08-31T17:04:25.777293Z", - "shell.execute_reply": "2026-08-31T17:04:25.776449Z" + "iopub.execute_input": "2026-08-31T17:13:58.613920Z", + "iopub.status.busy": "2026-08-31T17:13:58.613742Z", + "iopub.status.idle": "2026-08-31T17:13:59.256253Z", + "shell.execute_reply": "2026-08-31T17:13:59.255402Z" } }, "outputs": [ @@ -1678,7 +1678,7 @@ }, { "cell_type": "markdown", - "id": "6238a7bb", + "id": "f5db2393", "metadata": {}, "source": [ "## 5. Generate black-oil PVT with NeqSim\n", @@ -1692,13 +1692,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "63acf2a8", + "id": "17d08358", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:25.779133Z", - "iopub.status.busy": "2026-08-31T17:04:25.778917Z", - "iopub.status.idle": "2026-08-31T17:04:26.932452Z", - "shell.execute_reply": "2026-08-31T17:04:26.928873Z" + "iopub.execute_input": "2026-08-31T17:13:59.258006Z", + "iopub.status.busy": "2026-08-31T17:13:59.257839Z", + "iopub.status.idle": "2026-08-31T17:14:00.361886Z", + "shell.execute_reply": "2026-08-31T17:14:00.360936Z" } }, "outputs": [], @@ -1777,13 +1777,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "8c58c98f", + "id": "5421260b", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:26.938179Z", - "iopub.status.busy": "2026-08-31T17:04:26.937887Z", - "iopub.status.idle": "2026-08-31T17:04:27.837523Z", - "shell.execute_reply": "2026-08-31T17:04:27.836666Z" + "iopub.execute_input": "2026-08-31T17:14:00.364384Z", + "iopub.status.busy": "2026-08-31T17:14:00.364166Z", + "iopub.status.idle": "2026-08-31T17:14:01.224379Z", + "shell.execute_reply": "2026-08-31T17:14:01.223423Z" } }, "outputs": [ @@ -2110,13 +2110,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "13fd2899", + "id": "a3281a97", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:27.839802Z", - "iopub.status.busy": "2026-08-31T17:04:27.839394Z", - "iopub.status.idle": "2026-08-31T17:04:28.422447Z", - "shell.execute_reply": "2026-08-31T17:04:28.421253Z" + "iopub.execute_input": "2026-08-31T17:14:01.227492Z", + "iopub.status.busy": "2026-08-31T17:14:01.227261Z", + "iopub.status.idle": "2026-08-31T17:14:01.782297Z", + "shell.execute_reply": "2026-08-31T17:14:01.781526Z" } }, "outputs": [ @@ -2337,13 +2337,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "a2c117d6", + "id": "19dd2840", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:28.424648Z", - "iopub.status.busy": "2026-08-31T17:04:28.424411Z", - "iopub.status.idle": "2026-08-31T17:04:29.520470Z", - "shell.execute_reply": "2026-08-31T17:04:29.519617Z" + "iopub.execute_input": "2026-08-31T17:14:01.784625Z", + "iopub.status.busy": "2026-08-31T17:14:01.784345Z", + "iopub.status.idle": "2026-08-31T17:14:02.754773Z", + "shell.execute_reply": "2026-08-31T17:14:02.753946Z" } }, "outputs": [ @@ -2421,13 +2421,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "ea8347b8", + "id": "2f969c8e", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:29.522669Z", - "iopub.status.busy": "2026-08-31T17:04:29.522490Z", - "iopub.status.idle": "2026-08-31T17:04:29.552725Z", - "shell.execute_reply": "2026-08-31T17:04:29.550028Z" + "iopub.execute_input": "2026-08-31T17:14:02.756535Z", + "iopub.status.busy": "2026-08-31T17:14:02.756325Z", + "iopub.status.idle": "2026-08-31T17:14:02.787010Z", + "shell.execute_reply": "2026-08-31T17:14:02.785633Z" } }, "outputs": [], @@ -2529,13 +2529,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "9febcdc5", + "id": "ecef8770", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:29.555949Z", - "iopub.status.busy": "2026-08-31T17:04:29.555417Z", - "iopub.status.idle": "2026-08-31T17:04:29.701463Z", - "shell.execute_reply": "2026-08-31T17:04:29.700531Z" + "iopub.execute_input": "2026-08-31T17:14:02.789485Z", + "iopub.status.busy": "2026-08-31T17:14:02.788987Z", + "iopub.status.idle": "2026-08-31T17:14:02.930922Z", + "shell.execute_reply": "2026-08-31T17:14:02.930057Z" } }, "outputs": [ @@ -2840,13 +2840,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "9c74e905", + "id": "c10735cf", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:29.703975Z", - "iopub.status.busy": "2026-08-31T17:04:29.703740Z", - "iopub.status.idle": "2026-08-31T17:04:29.722451Z", - "shell.execute_reply": "2026-08-31T17:04:29.721551Z" + "iopub.execute_input": "2026-08-31T17:14:02.933351Z", + "iopub.status.busy": "2026-08-31T17:14:02.933126Z", + "iopub.status.idle": "2026-08-31T17:14:02.950536Z", + "shell.execute_reply": "2026-08-31T17:14:02.949640Z" } }, "outputs": [ @@ -3085,7 +3085,7 @@ }, { "cell_type": "markdown", - "id": "ba2487e3", + "id": "c541d57a", "metadata": {}, "source": [ "## 6. Export Flow grid and static properties\n", @@ -3108,13 +3108,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "b63134c2", + "id": "1397a9d0", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:29.724895Z", - "iopub.status.busy": "2026-08-31T17:04:29.724663Z", - "iopub.status.idle": "2026-08-31T17:04:30.417218Z", - "shell.execute_reply": "2026-08-31T17:04:30.416254Z" + "iopub.execute_input": "2026-08-31T17:14:02.952973Z", + "iopub.status.busy": "2026-08-31T17:14:02.952741Z", + "iopub.status.idle": "2026-08-31T17:14:03.578285Z", + "shell.execute_reply": "2026-08-31T17:14:03.577528Z" } }, "outputs": [ @@ -3319,13 +3319,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "a6aee21f", + "id": "55215484", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:30.418988Z", - "iopub.status.busy": "2026-08-31T17:04:30.418819Z", - "iopub.status.idle": "2026-08-31T17:04:30.427208Z", - "shell.execute_reply": "2026-08-31T17:04:30.426516Z" + "iopub.execute_input": "2026-08-31T17:14:03.580207Z", + "iopub.status.busy": "2026-08-31T17:14:03.580030Z", + "iopub.status.idle": "2026-08-31T17:14:03.588740Z", + "shell.execute_reply": "2026-08-31T17:14:03.587990Z" } }, "outputs": [ @@ -3636,13 +3636,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "ded41d28", + "id": "3d66badf", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:30.428895Z", - "iopub.status.busy": "2026-08-31T17:04:30.428736Z", - "iopub.status.idle": "2026-08-31T17:04:30.493986Z", - "shell.execute_reply": "2026-08-31T17:04:30.493266Z" + "iopub.execute_input": "2026-08-31T17:14:03.590315Z", + "iopub.status.busy": "2026-08-31T17:14:03.590150Z", + "iopub.status.idle": "2026-08-31T17:14:03.655307Z", + "shell.execute_reply": "2026-08-31T17:14:03.654542Z" } }, "outputs": [ @@ -3897,7 +3897,7 @@ }, { "cell_type": "markdown", - "id": "08514558", + "id": "4448e03f", "metadata": {}, "source": [ "## 7. Run OPM Flow and retain diagnostics\n", @@ -3910,13 +3910,13 @@ { "cell_type": "code", "execution_count": 25, - "id": "9976bace", + "id": "d8172007", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:30.495890Z", - "iopub.status.busy": "2026-08-31T17:04:30.495722Z", - "iopub.status.idle": "2026-08-31T17:04:42.585943Z", - "shell.execute_reply": "2026-08-31T17:04:42.585194Z" + "iopub.execute_input": "2026-08-31T17:14:03.657223Z", + "iopub.status.busy": "2026-08-31T17:14:03.656949Z", + "iopub.status.idle": "2026-08-31T17:14:16.439774Z", + "shell.execute_reply": "2026-08-31T17:14:16.438915Z" } }, "outputs": [ @@ -3956,7 +3956,7 @@ " \n", " 1\n", " wall time\n", - " 12.080665\n", + " 12.772644\n", " s\n", " \n", " \n", @@ -3984,7 +3984,7 @@ "text/plain": [ " diagnostic value unit\n", "0 return code 0 -\n", - "1 wall time 12.080665 s\n", + "1 wall time 12.772644 s\n", "2 SMSPEC exists True boolean\n", "3 UNRST exists True boolean\n", "4 PRT exists True boolean" @@ -3999,17 +3999,17 @@ "text": [ "\n", "Report step 22/24 at day 669.625/730.5, date = 01-Nov-2026\n", - "Restart file written for report step 22/24, date = 01-Nov-2026 15:00:00\n", "\n", "Starting time step 0, stepsize 30.4375 days, at day 669.625/700.062, date = 01-Nov-2026\n", + "Restart file written for report step 22/24, date = 01-Nov-2026 15:00:00\n", " Newton its= 2, linearizations= 3 (0.1sec), linear its= 3 (0.1sec)\n", "\n", "Report step 23/24 at day 700.062/730.5, date = 02-Dec-2026\n", - "Restart file written for report step 23/24, date = 02-Dec-2026 01:30:00\n", "\n", "Starting time step 0, stepsize 30.4375 days, at day 700.062/730.5, date = 02-Dec-2026\n", + "Restart file written for report step 23/24, date = 02-Dec-2026 01:30:00\n", " Oscillating behavior detected: Relaxation set to 0.900000\n", - " Newton its= 5, linearizations= 6 (0.2sec), linear its= 6 (0.3sec)\n", + " Newton its= 5, linearizations= 6 (0.2sec), linear its= 6 (0.4sec)\n", "Restart file written for report step 24/24, date = 01-Jan-2027 12:00:00\n", "\n", "\n", @@ -4017,16 +4017,16 @@ "\n", "Number of MPI processes: 1\n", "Threads per MPI process: 2\n", - "Setup time: 0.51 s\n", - " Deck input: 0.19 s\n", + "Setup time: 0.55 s\n", + " Deck input: 0.20 s\n", "Number of timesteps: 27\n", - "Simulation time: 11.12 s\n", - " Assembly time: 3.72 s (Wasted: 0.0 s; 0.0%)\n", - " Well assembly: 0.03 s (Wasted: 0.0 s; 0.0%)\n", - " Linear solve time: 5.20 s (Wasted: 0.0 s; 0.0%)\n", - " Linear setup: 2.65 s (Wasted: 0.0 s; 0.0%)\n", - " Props/update time: 1.42 s (Wasted: 0.0 s; 0.0%)\n", - " Pre/post step: 0.61 s (Wasted: 0.0 s; 0.0%)\n", + "Simulation time: 11.75 s\n", + " Assembly time: 3.92 s (Wasted: 0.0 s; 0.0%)\n", + " Well assembly: 0.04 s (Wasted: 0.0 s; 0.0%)\n", + " Linear solve time: 5.53 s (Wasted: 0.0 s; 0.0%)\n", + " Linear setup: 2.83 s (Wasted: 0.0 s; 0.0%)\n", + " Props/update time: 1.53 s (Wasted: 0.0 s; 0.0%)\n", + " Pre/post step: 0.62 s (Wasted: 0.0 s; 0.0%)\n", " Output write time: 0.03 s\n", "Overall Linearizations: 120 (Wasted: 0; 0.0%)\n", "Overall Newton Iterations: 93 (Wasted: 0; 0.0%)\n", @@ -4069,13 +4069,13 @@ { "cell_type": "code", "execution_count": 26, - "id": "675d2ce3", + "id": "f4eeae7b", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:42.588076Z", - "iopub.status.busy": "2026-08-31T17:04:42.587887Z", - "iopub.status.idle": "2026-08-31T17:04:42.610403Z", - "shell.execute_reply": "2026-08-31T17:04:42.609430Z" + "iopub.execute_input": "2026-08-31T17:14:16.441532Z", + "iopub.status.busy": "2026-08-31T17:14:16.441354Z", + "iopub.status.idle": "2026-08-31T17:14:16.464215Z", + "shell.execute_reply": "2026-08-31T17:14:16.463560Z" } }, "outputs": [ @@ -4613,13 +4613,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "719a74d6", + "id": "27a0117e", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:42.612106Z", - "iopub.status.busy": "2026-08-31T17:04:42.611918Z", - "iopub.status.idle": "2026-08-31T17:04:43.741289Z", - "shell.execute_reply": "2026-08-31T17:04:43.740226Z" + "iopub.execute_input": "2026-08-31T17:14:16.465935Z", + "iopub.status.busy": "2026-08-31T17:14:16.465673Z", + "iopub.status.idle": "2026-08-31T17:14:17.478094Z", + "shell.execute_reply": "2026-08-31T17:14:17.477329Z" } }, "outputs": [ @@ -4659,7 +4659,7 @@ }, { "cell_type": "markdown", - "id": "6046ba52", + "id": "9562b0f9", "metadata": {}, "source": [ "## 8. Read real restart states and visualize displacement\n", @@ -4672,13 +4672,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "b100e1bd", + "id": "235eb540", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:43.743199Z", - "iopub.status.busy": "2026-08-31T17:04:43.742985Z", - "iopub.status.idle": "2026-08-31T17:04:43.758743Z", - "shell.execute_reply": "2026-08-31T17:04:43.757946Z" + "iopub.execute_input": "2026-08-31T17:14:17.480212Z", + "iopub.status.busy": "2026-08-31T17:14:17.480037Z", + "iopub.status.idle": "2026-08-31T17:14:17.496303Z", + "shell.execute_reply": "2026-08-31T17:14:17.495595Z" } }, "outputs": [ @@ -4806,19 +4806,19 @@ { "cell_type": "code", "execution_count": 29, - "id": "757ebbf4", + "id": "6ad9ca4e", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:43.760331Z", - "iopub.status.busy": "2026-08-31T17:04:43.760168Z", - "iopub.status.idle": "2026-08-31T17:04:46.868120Z", - "shell.execute_reply": "2026-08-31T17:04:46.867018Z" + "iopub.execute_input": "2026-08-31T17:14:17.497905Z", + "iopub.status.busy": "2026-08-31T17:14:17.497744Z", + "iopub.status.idle": "2026-08-31T17:14:20.248757Z", + "shell.execute_reply": "2026-08-31T17:14:20.247904Z" } }, "outputs": [ { "data": { - "image/png": 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r2rVrh+XLl+PmzZuoXLkybt68iZs3b6JZs2bw9vbGqlWrYDAYoFKpcOTIEej1erRv397hfbOlQYMGKF26tNkyY2iFrJ3TpUuXxpAhQxATE4MdO3bg9u3bSE5ORnp6Oi5evJhtfUcEBQVlizFn7Fzu2bNntvXbtGkDd3d3HDhwwGx//vrrL3z88cd499130aZNG5QsWdJmGdY4uq/W6p9Tzqy/JWe0vRJrbZzTa8+ofv368PT0NFu/bNmyePDgQbaQGMZYxMYOuNxo0KBBtpAFxvKzHpectr89LN/jZFnG/fv3sX//fkRGRqJevXpmjyDntA65qXNYWBjeeecd1K9fH/PmzbP5Xmvpv//+AwC0aNEiW9rAgQPN/nb0XBFd2/a+hzraNo6+r1g7z/z9/eHr64tr164hNTUVbm5uDreJrW3a0xaOyu01m9N9NXb2NmnSJNv6bdq0wYYNG8yW5cc1S0RERR87TomInhBlypQBAFP8QSXx8fEAYHXSoHnz5mVbVqpUKfTo0QNvvPEGateubbPcnj17YubMmVi9erWp43TNmjUAgEGDBtlVN1us1SurF154weaHeeOENrbiuZYtWxaxsbGIi4tDuXLlTB2nR44cQeXKlXHgwAFIkoQWLVrAx8cH8+fPx9mzZxEYGIiDBw9CkqR86TjNOjmVkXHWbVmWTctkWca3336LJUuWQKfToXTp0ihVqhRcXV0RExOTbX1HVKhQIduye/fuAQD27duHS5cuZUt3c3NDZGQk9Ho91Go1goODcfv2bfz22294++23oVarUbduXXTs2BGDBg2yug1Lju6rPWUrcWb9LTmj7ZVYKyOn156R8T0tK+MXL5ZpxpFjuT3HAfuvuZy2vz1svcdVq1YN77//PkaOHGkW+zKndXC0zlevXsVrr72G5ORkDBkyxO4v6IDHx9/a8bS1bk7PFdG5m9/H09q2169fn+1pjs6dO6NevXqKdS5TpgwePnyIuLg4lC9f3uE2scbetnBUbq/ZnO7rw4cPAVj/P8bavubHNUtEREUfO06JiJ4QxllrT5w4gcGDByuubxypYfnIPQAcPXrUbNSTi4uL3RM6ubm5oW/fvli2bBlOnTqFgIAArF+/Hs2bN7e6rZz4559/hCNOjaPnrLE2S3FWxg9vxvVatGgBNzc3HD58GAMHDsSBAwdQq1YtlClTBo0aNYKLiwsOHTqEwMBAHD58GAEBAbkezehIvY3++usvLFq0CPXq1cO3336LGjVqmNLmzJmDn376Kdd1ydphY2nbtm1Wj40kSShVqhSSkpLg5eUFSZLwzjvv4KWXXsLevXuxf/9+HDp0CD/++CMWLFiAOXPmoGPHjsJ6OLqvovrby5n1t6Ug216JtTbO6bVnTz57r4uCYG/728Pyvffy5cumL6CGDRtm8xzOaR1yuv6RI0cQEBAAWZbx+eefo0mTJqhSpYpd+5STY+XouSK6tnN6ruS0baxte/369Th48KDZsgoVKph1nNrqmDMYDGb1drRNrMnv6ya312xe7qter7eZlpfXLBERFX3sOCUiekJ0794dM2bMwJYtWzBx4kSrIzCMYmJisG3bNri6uqJbt27Z0j08PHL1SP3gwYOxbNkybNiwAXfv3sXDhw9zPdoUAJ5++mmH62UcwXLnzh34+/tnS4+OjoaLi4vp0UkPDw80bdoUR44cgcFgwKFDh9CnTx9TmjHOaf/+/XHx4kWbM/gWlH/++QcAMHnyZLOOOCBjNvT8Yuws/vnnn3MU38/b2xu9evVCr169AAA7d+7E22+/jU8//VSx885Z+5pVYai/M9reETm99ooKR9tfxPK9t0GDBnj99dcxZ84cfPbZZ/jyyy9zVQdH69y4cWP89ttvOHDgAN544w288847+PPPP+36Qs048s/W8c/KmedKXh7Pr776CikpKWbLLEdcPnjwwGrehw8fQq1Wo1SpUgCK7/VjTU731dhGDx8+zLb+7du3s+XPj2uWiIiKPk4ORUT0hChbtiyCg4ORlJSE8ePHm2KeWkpJScHEiRORlJSEUaNGmT545CWtVotGjRph+/bt2Lp1K7y9vdG9e/c8305ONG/eHACwd+/ebGknTpxAfHw8GjVqZNZp0b59e9y+fRv//vsvYmNjzSbyad68OY4dO4b9+/cDMJ+wxRl0Oh0AZDuet2/fNnXU5Qdj3MKsk2UZ6fV6/O9//8OtW7cAZIykWr58Of74449s63bq1An16tXDrVu3kJaWZpZm+QhpfuyrPY+pOrv+luUURNvnBUeuvYKQ20eTc9L+ufHaa6+hQYMGWLduHTZt2pSrOjha51q1asHDwwNdunTBSy+9hLNnz2LGjBl21d8Y23LXrl3Z0kJDQ7FkyRLTI9fOPFfy8nj6+fmhatWqZj+WT0SEhYUhNTXVbNmVK1cQExODWrVqmTqlndkmefH4fk7kdF/r1KkDADh+/Hi29Xfu3JltWUFds0REVLSw45SI6AkyefJkNGzYEIcOHULfvn2xYsUKnD9/Hnfu3MGFCxewatUq9OvXD/v27UPr1q0xfvz4fKvL4MGDTR1Bffr0yZPHpHOjb9++KFeuHFauXInt27ebll+8eBGTJ08GALzyyitmeYwTVCxYsABqtdr0oQ7I6AxISkrCihUr4OPjgwYNGhTAXtgWEBAAAFixYoXpEcXjx49j9OjRaNy4MQDrI3Byq1evXqhYsSIWLVqEDRs2mDoF79+/jylTpmDy5MnYsmULAEClUmHbtm34/PPPsWbNGlOngcFgwM6dO3H27FnUrl3b1GFQsmRJSJKEGzduICYmBsnJyXm+r7a2YY2z6m+rnPxs+7zkyLWXn3JyzEVy0v65oVar8dVXX8HDwwOffPKJ2ajknNYhL+o8YcIENGzYEH/88Qc2b96sWP8+ffqgXLlyWLVqFVatWoXExETodDqEhoZi8uTJWLhwoemxaGeeKwV1PI3S0tIwadIkU1zyhw8f4v/+7/8AAP379zet54w2yatrJKdyuq+9evWCSqXCr7/+in379kGv1yM1NRW//fYbwsLCspVf0MeYiIiKBj6qT0T0BHF3d8eyZcvw448/4o8//sD06dOzrePr64vx48djzJgxwnihufXss89i5syZiI+PR3BwcL5tx15eXl745ZdfMG7cOLzxxhvw8fGBLMuIi4uDh4cH/u///i/bqFF/f39UrFgRp0+fRv369eHt7W1Ka9y4MTQaDU6cOIHevXs7fSKJV155Bf/88w/++OMPbNiwAZIkITk5GW+//TZ69uyJXr16Yd68edi9ezdWrVqVZ9v19PTEggULMG7cOHzwwQeYMmUKSpYsibi4OEiShFGjRuGFF14wrf/ll1/i1Vdfxccff4xPPvkEXl5epo6UGjVq4Ouvvzat6+rqijZt2mDfvn1o1aoVACA8PDxP99XWNmyN3nJG/W2Vk59tn5ccufbyk632zKmctn9uVK9eHRMmTMDnn3+OiRMnYvny5dBoNDmuQ17U2cXFBd9//z369euHjz/+GAEBAdlCTmRVsmRJzJ8/H+PGjcO0adNM9yWDwYCnn34aP/30k2miIGeeKwV5PIGMTrzbt2+jefPmKFOmDB48eABZltGlSxdTXFvAOW2S0/fFvJLTfdVqtXjvvfcwa9YsvPzyy3Bzc4Ner0eZMmXwySef4N133zXFjAUK/hgTEVHRIMkF/YwFEREVCmlpaTh8+DCioqIQFxeH0qVLo3LlymjWrJnNDz/z58+HTqfDm2++maNO1fnz56N06dIYMmSI2fLQ0FDcvXsXL730UrblERERGD16tF2zM+e0XpGRkVi3bh0CAwPRqVMns7S0tDQcPHgQly9fhl6vx1NPPYU2bdrYjAm7ZcsWXL582WpZy5cvR0xMDNq3b2/3iNNVq1bh/v37eP31103H4ebNm1i/fj2CgoJMHwrv3LmDNWvWICAgAF27djUrw5hWu3Ztsxi1KSkp2LFjByIjI+Ht7Y02bdqgcuXKADIeczxy5Ai8vb0xdOhQYRtZEtXFSK/X48iRI7hw4QLS0tJQtmxZtGrVCk899VS2dWVZxrFjx3D58mXExcXBx8cHNWrUQNOmTbNN+pGcnIxNmzYhPj4elSpVwjPPPANJknK0r0r1t7UNWwq6/kp1zK+2t8XaOWzPOWLvtScq688//0R0dLTZtoGMye527NiBdu3aoWHDhor7YK097969m+NrDshZ+9tiz3ucLMtYvHgxEhIS0LNnT9SsWdPhOti7vuh94r///sOhQ4dQs2ZN9OzZU3Ef09LSsG/fPkRERMDFxQXVqlVD27Ztrd6P8uJcceQ9NCdtY885b83hw4fx/PPPY+jQoZg2bRoOHz6Ms2fPwmAwIDAw0CwkjCNtklf3k5y8L+b1NZvT+/SVK1dw4MABJCYmolKlSujQoQP0ej2WLl2KOnXq4JlnnjFbPy/OfyIiKj7YcUpERERERFQIGDtOhwwZYvWpECIiIipYjHFKREREREREREREZIEdp0REREREREREREQWODkUERERERFRIVCxYkW89dZbCAwMdHZViIiICIxxSkRERERERERERJQNH9UnIiIiIiIiIiIissCOUyIiIiIiIiIiIiIL7DglIiIiIiIiIiIissCOUyIiIiIiIiIiIiIL7DglIiIiIiIiIiIissCOUyIiIiIiIiIiIiIL7DglIiIiIiIiIiIisqBxdgWo8Pjoo49Qr149DB8+3NlVcdi///6Lbdu2oW/fvmjXrp1DZXz00UdITU01/S1JEkqVKoXq1avjueeeQ8mSJfOqukREZKeffvoJKSkpmDBhgrOr4rBTp05h1apVaNiwIYYMGZKjvAaDwXSfHjlypFmaLMv4+OOPUbVqVYwZMyZb3k8//RRPPfWU1TTKvZUrVyIyMhIffPCBs6tCRE+YW7duYdasWRg4cCBatWrl7Oo4RKfTYcWKFQgPD8c777yDypUr56q8y5cvY+fOnYiMjITBYECZMmXQvHlztGjRAmq1Oo9q/WTZu3cvNmzYgJkzZ8LV1dXZ1SEqcBxxSiahoaE4fvy4s6vhkIsXL+KFF17Ae++9h40bNyIiIsLhskJDQ3HixAloNBpoNBqo1Wpcu3YN33zzDbp06VJk24iIqCjbt28fdu7c6exqOCQ6OhqTJk3C8OHDERISgrCwsByXoVKpcPPmTSxcuDBb2vnz5/HXX39h4cKF0Ov1Zmk3btzAypUrzb4QFElJScH7778Pg8GQ4zoWNGfU1do2Hzx4gNu3bxdYHYiIjOLi4hASEoJr1645uyoO2bVrF3r37o1Zs2YhJCQEsbGxDpeVmpqKyZMno3fv3liyZAnu3LmD+/fvY9u2bXjppZcQHByMmzdv5l3lC8hXX32FGzduOHWb8fHxuHHjRpH434AoP7DjlIqFWbNmoWzZsli7dm2elNewYUN8+eWXpp9FixZh27ZtcHFxwbRp0/JkG0RE9GRYsWIFoqKi8Ndff6F8+fIOl9O+fXvcuXMn25eD+/btQ7Vq1ZCQkIDTp0+bpe3fvx8A0KFDB7u2ER4ejo0bNxaJD0fOqKu1bY4bNw7ff/99gdWBiKi4+Pjjj/Hss8/iq6++ynVZU6dOxYYNG/Dxxx9jz549+OWXX/Dzzz9jy5YtWLp0KaKiovDOO+9k+4KxMEtKSsKSJUvw8OFDp27z2WefxerVq+Hu7l5g9SAqTPioPuVIcnIytm7dirNnzyI5ORlPPfUU+vbti8qVK+Pu3bv45ptvMGDAALRu3dos34YNG3Do0CFMnz4dLi4uAID//vsPO3bsQFxcHJ5++mn06dMHVatWBQDcvHkTP/zwA95++23s378fZ86cwbRp06DRWD9lP/30U/j5+eHRo0c26/7DDz8gPT3d4cc8/fz80K1bN6xYsQIJCQmIiYkR1lG0f0DGCKSNGzfixo0bUKvVqFOnDnr16mUWCkBpnatXr+Knn37C2LFjUatWLbP2PnbsGKZPn67Ylkr1JCIqKgwGA3bs2IFjx44hLi4O5cqVQ5cuXRAUFAS9Xo+pU6eiUaNGCA4ONst39OhR/PHHHxg/fjwqVqwIALh06RJCQ0Nx584d+Pr6onv37ggKCgIApKen48MPP8SgQYOQmJiIXbt2YezYsahUqZLVeg0fPhzjx48X1n3NmjU4fPgwvv76a6hU1r/Xbt++Pb777jvs27cP/v7+puX79+9H69at4erqiv3796Nhw4amtAMHDqB06dIIDAwEkPEI499//43bt2/Dzc0N9erVQ58+faDRaBAaGoolS5YAACZNmoQGDRrg+eefBwAkJiZiw4YNOHfuHFQqFRo2bIjevXub7ukrVqxATEwMBgwYgMWLFyMwMBB9+/a1ub8HDx7Evn37EBMTA19fX3Ts2BFNmzY1W8eRui5duhT37t3Ldq+fOHEiunXrZrqP26qrI9u09qj+o0ePsH79ely8eBEqlQq1a9c2C/ezatUqPHjwAK+++irWrFmD8+fPw9fXF88995zZsSUiygvR0dEIDQ3F9evXYTAYUKNGDQwcOBAlS5bEwYMHsXbtWrz33nt4+umnzfLNnj0bLi4uePPNNwEAer0e27Ztw9GjR5GWloaaNWtiwIAB8Pb2BgDs3r0bW7ZswdSpU/Hbb79Bo9Hg9ddft1mvtWvXws/Pz+YTJTqdDpMnT0arVq0wcOBAm+WcPXsWGzZswOjRo7OFswGAFi1aYPny5ShZsqTZ4/qidjG6cOECtm7diujoaJQoUQLNmjVDly5dbN6rc5JPtP1Tp07hp59+gl6vx48//gh/f39MmTIFO3fuxObNm/HFF1+Y7sFAxmddLy8vjB49WngcHNmmtUf109LSTE9qpqWloWrVqujbty+eeuopABmP92/evBlffvklQkNDcejQIXh4eKBjx45FNqwEPbk44pTsptfrMXz4cHz33XeQZRmlSpXC1q1b8eyzz+L06dPw8/PDmTNnsGjRomx5f/75Zzx69Mj05j5r1iyMHDkS169fR7ly5bBv3z707t0bBw8eBJDxgSMkJAS///475s6di6tXrwpHlPj5+SnWf/fu3bl+zNNgMECSJKjVamEdlfbv5s2b6N27N7Zu3YrSpUvDzc0NP//8M4YNG2Z6nNKedR48eICQkBDcu3fPrJ7h4eEIDQ0FIG5LpXoSERUlkydPxoQJExAfH4+yZcsiLCwMgwcPxvr166FWq5GcnIwffvgh22iT5cuXIywszPSBMSQkBM899xwOHjyI8uXL4+rVqwgODsby5csBAGq1GiEhIQgNDcXkyZNx5coVJCUl2ayXPfeoEydOICQkRHivq1OnDvz8/EyjSIGMR8ePHTuGZs2aoUmTJjhw4IApTa/X49ChQ2jbti1UKhX27duHfv364fDhwyhdujRSUlLwySef4NVXX4Usyyhfvjy8vLxM2zJ2BEdHR6Nfv36YO3cuPD094eLigpkzZ2LUqFFIS0sDAJw8eRLbt2/He++9h6NHj+Lu3bs29+PHH3/EmDFj8PDhQ/j5+SEqKgqjRo3CsmXLTOs4WtejR49avdeHhITgwoULwro6us3jx49jx44dpm1FRkaid+/e+O233+Dp6Wm6f/ft2xf3798HAJw+fRrbtm3Da6+9hiNHjsDLywvbtm1D//79ERUVZbPtiIhyKjIyEn369MHatWvh4eEBNzc3/PLLL+jbty8ePXqEmjVrYsuWLfjf//5nlu/+/ftYsGABPDw8AGR0Yr788suYNGkSdDodSpUqheXLl6Nv3764c+cOAOD69etYt24dvv32W2zatAmRkZHCuindHw0GA0JCQnDy5Enhev/88w8ACOfqqFWrlqlTD1BuFwDYsWMHnnvuOVy8eBHly5dHcnIyPvjgA0yZMkVYH3vyKW3fy8sL5cqVAwBUr14dNWvWBJDxxa61/xd2796Nw4cPA7B9HBzdZkREBEJCQqDT6QBkhEUYOXIkPv30UxgMBvj6+iIkJATPPvus6VjdvHkT69atw4wZM7Bq1SqUKFECp06dwosvvohdu3YJ24+o0JGJMjVs2FCeMGGCzfSkpCR5+/bt8pUrV0zLEhIS5KZNm5ry/fbbb3KdOnXkW7dumdY5c+aMrNVq5e3bt5v9/c0335jWMRgM8gsvvCB37dpVNhgM8oULF2StVis/99xzcnx8vN37EBcXJ2u1Wvn333/PlrZr1y55x44dimXYaodbt27JrVq1kocMGSLLsmyzjvbs32+//SYHBATIqamppnXu3r0rjxs3Tr506ZIsy7Jd6/z333+yVquV9+/fb1bXzz//XG7SpEmu60lEVFgMHTpU7tWrl3Cd3bt3yydPnjRbNmDAAFO+AwcOyFqtVt65c6cpPTExUW7QoIE8d+5cWZZlOT4+Xm7UqJH86quvynq93rTezJkz5cDAQPnBgweyLMtyQECA3LJlS/nGjRs52o9OnTrJkyZNyrY8LCxMXr9+veJ770cffSQ3bNjQdG/Ys2ePXLt2bfnBgwfy1q1b5Xr16pne60+cOCFrtVp548aNsizL8qVLl+Rt27aZbWPZsmWyVquVT506JcuyLP/000+yVquVdTqdaZ0PPvhADgoKMtvXs2fPynXq1JGXLl0qy7IsT5kyRQ4KCpIXLFig2AYdOnSQp0+fbrZs5cqVZvcjR+s6btw4q+eJVquV58yZI6yro9ucMGGC3L17d9Pf48ePlxs3bmw6V2RZlm/cuCHXrVtX/uyzz0x10Gq18qpVq0zrREZG2vwfhojImrNnz8parVZeuXKlzXXu3Lkjb9u2TY6NjTUtCwsLM8v35ptvyp07dzZ7/1u6dKlcr149+f79+6a/tVqt2eepuLg4uWXLlvIHH3wgy3LGe7lWq5UnTZqUo88SO3bsMHuvNdLr9fL69euz3dstvf3223KjRo3s3p4s298ugwcPNsu3f/9+ecKECXJKSorNsu3JZ8/2d+/eLWu1WjksLMy0zi+//CJrtdps2+/fv788ZswYWZZtHwdHt/n777/LWq1WTkhIkGVZln/99VdZq9XKR48eNa2TlJQkd+rUyfRZ2ViHjz/+2LSOTqeT27RpI7/33ns2246oMOKIU7Kbh4cHOnfujOrVq5uWlShRAv7+/qZvsfr16weNRoN169aZ1gkNDUW5cuXQvn17ADCNyhg1apRpHUmSMGjQINy4cQOXLl0yLe/Ro0eezWLfoUMHdOrUya51w8LCMHHiRNPPyy+/jJ49e8LV1RWffvqp2bqWdbRn/0qXLg29Xo/58+fjwYMHAIDy5ctjzpw5pm/27FknJxypJxFRUdK+fXvT4/RGQUFBpntUy5YtUbVqVbNRNTt37kRqaioGDBgAADh06BASExMxfPhws8fpBg8ejLS0NOzdu9e0rHnz5rme/deoYcOGeO655yBJknC99u3bIykpyTRR4f79+6HVauHr64uWLVtCr9fjyJEjADIe01epVGjTpg0AoGbNmujWrZvZNoztJRoVtHPnTnTo0MFsXwMCAhAYGIjt27eblqWlpQlH+xj5+vpi586dOHjwINLT0wEAw4YNw8SJE03rOFpXe1mra15tc+/evejYsSN8fX1NyypXrozGjRubPdHh4uKCQYMGmf6uWLEiPD09haN1iYhyyhhurFSpUqZl9evXh0qlMr23BQcHIzIy0nT/ADI+w3Xo0AFlypQBkPHZoWLFimafp7y9vdGjRw+zUfcAMHDgQMX7mT1UKhWee+65bPd2S2lpaaaRsVmtX7/e7DPdxIkTcfToUQD2tYuvry8uXLiAjRs3mp4sad26Nb799lu4ubnZrI89+ezZfm5ZHoe82uaePXtQrVo1NGnSxLTMw8MD3bt3x8mTJ82ewhk6dKjptUajQfXq1REdHe3oLhE5BWOcUo7ExMTg33//RUREBOLj46HX6xEZGWl6vLF06dJ45plnsG7dOrz++uuQJAmhoaF47rnnTDE1jR8Ivvnmm2xlAxkzAFepUgUATLHmCppKpTK7GWq1WvTs2RO9evXKdlO2rKM9+9evXz9ERUXh119/xbx581CrVi106tQJI0eONE0cYs86OeFIPbVabY63Q0TkLKmpqfj3339x7tw5xMXFQafT4fTp06ZH841fDs2ZMwcxMTEoXbo0Nm/ejNatW5se3zO+N65atQqbNm0ylW18JC7rLLPOuEe1bt0aLi4u2L9/P1q2bIkDBw6Y4oqXKlUKdevWxYEDB9C5c2ccOHAA9evXN+vAO3HiBA4cOIDo6Gikpqaa3vNthQhITk5GXFwcLl26ZNaxCWSEizF+sQcAPj4+KFGihOI+/Pjjj5g0aRJefPFFeHp6okWLFujduzd69epl9gEvp3XNCVt1ze02k5OT8ejRI7PHQY38/PxM4QKAjP+ZLGO3q9VqU2cyEVFeuXbtGnbu3ImoqCgkJSWZ7ovG97Z27dqhYsWK+N///ocWLVrg9u3bCAsLw7x580xl3L17F8nJydnuBREREXj06JHZZEIFfX8sW7Ys4uLikJaWZorBCWR0qBo78XQ6Hfbs2YPWrVubYmortcukSZOQlJSEyZMnY8qUKWjYsCG6du2KYcOGCTtO7c2ntP3csnYc8mKb0dHRNu9zBoPB7H8D46P/Rmq12hR2jqioYMcp2e3mzZsIDg6Gu7s7+vTpg5o1a8LFxSVbzJnBgwdj8+bNOHbsGDQaDaKiosyCeWs0GqhUqmwTEFWtWhUNGzY0G9GSNeB1QWrQoAFmzJhh17qWdbR3/9588028+uqrOHr0KA4ePIh169Zh1apVWLt2ranj2J51rLH2ocvRehIRFQWJiYkYNmwYoqKi0L9/f1SrVg2urq64e/euWWfnwIEDMWfOHGzevBnPPfcc9u7dazabr/G98qmnnjKNsjF666230Lhx42zrFqSSJUuiUaNGOHz4MB4+fIiLFy+afYht3bo1du7ciZSUFISFheG1114zpc2dOxc//vgjOnXqhAYNGsDT0xMPHz4Uxv82duyVKlXK6v0i65eJ9rZHxYoVsXz5cty+fRv79+/Hnj17MHHiROzevdv0ZZ4jdbXFGJMtK2t1zYttGicdMcZ+zSotLc2sozQvRmMRESnZsWMH3nrrLdStWxcdOnRApUqV4OLiYvbloEqlwsCBA7Fw4UJ88sknCA0NRdmyZU1PDAIZ9wMPDw+r94LOnTubva8W9P2xYcOGWL16NY4cOYK2bdualg8ePBiDBw8GADx8+NBsUiJ72qVEiRL49ttvMXXqVBw8eBD79+/H7NmzsX79eqxevdqskzYre/LZs/2csOfzX15tU6PR2LzPGdONeK+j4oAdp2S3NWvWICEhAVu3boWPj49pedbJHICMRyGrVauGzZs3Q6PRoHHjxqhRo4Yp3d/fHwaDAcHBwahQoYLVbV28eDFf9qEg2LN/Op0OycnJ8Pb2RuvWrdG6dWu89NJL6NChA0JDQ/Haa6/ZtY67uzuAjMlBsrp69Wqe1JOIqKjYvXs3Lly4gBUrVpjNzm58JM+oTJky6Ny5MzZt2gRPT094enqia9eupnTj/Son4V0KWvv27fHDDz9g37590Gg0aNasmSmtdevWWLhwIXbt2gWdTmf60GswGPDrr79i0KBBZl8M7t69W7gtFxcXVKlSBeXLl8e4cePypP4xMTEoVaoUnnrqKQwaNAiDBg3CvHnzMGfOHEybNg0eHh4O1RUA3NzcHLonOto+llxdXVGpUiVcvnw5W9rVq1fNwh0RERWEX3/9FbVr18aaNWtMnVj379/H9OnTzdYbNGgQfvrpJ+zYsQObN282hWAz8vf3x8mTJ/PsXpCXunXrhq+++gqzZs1C8+bNrXZoWn6JZk+7JCUlQZIklCpVCj169ECPHj3wzDPP4NVXX8XJkyfN7r9Z2ZPP3uNiyThiNSUlxfQ6LS0NUVFRVkeB5nSf7VG9enX8999/SE9PNztHrl69Ck9PT4eejiQqzBjjlOym1+shy7LpppOeno7vv/8eqamp2WYTDg4OxrZt27Blyxaz0aYA0L17d3h6euKbb74x+6Zq8+bNWLBgQZ49mmDphx9+wHfffZcvZWdlz/699957eOGFFxAXF2dKf/jwIdLT01G2bFkAsGudSpUqQaVSmcXc+/fff3H69Ok8qScRUVFhfM/K+n62YcMGhIeHIz093Wz5kCFDcOLECfz+++/o06eP2Qesxo0bo1q1apg3b57pMW0gY+b0WbNmmb0n56U1a9Zg4sSJdr33tm/fHjqdDsuWLUOjRo3MRn02adIEbm5u+P333+Hr64v69eub0gwGg9njcTdv3sTSpUsBwHQfN7ZF1n3v168ftm/fbhafMz4+Ht9//322jmklUVFRaN26NZYvX25aJssyoqOjUbJkSdO+OFrXqlWrIioqChEREQAyHp2fO3euXaOfHN2mpf79++PgwYNm7bVp0yZcvHgR/fr1U6wHEVFe0uv10Ol0pkeyHz16hC+++AJeXl5ITEw0refn54cOHTpgwYIFOHPmTLbPcMYwYosXLzYt0+l0WLRoEUJDQ/Ol7jqdDhMnTsTatWuF63l5eeGzzz7DuXPn8Pzzz+P48eOQZRkAEBcXh40bN+L555+Hl5cXateuDUC5XfR6Pbp3747p06ebjeY0hvSxfCrFyN589hwXW/c5AKbPf7IsY86cOab9FXF0m5b69++PBw8eYMmSJaZl586dw9atW9G3b1/T0xdExQVHnJKZrVu3YteuXdmWjxw5EsHBwVi7di169uyJatWqISoqCi+88AJefPFFfPHFFxg7diy+/PJLlC5dGgMGDMDs2bPh4uKCnj17mpVVrlw5zJo1Cx9++CHatWuHypUr4969e4iNjcWUKVPMJuOwR3Jysmm0kPGGMWfOHCxcuBAA8N1336Fly5bYvXs30tLSMGHCBAdaxn727N8HH3yAV155Be3atUO1atVgMBhw5coVdO3aFX379gUAu9bx8fHBCy+8gN9//x179uwxbX/UqFGmD3u5qScRUWESERFhNprUqHnz5vjmm29Qs2ZNjB07FlqtFvfv30fTpk3x2Wef4aWXXsKLL76IKVOmIDAwEK1bt0alSpVw8eJFfP3112ZlqVQqzJkzB2+99ZZpQsTExETcuHEDo0aNMptQwV5DhgwxTbrw8OFDhIaGmj7wvP766xg5ciROnDiBkJAQfPnll4rvv7Vr18ZTTz2FU6dOYfz48WZprq6uaNy4MQ4cOGA22ZRKpcLo0aMxb948hIeHw9XVFUlJSfjll1/w6quv4scff0RCQgKaNm0KlUqFPn36IDAwEIsWLcKrr76Ka9euYfTo0ahSpQpKlCiBiIgI1KhRA0OGDMlRW1SsWBEffPABvv76ayxcuBDlypUzxROdOXOmad8drevgwYOxZs0a9O/fH1WrVsWdO3fwwQcf4OjRo8JO6dy0j6UxY8bg0qVLePnll+Hv74/U1FRERkZi1KhRZpNBERHllS+//NLqAJFvv/0WL774IiZOnIjOnTujbNmyuHPnDr744gukpaUhJCQEkiSZJr8dMmQIxo4di8aNG2cbId+hQwe8++67+Pbbb/H777+jXLlyuHHjBtzc3DB79uwc1/nQoUOmz2XGLzdfeeUV0wjGf//9FyqVCiEhIfD09MzWkWupe/fu+O233zBz5kwMGzYMLi4ucHV1RWJiIlxdXdG9e3e88847pnBk9rTLjBkzMGHCBGzfvh2VKlXCo0ePcOfOHbzzzjtmT1RmpVar7cpnz/bHjx8Pb29vvPfeeyhdujT++ecftGvXDo0aNcL777+Pn3/+GTExMejRoweaNm2q+OWro9u01LFjR7z55puYNWsWVq1aBQ8PD1y5cgWtWrXC+++/L6wDUVEkyfZ8NUFPhNDQUKtxwICMmWbr1auHhIQEHD9+HKmpqahXrx6efvppyLKM/fv3Iy0tDW3btjV9S9WrVy8EBQXhiy++sFpmcnIyTpw4gZiYGPj4+CAoKMg063t8fDx27NiBZs2amSaesiU9PR2bN2+2md6qVSuUL18eu3fvhsFgUHz0MjQ0FBUqVDCLY2eNUh1F+wdkdPKePn0ad+7cgYuLC/z9/bPFLbVnHQC4cOECrl27hjJlyqBx48a4evUqzp07h969e+e6nkREhcG+ffvMJhvIqnz58mjVqhV0Oh2OHTuG2NhY+Pv7o1atWgCAkydPIjo6Gk2bNkXp0qUBABMmTMCVK1ewbt06q2Wmp6fj1KlTuHPnDkqUKIHAwECz0SUhISGoVasW6tSpo1j3f//912xUT1Z169ZFrVq1cOLECVy/fh19+/a1Kx7YkSNHcPv2bbRs2RJ+fn5maWfOnMHly5cRGBgIf39/s7TLly8jIiIC3t7eaNKkCVxdXREdHY2wsDBUrVoVderUwaVLl3D16lVUq1bNbJLAmzdv4ty5cwCAKlWqmO37iRMncO/ePTzzzDOKdQeA2NhYhIeHIz4+HmXKlEFgYCA8PT3zpK5JSUk4ceIEEhISEBgYiKeffhr//PMPKlWqhICAAGFdHdnm8ePHERsbi86dO5uVdeXKFVy8eBEajQaBgYFmYXFs1SE0NBRVqlRBYGCgXe1IRE+2uLg4q4NejFq0aIEKFSrgzp07CA8Ph0ajQZMmTeDl5YWkpCQcOHAA3t7eaN68OYCM9+YWLVpgxowZNr/oefjwIU6ePImUlBT4+fkhKCjI1Nl57do1nDx5Ej169BBOngRkTDCUdWS+pV69epk6TqtXr46goCCF1njsypUruHnzJpKTk1G2bFkEBgaaQpxlZU+7pKam4uTJk3jw4AG8vb0REBBgNumiLfbks2f7d+7cwalTp1C2bFnT51O9Xo+wsDDcv38f1atXR+3atU2Pzrdq1Up4HBzZZkREBMLDw9GrVy+zR/Ojo6Nx6tQppKenm/3fBdg+Fw4ePAi9Xm8Wi5aosGPHKeWLs2fPon///li5ciWaNGni7OoQERGZJCQkoF27dpg4cSJGjBjh7OoQEREVCsuWLcOsWbOwb98+lChRwtnVISIqFPgsLuW5xMRETJ8+HU2bNmWnKRERFSoGgwGff/45PD09MWDAAGdXh4iIqFC4ceMGfvrpJ4waNYqdpkREWTDGKeWp999/H3v27IGLi4vZxA9ERETONm/ePPzxxx+Ii4vDvHnzzCZVIiIiehLp9Xr069cP169fR0BAAN544w1nV4mIqFDho/qUp44ePYqYmBi0bNkSXl5ezq4OERGRycWLF3HlyhU0atQoW1xQIiKiJ5Esy9i+fTvc3NzQqlUrsxiWRETEjlMiIiIiIiIiIiKibBjjlIiIiIiIiIiIiMgCO06JiIiIiIiIiIiILBT5ACbp6emIi4uDm5sbVCr2AxNR4WAwGJCamopSpUoxVhQVGN4Tiagw4j2RCgLvgURUGPEeWPQV+aMWFxeHa9euObsaRERWVatWDWXKlHF2NegJwXsiERVmvCdSfuI9kIgKM94Di64i33Hq5uYGAPj+0hlEJSfBXa3G5/Ua4+Mzx5Gi12esJPrCUZWLubFyU64kShPkFeVTkqVYN5UKM2o0x0dXjiDVYAAMCgWnO7hhpXJzMzWZ2sHMSl9AZ5brplJhhn8zfBTxX0YbAYDGYDObJDpuCmRZ0E5KbSiSm+MqSJP0j9PcVCp8HtQYH586/ridBES7Kjz3AfExFx1XR8+VXKjo5onxVeub3qOICoLxfHu6UjW4ubvDYNDj+pVLqFqjFlQqtZNrV3ixnZSxjezDdrIuNSUFtyKv8Z5I+cp4flV7qgw83FygN8i4eOMutFX8oFbl5kNU8cZ2UsY2sg/bybrkVB2u3X7Ae2ARVuQ7To2PYUQlJ+FqUgI81Bn/pF5LSkCyHR2ncm46OEV586lcKRfvP3KWYj0y/5m/lpKAZIMe0Ct1sIka0XaSlI8dp7KoM8zR4wYAmoz0bG0EQBJ1nOaiE14WtVMuOk6F5QKATtA5qrN9zKUsnarGa+56UuLja05Up1wcG+ExdzQtn/FRMSpIxvPNzd0dHp6e0Gdek+4enlCr2YljC9tJGdvIPmwnMd4TKT8Zzy8PNxd4erhBr8/4v93T3RVqNc89W9hOythG9mE7ifEeWHTxyBERERERERERERFZKPIjTs3IeDyC0fK1KI+IaHScsFyFkX65eKy70MnNruRX3rwIaZDTcym/KJwriqNKRURfnQiKzXp6G1/L0uPXwmgTgjRZaaC24LoS5lW8zkWVcrB9Hc1HRERERERERIUCR5wSERERERERERERWWDHKREREREREREREZEFdpwSERERERERERERWSg2MU4lvZTxkxmY0fg3II59qBSFUBbGlxTNhJ6L4KmiSinFu7Q37qplYMr8iseYjzFBJUFsT+FxU6qTqVzp8d+Zy0TxRCWFGeGFh1xQXzk3x0Yplq7gqxPRDPaS3so2JPnxawfjroqOKaBwXEVfAxmUNuxoHNNcbJOIiIiIiIiICjWnjzhNS0sTpqekpBRQTYiIiJwrNTVVmM57IhERFVeie6Asy4r3SCIiovzgtI7TiIgIDB48GE2aNEGjRo3w/fffm6X/73//Q9u2bdGkSRO0bdsWmzZtclJNiYiI8teJEyfQq1cvNG3aFC1atMDixYvN0hcuXIjmzZujSZMm6NKlC/bt2+ecihIREeWx3bt3o3PnzmjatCnatm2LjRs3mtJkWcbXX3+Nxo0bo3HjxujTpw9Onz7txNoSEdGTxikdp2lpaRg7diyaNGmCsLAwrFu3Dn/++Sd2794NADh9+jSmTZuGzz77DKdOncK0adMwZcoUXLp0yRnVJSIiyjexsbEYO3YsgoODcerUKSxcuBCzZs3C+fPnAQDbt2/HL7/8gvnz5+PkyZMYM2YMxo0bh/v37zu55kRERLlz/fp1vP3225gwYQJOnz6NGTNm4KOPPsK9e/cAACtXrsSmTZuwevVqhIWFoXv37nj99df5BAYRERUYp3Sc7tixA7GxsRg/fjw0Gg2qVauG3bt3o0OHDgCAdevWoUuXLujUqRPUajW6du2K1q1bY926dc6oLhERUb5Zv349fHx88OKLL0KSJAQFBeG///5DnTp1AABr165FcHAwGjduDI1GgyFDhqBatWoIDQ11cs2JiIhy548//kCDBg3Qq1cvAECHDh1w9OhRlCtXDkDGPXD06NGoVasWXF1d8cYbb0CWZezZs8eZ1SYioieIUyaHCgsLQ4sWLeDq6oq0tDS4uLjAzc3NlH7u3Dl06tTJLE+9evVw/Phx24XKWX5g/loSTNKiNO+OJJg0RhZOBqRQsGgWJ1GSg5PuZCvXSjvlC8XZt3JRtmjSL+HEUQrlWk7sZcDjiX5E5erF30NIasGJKJwQTFhs7ojOYcHkUHKWdw7jdSCr5ceTNwka2WxiqZwSTc4lKFfxNMuPNs7P64qKlbCwMLRt2xZAxhMZrq6uZvfE8+fPo0ePHmZ56tati3PnzhVoPYmIiPJaWFgYOnfuDCD7PdBgMODixYuoW7euaX2VSoU6derg7Nmz6Natm1PqTERETxandJxGR0fD3d0dr732GsLCwqDT6dCvXz9MmTIFGo0GsbGxKFWqlFkeb29vxMbG2izTTa2Ch1oNd7UaAEy/AQg7RUQzhwMQzzou6nRS6ogR5c1NuSJZivVQqc1+K3f0OjhLumLPtEPF2rFh20niDm+YZobP3kYQHhtJoVxJJaiUqL4KjSQ7eGwUqQQnf5Z9sdZOktp2XmFtlU5DUd+0qKNX8ZiLkx3hlvW8IRKIjo5G6dKlMXLkSFy8eBGyLOP555/HuHHjAAAxMTFW74lXr161WabBoIden/EDwPSbrGM7KWMb2YftZJ3BwPYg66Kjo/Ho0SP0798fkZGR0Gg0eOuttzBixAgkJiZCp9PB29vbLE+pUqWEnwv1Bhl6vQF6fcagBeNvso7tpIxtZB+2k3V6y4FZVOQ4peNUr9dj//79+PXXX1G/fn3cuHEDI0aMQJUqVUyPKhoM5hebLMvC0Z+fN2hs9vfCFq3zpe7FzaK67Z1dhUJvUVA7Z1ehSFjUqK2zq0BUJOn1euzatQuLFy9GtWrVcPbsWYwYMQI1a9ZEz549HbonXr9iHhP88vnwfKl7ccN2UsY2sg/bicg+6enp2Lt3LxYsWAA/Pz8cOHAAr776KgICAqDVagFk3POyMhgMwnvgxRt3zf4+HRGV9xUvhthOythG9mE7UXHjlI7T0qVLo3Hjxqhfvz4AoEqVKujduzcOHjyIF198EWXLlsXDhw/N8ty/f98U68aaj08ex/XERLir1VjYojVePXwAKcZv+4vaiFNRnfJwxOmiuu3xytk9SDbohY9AAwD0gnTRE+hKoyHz68uX3BzzLCNOFwW1wyun9ma0EQBoBCNOFcp19FF9pfAMsujY5Ea64OTXmY84XdSoLV4J22dqJ0mQV/iofjEacVrNoyRm1GmS9wVTsePr64uAgABUq1YNQMZj+J06dcLBgwfRs2dPlCtXLsf3xKo1asHdwxN6vR6Xz4ejZp1AqNUcBW0L20kZ28g+bCfrUpKTsn2hQwRk3AO7d+8OPz8/AEDr1q3RoEEDHDp0CI0aNYKHh0e2e+CDBw9MnarWaKv4wdPdFXq9AacjolDfvyLUgqehnnRsJ2VsI/uwnaxLSknL9oUOFS1O6TgNCgrC4sWLzZYlJSXB09PTlH7ixAmz9GPHjqF9e9ujI9PSDUjR6U3xTFN1elPHqbCzRWEUubDDRfD/sGJHjWi7etFj/OJihR2yVmKcJuv1GZ1duXrcW/icuTinqFMwnzpVLb+1zsbYxpltkqw3INk42ksYm1ah41S2fdCVHvMXET3Kr9TpKviyHrIoOHDWjJkdzclSOpIze0UlyfaJqkrPRfxZUX1FHbKKneWCciXHjk0qH9MkO9WvXx9hYWFmy5KSkkwfIuvXr48TJ05g4MCBADLew44fP256lN8alUpt1mmjVqvZiWMHtpMytpF92E7mVAxfQzYEBQUhKsp8dJrxc6EkSaZ7ZJs2bQAAKSkpCA8PxxtvvGGzTLVKMuu0UatV7MSxA9tJGdvIPmwnc2pRyD4qEpxyNvfo0QOPHj3C3LlzER0djd27d2Pjxo149tlnAQCDBw/Gf//9hyVLliAyMhLz58/HpUuXEBwc7IzqEhER5ZtBgwbhxIkTWLVqFe7du4eQkBDs27cP3bt3BwCMHDkSGzZswIYNG3Dz5k3MnDkTBoMh24RRRERERc2IESMQGhqK0NBQ3L9/H0uXLsXVq1fRpUsXAMCoUaPw+++/Y+fOnbh+/To++ugjVKtWDS1atHByzYmI6EnhlBGnJUqUwG+//YaZM2di1apV8PPzw0cffYRnnnkGQMaj+/Pnz8f333+PX375BdWrV8dvv/2GsmXLOqO6RERE+cbPzw+//vorvv76a8ydOxeVKlXC7Nmz0bBhQwBAw4YN8c033+Dnn3/GV199hYCAAPz666/w8PBwbsWJiIhyqXbt2pg9ezbmzJmDmTNnonr16li4cCEqV64MAOjWrRtiYmLw7bffIjY2Fk2aNMH8+fOhEk1iSkRElIec0nEKAP7+/vj1119tprds2RJ//vlnAdaIiIjIORo0aIAVK1bYTO/evbtpBCoREVFx0r59e2FItiFDhmDIkCEFWCMiIqLHnNZxmudk6fFP1r8BSAbBpDEKxUqi+JGC2IeifEqEMVlzE/dTFKtRMY6jYH+E5Sq0Qx5NdpWTNGFcVQCyZWYDssSkFeRVCGkpi9pClKR0bIR5FeokKlq03axxWlQWvyGO8SvpBSd4biZVExDFgVUqV3gti4pVmnCNiIiIiIiIiAo1PuNAREREREREREREZIEdp0REREREREREREQW2HFKREREREREREREZIEdp0REREREREREREQWis3kUJIh80cy/zvjD9EkLeLJZoRz54gmjlKaxUk034xgUhlZrVCuQZCWdUIfy0m0FCcgcnACKKXJiUQTbMn5NLmOqI3w+LhKmfslyZLpmIjqC73SRFiOtZNwsjCIJ49SbELRCrmY3Mj1gdr2Jm0n2TGZlYPXslK5ju6r6HzIzURuREREREREROR0HHFKREREREREREREZIEdp0REREREREREREQW2HFKREREREREREREZKH4xDhFRrhBY8jBrK+FoQYVgkBKBkH8SGFGhZiJKgcDICrE5zSLY2pJzrJNOctvGYoxIIVEeRVjpzoeHzXfYkiaYuNm+TtzmSSor3L8WUFewXFVbAbR1x+5Oa6i3bFW3yztpEq3vWGDLLimBPFPFQmOjZSLmMPiNhRsUynmLREREREREREVahxxSkRERERERERERGSBHadEREREREREREREFthxSkRERERERERERGSh2MQ4NcXqtIzdqUBSiBkqC2KGSoJYjUqxSGVR4ERB/FNJISarLKxT1rzS42UGSTkWplKsUluUuuYFxcoK2xTFG81V/FNjG2f9bXwtiHmrHE9UsIJeUB2lYnOzr4K4qxDF6BSdSwAkwf4Ij5tiTFxBmuCaE15vACTRdh0MVeroJUNEREREREREhQNHnBIRERERERERERFZYMcpERERERERERERkQV2nBIRERERERERERFZYMcpERERERERERERkYXiMzmUlOUH5q9Fc7uI5lICxBO8yIKJdaRczNgjnP9JqVhRuoMT6wAA1ArpjhJOyqMwK48oOTdtKCKqrmgyJYW8wiop1Tc3X3+I6ixIK3H98VuHu0YNNAY8b2igSs/IIwvqJJzTKxeTiQmPjeJ1Y7tSCvOx2SaaeIuIiIiIiIiICj2OOCUiIiIiIiIiIiKywI5TIiIiIiIiIiIiIgvsOCUiIiIiIiIiIiKyUHxinDpIKQqhKAZqbmKnimM1ioJAiguWBHEVZWsbNWT+qBRaQhQDVSUK3plPcUoByILAlZLikRVtV87+2/haeGwUyhUcO0kUY1MpvqwolqZSDF8HY5yq0rO8zrLMuNwgeGeRBOeSSiFOrEEYyNR2kqwUw1eQVxgf1dF8RERERERERFToccQpERERERERERERkQV2nBIRERERERERERFZYMcpERERERERERERkYViE+NUzvIDi9fCqIlKMSAdzaoQ21MSxSoVxGNUChkq3qaU7bUkS5BkCbJSUFZHY7IqVkqUpnRwRAEmHapNBgdjUyrGtBTFMRWF7lSKz+ngNgHzWKXZ89pOkvRZXkuPlxmXCw+NIGarUixSSVSwQVDhXHxFJL7OHc1IRERERERERIUdR5wSERERERERERERWWDHKREREREREREREZEFdpwSERERERERERERWWDHKREREREREREREZGFYjM5lIhojhbRXDO5oTRRkMOTASlUWFYLCraVJAvSTOsItivcGYVyRZTyiqrkSDsYizVkFmz8WkGVJY9oAqJcTJIlPF8MCuXmYmIpSW+77JI3BZNZZUmSVI+3ZZwcSiU6Nnrbabm7HgWZNeKDLjp0wjrl0/xkREREREREROR8Tuk4PX/+PObNm5dt+TvvvAN/f38AwL1797B8+XJERkaiSpUqGDVqFHx9fQu6qkRERPnq0KFDWLlyZbbl06ZNM933bty4gVWrViE6Ohq1atXCyJEjUbJkyYKuKhERUZ7atm0bNm/enG357NmzoVJlfDt//vx5/PXXX4iNjUX9+vUxbNgwuLq6FnRViYjoCeWUjtObN2/i6NGjmDhxotlyHx8fAMCjR48QHByMwMBAdOjQATt27MCQIUOwfv16lChRwgk1JiIiyh+XLl3CpUuX8Oqrr5otd3NzAwBERkZi4MCB6Nq1K1q2bImNGzdi+/bt+PPPP00fKomIiIqiU6dOITo6GoMHDzZbLmU+8nPmzBkMHz4cQ4YMQbNmzfDHH3/g8OHDVgfhEBER5QendJwmJCSgXLlyGDBggNX01atXo0yZMpgzZw5UKhUGDBiA5557DuvXr8eIESMKuLZERET5JyEhAVWqVLF5T1y8eDGaNm2KL774AgDQu3dvdOnSBTt37kSXLl0KsqpERER5KiEhAVqt1uY9cP78+RgwYACmTJkCAOjSpQs6deqE8PBwBAYGFmRViYjoCeWUoSoJCQnCkaNHjhxB27ZtTSNp1Go1WrdujcOHD4sLlvA4sKBk348xvKetHyGlzMIfSfAjyGdQ+pFs/1jbuczXkiwJf8R1ysE2c9TAClSygz8Q/2Q9j4yMyxw9brn5USAZRD+S+EeGzR9RnbKtC4tlesd+VAo/wvyidshF3lxdj0R2sOee2K5dO9PfHh4eaNq0KQ4dOlQQ1SMiIso3Ob0Hli1bFnXr1lX+XEhERJRHnDbiVK1WY/HixTh37hx8fX0xcOBA1KxZEwBw+/ZtdOjQwSyPn58fjh8/brNMN7UKHmo13NVqADD9VqQ0UZCjWZXm8xF1WQtmChLmAyCLdlv1uFwPldrst3J9HZwASpRPKW9uOlZFeRUmcZIy07O1EQCobefNr4nGlAjnLlKolCjZ3cW+mY/cNWqz34D4PNUL3nVkhXck0fktyiucLAwKc585+PWSGx+hJjslJCQgPT0d8+fPx5UrV/DUU09h8ODBqFixIgDgzp078PPzM8vj5+eH27dv2yzTYNBDr8/4AWD6TdaxnZSxjezDdrLOYGB7kHWJiYlITEzE7NmzcevWLVSpUgXDhw+Hr68vkpOTERsbm+N7oN4gQ683QK/P+Bbb+JusYzspYxvZh+1knV40wTQVCU7rOD158iTq16+PVq1a4ejRo+jXrx+WLVuGRo0aIS0tDRqNedVcXFyg0+lsljm9SSOzv39p2ypf6l7cLGzSxtlVKPQWNmYb2WPW4JbOrgJRkZSQkIADBw6gVq1aaNWqFXbt2oXevXtj/fr1qFq1qkP3xOtXLpn9ffl8eL7UvbhhOyljG9mH7URkn4SEBISHh+P5559HixYtsHnzZvzxxx/YuHEj1JkDYdQWA2JcXFyQlpZms8yLN+6a/X06IirvK14MsZ2UsY3sw3ai4sYpHacTJkzAhAkTTI/i9+vXD48ePcLixYvRqFEjlCxZEklJSWZ5EhIS4OXlZbPM/zsWhusJiXBXq/FL21YYu+8gUuz5tv8JHnG6sEkbvHpsP5INeo44zULSPx5xurBxG7x6PLONAEAvGHHqpC/WJMFpLhkU9lVQZ88o+0eczhrcEu+tPoSU9IzKCEecuthOK04jTquWLIFPmzdSXpGeeN988w0AmO6J/fv3x5AhQ7By5UpMnjwZJUuWRGJiolmehIQElCxZ0maZVWvUgruHJ/R6PS6fD0fNOoHZPnjSY2wnZWwj+7CdrEtJTsr2hQ4RACxZsgQqlcr0lFTfvn3RvXt3bNiwASNHjoRKpbL6ubBChQo2y9RW8YOnuyv0egNOR0Shvn9FqNV8EsgWtpMytpF92E7WJaWkZftCh4oWp3ScGgwGpKenw93d3bSscuXKOHPmDACgZs2auHLlilmeiIgI06P81qTqDUjO0lGaoteb/e0oQR+mkELfnDD+obCTUqFzThZktdZ5lGzIbCeljlNRwaK8+dmZ6OjBUepMtOgcNbURAEmXPx2niueLgGi7lvtiqeR12+kpwhgAVtZP1yNFp9xxKgvqq/Qkn0HwjiV6AkJ4/kKh/R1MS+UjKmSn9PR0AICrq6tpWaVKlRAXFwcA8Pf3t3pPzBrzzZJKpTbrtFGr1ezEsQPbSRnbyD5sJ3MqFduCrEtPT4dKpYKLS8Y36y4uLnj66acRGxsLFxcXVKlSBVeuXEHjxo1NeS5fvoyePXvaLFOtksw6bdRqFTtx7MB2UsY2sg/byZxa5aSYfpRnnHI2T5gwAW+99RYMhoyOhYSEBPz777+mG2K3bt3w999/m2LXXL16FTt27ECvXr2cUV0iIqJ88/zzz2PatGmmv6Ojo3HgwAGze+KGDRtMHaknTpzAyZMn0aNHD2dUl4iIKE/IsoyePXti3rx5pmURERE4ffq06R7YvXt3/Pnnn6ZH8//++288fPgQHTt2dEaViYjoCeSUEacTJ07Eyy+/jJ49e6Jq1aoIDw9HQEAAXn31VQBAly5d0LVrV/Tr1w8BAQEIDw/H0KFD0axZM2dUl4iIKN9MnToVY8eORZ8+feDn54eTJ0+iY8eOGDRoEABg8ODB2Lt3L3r16gV/f3+cPn0aEydORLVq1ZxbcSIiolyQJAmfffYZ3n33XezduxelSpVCWFgYhg0bZpooeMyYMTh8+DB69OiBihUr4syZM5g+fTp8fHycW3kiInpiOKXjtHLlytiyZQtOnz6NuLg4VKpUCf7+/mbrzJgxAy+++CJu3ryJ6tWro3r16s6oKhERUb6qV68eduzYgVOnTiExMRGffPIJKleubEp3cXHB/PnzER4ejujoaNSuXRsVK1Z0Yo2JiIjyRps2bbBr1y6Eh4dDp9NhxowZZvFLS5YsiVWrVuHkyZOIjY1FYGAgypUr58QaExHRk8YpHadARuynhg0bCtepVasWatWqVTAVIiIichJXV1c0bdpUuE5gYGAB1YaIiKjglChRAi1atLCZrlKp0KgRJ9wkIiLncFrHaVEhnDQmN7O+O7pRpQmRRMlZJ0XKnLkSBinjRzQhFRRmZ8+nWMey0r5Kjm1YacIk4wRQxn2WdJIpj2gG+9zM8CQptL8wr+jY5OYcFeVVmkxMVKV8Ol9Ep4ussFFhXkePTX69PxARERERERFRgeBUZ0REREREREREREQW2HFKREREREREREREZIEdp0REREREREREREQWnowYp/kUU9EplOImCtIlQ5bX0uNlkgGKgSeFyUqxSEWEoVNzceBE7ZAuLleVGeNUpc78nSZBZYxxmk9xK2VRnFIFojp5XVXYriCzrMqfOgmbMJ+uVZVOnC6rBYlOiNdKRERERERERM7HEadEREREREREREREFthxSkRERERERERERGSBHadEREREREREREREFopNjFNZ9fjH9Lc98SiVglaKYhiKsioEP3Q4VqZSuQZRcM8sLy1jnCoRblYYqDQX5TpO0gvSFGKcGvNKWf42LROVq3BMZcHXFFIu2kEpjqmQ6KsTUfzTrOehnOV3bmPA5iK/OtV2mkEUwxQQxzEVxnrNp6C3REREREREROR0HHFKREREREREREREZIEdp0REREREREREREQW2HFKREREREREREREZIEdp0REREREREREREQWis/kUBoZsosMWZ0xWYvsIkNWZU7cIpzEKRcbFc2JJJqkCYCcX7MiiSaPylol42xEBinjR4FKsDvC+apyM/lWLkh62wWLJngCAFV65m/j3/rHy0QTSylOsiWcgMh2WolIcRs6PNGYEuGxebxR47kuGeTH571gtitRO4naAQA0SbbTDC620ySFcu2aSI6IiIiIiIiInigccUpERERERERERERkgR2nRERERERERERERBbYcUpERERERERERERkofjEOFXJph/T38bAhYIgnIrxIR2MfagUq1ESBVUUxmQVBwUV7k+WNGMISslgR2xOBcJtCmJd5ppgu6J9UopxaoyPKmXWXdJLj5cJ8iq1oyjdLcbxIJvC7ebm/BalqbNsP/Ncl/RZ20eQWW37nHBJFFfYoLGdVyU4Nnq17TQlovObsVGJiIiIiIiIii+OOCUiIiIiIiIiIiKywI5TIiIiIiIiIiIiIgvsOCUiIiIiIiIiIiKyUGxinAIApMwfy9eCeIsyFGKGihKFsT2FxYpDTwryCmOjQhxzUTJkKdiynfIrVmN+xoB0MD6nWTtYTc/+23KZNSqdsFjF+J0OczDWqxJRbM+sxVqLcSqK8atJFlyPirGBbacx3CgRERERERER5SWOOCUiIiIiIiIiIiKywI5TIiIiIiIiIiIiIgvsOCUiIiIiIiIiIiKywI5TIiIiIiIiIiIiIgvFZ3IoCbYnhxLNBZSLyZYg2y5YNIlNxgqich1KUi5WJWd7LatkyLIs3BdAYX9yMyuPg+2gWKxowiSlcg1WftszOVS6Qp0EeYVpCvUV76tCZsFEWUoTNYmI2kJYrsKkag5TKleQLrw08qu+REREREREROR0HHFKREREREREREREZIEdp0REREREREREREQW2HFKREREREREREREZKH4xTi1XJYX5doiiB+pGJ4zn2KnikJamsXRzBYLViHWq6AhctPMoviRirE9hQUL0gTxRIHH7ZT1t/G1Os12PnWq0sFxLE3Si4uV9LYzqxTyyoJGNmgcP7KiOKbCNEm8TdH5IkozKLzTyWpRmuA6F331xK+liIiIiIiIiIq0QvHR/tSpU7h06VK25ZGRkTh06BCioqKcUCsiIqKC999//+H69evZll+5cgWHDh1CdHS0E2pFRESU//bv3487d+5kW37hwgUcPnwYMTExTqgVERE9yZzecXr+/HkMGzYMixcvNls+bdo09OnTB7NmzULv3r3x5ZdfOqeCREREBWTv3r0YOXIkNm7caFqm0+nw1ltvYejQoZg1axa6deuGX3/91Ym1JCIiyntr1qzB6NGjsXfvXtOyxMREjBgxAi+99BK++eYbdO7cGevXr3deJYmI6Inj1Ef1DQYDPv74Y9SpU8ds+fbt27FlyxaEhISgUqVKuHbtGgYOHIgOHTqgVatWTqotERFR/klOTsb06dNRt25ds+WrV6/G+fPnsW3bNpQuXRqnTp3C8OHD0bFjR/j7+zuptkRERHnn/v37+Omnn1CjRg2z5b/88gtSUlKwfft2eHh4YPv27ZgwYQLat28PX19fJ9WWiIieJE4dcbp06VKUL18ezZs3N1v+999/o3v37qhUqRIAoFq1aujcuTO2bNnijGoSERHluzlz5qBdu3aoVq2a2fK///4b/fv3R+nSpQEAQUFBaNSoEbZt2+aEWhIREeW9GTNmYPjw4fDx8TFbvm3bNgwfPhweHh4AgC5duqB8+fLYtWtXwVeSiIieSE4bcXrr1i0sWLAAf/31F5YtW2aWFhERgd69e5stq1GjBvbv32+7QEmGnPkDwOy1UC5mNpJEs9EoEVZNMBmNwsRGYo/ra5zURlZlTCglKZYrqFNeTMJljcJEQcJJnuydJMsKlS5zvcwyJB2gSs9ME0wOpUR0OoomcVKli89jtc52muftVGHedE/bsyIlPO0izCsinABKbfu4CidbgngSJ9G1rHdTeC9wdDIrB9OILJ05cwabN2/G5s2b8X//939maRERERg6dKjZsho1aiAiIqIgq0hERJQvdu3ahQsXLuDrr7/Gzp07Tct1Oh1u3LiRbRQq74FERFSQnNZxOm3aNLz00kt4+umns6XFx8ebvlU0KlGiBOLj422W5yap4aHK+AFg+p2fctVx6vBGc5P1cWZ3tdrst1IfpbijN39ICpUSpYo6KdUKs8WrMq8Kd43a7DcAaAR9iZJB3EgqwWaFk7MrfAEg6IeEm5v4OlAL0tNdbKdl7RR0z1zPPcv6BkE75arjVNghaztNpS74jlM3NXtOyT56vR5Tp07F+++/Dy8vr2zpCQkJ8PT0NFtWokQJq5NnGBkMeuj1GT/GbZBtbCdlbCP7sJ2sMxjYHmRdYmIipk+fji+//BIuLub/QCYlJcFgMOT4c6HeIEOvN0CvzxgtYfxN1rGdlLGN7MN2sk6v0E9AhZ9TOk43b96M27dv46WXXrKa7urqivT0dLNlOp0Orq6uNsucUbeJ2d+LGrXNfUWfAAubt3Z2FQq9Oc+1dHYVioRvnmf8YSJHLF68GN7e3ujTp4/VdBcXF+h05kPLle6J169cMvv78vnw3Ff0CcB2UsY2sg/bicg+s2fPRvPmzbOFbgNg6kjN6efCizfumv19OiIqD2pa/LGdlLGN7MN2ouKmwDtOU1NTMXPmTAwbNgwHDhwAAERFRSExMRH79+9HmzZtUKFCBdy9a37Du337Np566imb5X509hiuJSfAQ6XGokZt8UrYPiTn87fbThlxmosvbyS9+YjThc1b49UjB5Ci1ys/qu+MEacGhRGngsMrpdtOU6cpjDhNyfjtrlFjznMt8faGQ0hJz9iYJkWwTaURp4I6qQSP2+fmUX2PO44/qp9YwfawUcsRp9883wrvLz2IFF1GOxW2EadppQp+xGnVkiUwvUkj8XbpiXf//n3MnTsXEydOxJ49e0zL1Go1jh49iqZNm1q9J965c0d4T6xaoxbcPTyh1+tx+Xw4atYJhFqd/09iFFVsJ2VsI/uwnaxLSU7K9oUO0aVLl/Dnn3/i888/N90D4+PjcenSJZw6dQpBQUHw8fHB3bt3ERgYaMp3+/ZtNGzY0Ga52ip+8HR3hV5vwOmIKNT3rwg1nwSyie2kjG1kH7aTdUkpadm+0KGipcA7TnU6HRo0aICzZ8/i7NmzAIDLly8jPT0dq1evRps2bdCiRQuEhoZi/PjxkCQJ6enp2LdvH1544QWb5abKerOO0mSDPt87Tp1BUnpW386OVTmzJzQZ6UiGXrnc/OojFsUiVerr0tuulKiTUqnj1Ng5anxkPy1Fj7TMjtP0XHRwijpzNSm287omig+qx1Xbjyrp3MSXeEylkjbT5HTb149ZJ2XmPTFFr0eK8RFFQVwC0fcNSv3zovSUMoI4vIqxU0UnomMVSkHxe/+hvJecnIxWrVqZxfC+fv067t27h5CQEDRt2hQtWrTAvn37MGLECAAZjy4eOXIEX375pc1yVSq1WaeNWq1mJ44d2E7K2Eb2YTuZUxVACC0qelJSUtC2bVts3brVtOzevXs4cuQIPDw8EBQUhObNm2Pfvn3o0qULAODu3bs4d+4cpk+fbrNctUoy67RRq1XsxLED20kZ28g+bCdzalHMPioSCrzjtGTJkpg3b57Zsq+++gqPHj3CjBkzAADBwcFYtmwZJk6ciM6dO2PLli2QZRn9+/cv6OoSERHlm8qVK2e7J44fPx41atTAuHHjAAAvvvgi+vXrh+nTp6Nx48ZYvXo1atSogY4dOzqhxkRERHmjfv362e6Bw4YNw4ABAxAcHAwAGDNmDEaMGAFvb2/UqFEDS5YsQZcuXRAQEOCMKhMR0ROoUHwNUKNGDdSqVcv0d6lSpfDnn3+iTJky2LRpE6pUqYJVq1bB3d3dibUkIiLKfwEBAahSpYrp70qVKuHPP/+EXq/H5s2b0axZMyxcuFBxAj0iIqKipmHDhqhQoYLp7/r162P58uW4d+8etm3bhl69euGbb75xYg2JiOhJ45TJoSwZv1HMqkKFCpgyZYoTakNEROQ8Y8aMybbM398fn376qRNqQ0REVHAmTZqUbVlQUBCCgoKcUBsiIqJC0nGaJ2QJUuYPALPX+bdJB+Mi5mabClEghbFKJSuvpYwfYYxHy7w5oZRPGL5TnFl2MISk0kRYxniYWX8rxci0p1zRJE5uj2xnLnn0hrBcuaSnzTSVwog0YbxRQZpBnf21QQ0YMndDFryziCZxUrpkU0VxTAXlGlzE57fDMU4FlLZJRERERERERIVb8ek4JSqCRvZogh7Na0LS2+5kUyU97nHdtfEE1s7fURBVIyIiKlBqSYZaFsykaEEvaaDP5y/JiYiICoKscYecg0iKEgyQ0lPysUZEZMSOUyIn8vP1Qu0aFRF3YAX0KfE211O7e6FU6xEI/+9awVWOiIioAKkkCWqNq933RFkvQ/C9IxERUZEhSWpILh523wOhSy7A2hE92dhxSuREy7YcxcBO9ZEadQb3N31hc72yvScjPU2Hv37eXoC1IyIiKjg6A6CW9HbdE2WDHjpDoZjjlIiIKPfSkyGrXe2+B0rp7DglKij8j5PIiaJjErBu9xn49HgfKncvq+uo3L3g030iQpcfwIM7cQVcQyIiooKjl1Uo0+sD4T2xTK8PoLcn+DgREVFRIRsg6dPsugdK+jRAVphgg4jyTPEZcSrJkDN/AJi9dgqlTedXSC57y7WcHEqlUOH8+nySi3JF9wqD3nZDGFzsK9+QeXUYXAHjoBbRBFBqhRAz6lTrbbxs3WEMaFcXvl3fsvrtom/Xt6ByL4k/v9kIOTEpW7ok2z52D1r5iSslOl8Ex0ZWS9ley2oJssH42nZegyAtrZTSJE6iNNHEUQrlagTpjp6jojKJiAqQ3mD7/ejUDfEXck+V9gAAyIaMGRmjH6VCUmW8GRsE5T7l4y4s19bchToD4O7upXBP9EKKgxNEEhERFVrpyVC5l1K8ByKVg2mIChK/ridysnsPE7B50Q74dJ+Y7dtF42jTzYv34sHtWOdUkIiIqADZGnXK0aZERFSsCUadcrQpkfPwP0+iQuCPL9dB5VYSvl3fMltuHG26+vtQJ9WMiIioYOkMGR8Qrd8TvaDj50UiIiqu0pOF90AwtilRgWPHKVEhcD/qYbZRpxxtSkRETyrLUaccbUpERE8EK6NOOdqUyLmKUYzTLD+w8jo/5CaEobNjnFrmUfocIoqBKtpmLmK9KsVd1WtEcUxFaeKdVekyfqsz42LqvGSk6TNeqzxsl6t3Fzd+uRVhthPd3bF6dih6vdLZFNPGt+tbULkpjzaVdTphuqNEn02zphlfy6osrx09vxXOQ1F8VGH8U6V4o8IYpw6e+y6McUpEBUMUwxQAvt512WZaeGS8MK9On/EBzU0FTG6kwgcbzyI18zObLIix/W77GsJy29QsI96uRaxTxjYlIqInhkWsU8Y2JXIufm1PVEjcj4pB6O+74dN9IjSln84YbfrbLo42JSKiJ5Jx1Kmm9NMcbUpERE+OLKNOjfdAjjYlch7+B0pUiPz5XQhUbiVR9cOdULmVxJ/fbXJ2lYiIiJzCGOu06oc7GduUiIieLJmxTo33QMY2JXIedpwSFSLGUaduFbQZo01vxTi7SkRERE6jl1Vwq6DlaFMiInqyZI46daug5WhTIicrPjFORfIrFml+xSkVUdimMC6olH09WS1DlmRArdBIonJFWRViS0q5aENZbzuzrBLEOBXEwgQAOS0jrz6zTfQeMvSZMU4Nrrb3R+8m3hnJ1VW84Ux/fL8FJUuXxJ+zt2bkEeyLEqXPmaK4oAa1qH2zv84a41R4ngrqZFB4R5IF56koTfErIsF5KqkF/6SI9lPDf26IqGD8ceKmMH3L0Vs203Q6+4KGumsAwAeRdx4hJT1jmbe3m831z9wXx05VinFqqp8BAGSONiUioiePLgmyJEHSJTm7JkRPtCej45SoCHl4Nw5fj13k7GoQEREVCuw0JSKiJ5MMKS3B2ZUgeuLxuSciIiIiIiIiIiIiC+w4JSIiIiIiIiIiIrLAjlMiIiIiIiIiIiIiC8UrxqmEx5O1ZH2db9uzbyKmHBPNpyQrFCyczErO/lqSM35Ekz8BkEQT3eRiXyVBG8oK+yrKiywTG5XzKIEpjTth5vGduJecCFlhwipZnfF9giFzUiaDuwEGQ8b+S+m26ySlC4uFVMJTvIKDontUt5kmK1zhelfBJFqCvFknlTKbHEptvsxqXsFhVT42gkTR10C5OL9VokmnBOegMB8RUQ6lCgJ9rjl6W5j30rlIAICfjwemD2uM/1t1HHdjkwEAvuV9hXmfaVUVAOAiyQBS0bSOH3SZb+SlPGzfKALKlBSWS0REVLAkyK4lIKUlInezRxPRk4YjTqnYe6t+a/T3D8Sb9Vs7uypEREROM6FvIIa0qY4Jfes5uypEREQFy8UTksYdcMmfwSxEVHyx45SKtQqeXhhaqyEu3kvAsFoN4efBETBERPTkedrXEy90qomL9xLwQqdaeKq0h7OrREREVDAkFfRqN1y8lwC92g2Q2A1CRPbjOwYVa68HtkRCajo6zTuIhNR0vF6/lbOrREREVODe7V0PCWn6jPthmh7j+3DUKRERPSE0HohPzbgHxqfqAQ2/PCQi+xWvGKeOEMXJBBRihuaiXKVYpY6WK5I15qIx5qNGBgwyJBdBDFOI4zXKwpisOahfDkmCuJWS2oAKHhmjTT/ddhG3HqXgu11X8En3hvj5zAHcTU6wmdd0aDJjnMLVAGTGOJVF3zWImxCyt2C0q6ARpTSduFxBlfQu4vNMGMdUlJZlm6a4puosu+Hg6S2qDwDzczhbnRyPOSyKl6tSieL7CvJJCicEEVEOJOv0NtPi4lOFecuq0/FCp5r49J/M++HuCHzyjBZfrTiElBRx3nuPUgAArpnv+/fjU5GW+fZW2tP2fW3apnPCcrePbydMJyIiyhOZo02//ucCbj1Kwdc7L+OzHrWhTk8GZP6/TkTKOOKUiq3XAlojITUdc/ddAwDM3XctY9RpPY46JSKiJ8fE4MZW74cTBzV2bsWIiIjyW+Zo06z3QI46JaKcYMcpFUsVPLwwxL8Rvtt1BfGpGdPdx6em47tdVzCsViPGOiUioidCOS9XvNS9Hr7bY3E/3HMFo3vUQ4VS7k6uIRERUT4xjjbdednsHvj1zsuMdUpEduM7BRVLlqNNjTjqlIiIniTPt6wivB++2V3rnIoRERHlN4vRpkYcdUpEOfFkxDgVxQVV6jp2NG6iUmxPR2OV5iJmqJQlPqQxPqikliFJsjCGKSCOASlqCGH8UwCyKNarQRyYUrax3QqeGaNNP9120fTNopFx1Okn3Rth3ulDuJtkJdZpWuZJoVI9/tuQ2V6ptk8Yl0cK9XVx7HKL7lBemO5onFIAwvNfFDvVGNc062uzGKcCSU/bjtMHte2kjDo5HsdUWK6DMYclR2MgExHlkI+ni820nRPa20xzVQNTt563fj/ccwWfdq+Ny0l6xKWkW80/o2dtAIBBr8fVC6cxL7g+VOqMN+v4ZOt5AGDHyds204iIiPJdltim1u6BjHVKRPbiiFMqdl4PbGl1dI2RcZTNG4EcdUpERMWXiyrjw6Hofhifmo5utcsWbMWIiIjym43RpkYcdUpE9rJ7CFz//v0RHR1tNc3HxwctWrTAO++8g1KlSuVZ5YhyqoKnF4bWamh1tKnR41GnDTEv/KD1UadERAJt2rSxulySJPj6+qJDhw5444034OHBf8bJeSQJZnHdLMWnpuPbXRH4tHtt/H3hvs1Rp0RERtHR0ejfv7/VNEmSUL58efTo0QOjR4+GRvNkPNxIhZBgtKkRR50Skb3svps9ePAAgwcPRvny2R8bTkxMxKZNm/D+++9jwYIFeVpBopwYW68F3NRq1KvghZ8H1re5npe7Bm5qNcbWa4Hp/20vwBoSUXFw//59TJ48GW5ubtnS4uLi8L///Q9xcXGYPn26E2pHlDHaVK2S7LofuqhV6Kotg7Wn7hZgDYmoKDIYDHj06BGmTJliNf3BgwdYunQpdDod3nzzzQKuHVEmjQfUKpVd90C1SpUx6lSXWIAVJKKiJEdfA/bs2RNarfVJBAYNGoQ2bdogOTmZI2zIaW4nPsKJe3dQr7IGSqf3iXt3cCcxvmAqRkTFzoABA+Dt7W01rXPnzhg1ahQ7TslpDLKMlHQZA4OeRprO9iia+0lpuPIgCbHJugKsHREVZRqNBsOGDbOZHhQUhO+++44dp+Q0sqyHTpeG4PriuSIAIE2XBhdZz+kJiMgmuztOf/jhB1SqVMlmelxcHGRZhlqtMLtLfpGQMeGScRKjrK9FkVxFk80Yy7WVpJRXQDiJjoMT1SjKWl/ja5UMQFacrEo0OZRCTnGdBJllhcmhrOX95fR/+OX0f+KJpbJ9frQ4QWQrvzNfyy62P3y6PhJfTrK77XSDi+3rRu+qMOmUnZM4Wd2uIF2U1+wUlR7/liWLZTkkK02aJpyMSXQyKWxYcL4YDLYbWBJcyLIgHxV9S5cuhaenp830mJgYPqJITqXP8kYdHW+7U7Ri6Ywvu5/y9kDPOn62y8t8j3XVqKBWZ7y/uXm52lx/9/sdclRfIioafH19sXDhQuE6MTExzvtMSARASk+BK1KcXQ0iKibs/lTXqFEjm2kLFizA4sWL0bNnT7i62v4nOqudO3di2bJluH37NipUqIARI0aga9eupvSwsDDMnTsXkZGRqFKlCt5++23Ur297mD0REVFBadGihc20r776CqtXr8aQIUPsLm/9+vVYu3YtHjx4gMqVK2P06NFm29i9ezcWLVqE6Oho1KpVCxMmTED16tVztQ9EREQ55erqiqZNm1pNS01NxYwZM7B+/XpMnDjRrvJkWcayZcsQGhqK+Ph4+Pv7Y+zYsahXr55pnY0bN2LlypWIjY1F/fr1MXHiRPj52f6ih4iIKC/lyZCoBw8e4MUXX8QXX3xh1/r79u3D+PHj0bt3b8yePRtdunTB22+/jePHjwMAbt68idGjR6NJkyaYPXs2goKC8NJLL9mcnIqIiKiwiImJwYQJE+z+0Pi///0PX3zxBUaMGIHvv/8egYGBeOWVV3D9+nUAwKlTpzBu3Dj06NEDs2fPRrly5fDiiy8iKSkpP3eDiIgoR9zc3HD//n3MmDEDzz//vF15fv75ZyxevBivv/46vvvuO5QpUwYvvvgi4uLiAADbt2/HJ598guHDh+O7776DTqfDmDFjYDBwIh8iIioYefIc4eTJk3O0/s2bNzFu3DgMGDAAAFC7dm2sXr0aYWFhaNy4MdasWYMmTZrgjTfeAAAEBATg8OHD+Ouvv0zLiIiICqMvv/wyR+vfuXMHkyZNQo8ePQBk3BOXL1+O8PBwVK1aFcuXL0ffvn0xYsQIAMD//d//Yd++fdi6davpPkpERFQYzJs3L0frx8bGYurUqejQISO8x6RJk7By5UpcunQJTZs2xZIlS/Dyyy+jb9++ADKe6mjdujUOHTqE1q1b53n9iYiILDklAJtlMPHjx4/j5s2bpsc+Tp48iebNm5ut06hRI5w8edJ2oaosP4D5WFpR7EOFWIzCOKa5KVcQU1EY/1SJKCZrlvoaX0uSnPGTP5tUDtcqWkGpHfSiOKaOlytllitl7plkkEzLRHn1ClEqDG62L7c7LWzHSswNUfxTIAdxTAXlGl/LqizLHTwplEKcysKguMKNissVnEsG0cB8wfuDXsMQ82Qfyy8Ed+zYgZSUFAQFBQHIuCeOGTPGlC5JEho2bIgTJ06w45TsUrG0u7OrQERk1ZQpU0yvZVnG+vXr4ePjY5qQ+PTp0xg7dqxpHTc3N9StWxcnTpxgxykRERUIp85cMXfuXKxYsQJqtRrff/89GjRoAAC4f/8+fH19zdYtU6YM7t27Z7MsN5UKHio1PFQZPUHG3wDEvTEKHUv51XEqWkHYjanUmSjIqsqyr+6Z7WP8rVKY6MrRzlGDQkPIKtsHQFbqdRWm56LjNDOv1XNJkNddIw6C7+ZmO10pr6Ny03Equjaylmusu9k+iDrw1aKTVKGD09EJ2ZR6ZAXlCt8DBGnuEidFoJyZNm0aQkND4e3tjcWLF6Ny5coAbN8Tb9y4YbMsg0EPvT7jB4DpN1nHdlLGNrIP28k6g4HtQWJvvvkmDh06hIoVK2LlypXw9vZGQkICkpKSrN4D79+/b7MsvUGGXm+AXp/xOL/xN1nHdlLGNrIP28k6vSE3I+OoMHBqx2lwcDBatmyJAwcO4MMPP8Rvv/2GunXrQq/XQ5Kyd66IYtnMqGUepHxRYLs8r29xtLBmJ2dXodBb2LhN3hTUO2+KKazm9mjp7CoQFWmvvPIKnn32Wfz999949913sXLlSlSsWBEGgyHbPVGSJOE98fqVS2Z/Xz4fni91Lm7YTsrYRvZhOxHlzPvvv4/bt2/jr7/+wltvvYU///zTlGbtHij6cuLijbtmf5+OiMrbyhZTbCdlbCP7sJ2ouHFqx6mfnx/8/PzQtGlTXL9+HQsXLsT3338PHx8fPHr0yGzdR48eoXTp0jbL+ujSUVxLToCHSo1Fge3wSvheJBu/3S6MI05Fj+rn4jFz4YhTl8f/YLir1FhYsxNevbwTKQY9VOpcjDgVpBlE+wJATheMOBU9ig/k36P6hscjThc2boNXj+9/fC6JRpzeFY8wfHqv7Ylc7jYtfI/q52TE6dweLfHW1kNISTdec7bzppQVfAOpcB7KCuk2KY041QhGnIq2KXh/qObmhc+rNbeZTmSpUqVKqFSpEpo3b47z589j+fLlmDRpktV7YlxcnPCeWLVGLbh7eEKv1+Py+XDUrBMItZqjoG1hOyljG9mH7WRdSnJSti90iLKqVq0aqlWrhpYtW6Jbt2743//+h1GjRsHFxcXq58KqVavaLEtbxQ+e7q7Q6w04HRGF+v4VoVbnyZzIxRLbSRnbyD5sJ+uSUtKyfaFDRYtTOk6XLl2KKlWqoGPHjqZl3t7euHPnDoCMyaDOnTtnlic8PBx169a1WWYq9EhGOow9W8lIRzKUO7ugMIpcEmUWdaoq9e842nGqQBJ0EElylp3NXC1F1iNZ1kOlMHxcUjk23F4YHxKAQdCzJ+sV3mxFHaeCNFF8WbO8mY+UJ+sMSLbjcYMUH/E6qc3dbCfKtr81V+rzE1GKlyuLqiw6va3kS0nXI9nYcSo4dKk62xuVlR5jENRX+Bi/Yqxd0c469hh/ioaPqJB95s6di1atWqFJkyamZV5eXkhJSQEA1KlTB+fOnTNNjAFk3BODg4NtlqlSqc06bdRqNTtx7MB2UsY2sg/byZxKxbag7PR6Pb799lsMHDgQNWvWBJAxmtTLywupqalQq9XQarU4e/asaf4LvV6Ps2fPYujQoTbLVasks04btVrFThw7sJ2UsY3sw3Yyp1Zx7ouiziln8507dzBz5kzcunULAHDlyhVs3brVFOC7f//+2LFjB44cOQIA2L17Nw4ePMhJMIiIqNi5evUqvvrqKzx8+BBAxmRQBw4cMN0TBwwYgLVr1+LixYsAgL/++guRkZHo1auX0+pMRESUW2q1GqdOncK3336LhIQEAMDOnTtx/vx5tGyZEQKqf//+WLp0KW7dugWDwYBffvkFLi4uaN++vTOrTkRETxCnjDh955138PDhQ3Tv3h2enp5ITk7G0KFDMXz4cABAUFAQJk2ahNdeew0qlQpqtRozZsyAv7+/M6pLRESUb6ZOnYqPPvoI7du3R4kSJaDT6fDaa6/hmWeeAQA888wzOH/+PIKDg+Hi4gIPDw/88MMP2SbLICIiKmq+/vprTJkyBS1btoSHhwdUKhU++eQT06TBI0aMQEREBLp16wZXV1eULVsWc+fOhZub4EkuIiKiPOSUjlM3Nzd8+eWX+PzzzxEbGwtfX1+oLGZXHz58OIKDgxETEwNfX19oNE4Nx0pERJQvfHx88NNPPyEtLQ3x8fHw9fXNNhHGuHHjMGbMGMTFxaFs2bLZ7plERERFUcWKFbFkyRKkpqYiMTEx25eCKpUK06ZNw6RJk5CQkICyZctanUSYiIgovzi1N1Kj0aBs2bI2011cXFC+fPkCrBEREZFzuLq6okyZMjbT3dzceE8kIqJiyc3NTTiK1MPDAx4eHgVYIyIiogzFaxinhMeTwGR9LcyTm5l3cpHV0bxK9RVMVpN1Ihvja0klQ4KsOPmTo1/sqhTqK5rQR1ac0EcwAZQocz7N2aM047tBMC+CqJkcPlcAxWtAMDdXzrdh7zUnmsNJYeIuWTj5mWDCNdHEUYC43rlpfyIiIiIiIiIqsvisHxEREREREREREZEFdpwSERERERERERERWWDHKREREREREREREZGF4hPjVJIf/2T9Wylbfk3KqBig0zFK9ZUEsRxVWeKYqiRV5m8ZKpUBKqUYkA6S7Qp6mS8bzh+ic0opnqggxqmwvvkZ49TBw2PWDFZinIrKzVU8V1EMVFH8U4WGEMZ6FcTEFcZOZWxUIiIiIiIioiKNI06JiIiIiIiIiIiILLDjlIiIiIiIiIiIiMgCO06JiIiIiIiIiIiILBSjGKcZ8T+NMUCzvhbnK4SBCIVxNMX1Fcc4fZymyixHpZKhkmXFthLFnpTzKZ5rvlGob9YwucbfxtfCrIUxxqkSBw9d1vPBGB9UVmWJFSooNzenizA+qnCjCo0oShbFTs3F+UBEREREREREhRtHnBIRERERERERERFZYMcpERERERERERERkQV2nBIRERERERERERFZYMcpERERERERERERkYViNDmUbD6Lj9nrXJQrnLTHdsFKc9E4SjT5E/B40iereaXsryXJvuYxGGyvZdALZj1SmMyqyE0sJZp/SOHYyGrb6cKcCueSs+c3M+6XrJYhZ574Ou/8qZTDp0shnAOOiIiIiIiIiAo3jjglIiIiIiIiIiIissCOUyIiIiIiIiIiIiIL7DglIiIiIiIiIiIislCMYpxm+YHFaxGl2IfOiGMqqLek0NWtFAPVFlEM04x02xs26AVtpBPEPwUgaQy2E50Ul9J4yLP+lrOeV7YonG+yo1ebQjvkJj6qiCh2atbLwqB+/Nt4NEXxXHN1XB2NcaqUz9HjKmokZwefJSIiIiIiIqJc4YhTIiIiIiIiIiIiIgvsOCUiIiIiIiIiIiKywI5TIiIiIiIiIiIiIgvFJ8apnOUHFq+F+cTBD4VxTBXyOkxlO+6npBA3UeVgjFOleK2yIAaqnC7ofxfEPwUAWRS0VSHuqvD45iqOppz9t+m1KJ+4WIM43Kvj7A1GmtOsdrahrH7823T5iWKc5oLk4DUnK10XonRBmiimsNK1SkRERERERESFG0ecEhEREREREREREVlgxykRERERERERERGRBXacEhEREREREREREVlgxykRERERERERERGRhWI0OZT0+Cfr37B/khub5dpME+VTKNfBSYYkwcRRAKASpMtZ9sU4cY0kyZAgK1RIPDmUcAIohcmhpDTbffeyRtyIkqhO+TQvj5yLCX8cnjApN19vKJz8omR7d9W4X7JGftw+ju6r0mRtjh5YpcmhXGxfNyqNYLI2wX5K+TUZGBEREREREREVCI44JSIiIiIiIiIiIrLAjlMiIiIiIiIiIiIiC+w4JSIiIiIiIiIiIrJQjGKcZsRrNMZszPra4Til9qQ7ShTHVBBcUqUQq1EShoh8nDdbjFOl2JIOxjGV0sXlQhSnVCkupTDGrKBOSsU6GH9WsWDB1xTibSqUK8qreH7bziyK5yp7PI77qc/cL72XHnqDPuMPpWNns+BcXHCia0oh5qokiGOqVgtinAr2UxRvmIiIiIiIiIgKP6d1nOp0OuzcuRNRUVHw8/NDly5d4ObmZpa+fft2REZGokqVKujcuTM0muLTz0tERGSUnJyMHTt2IDo6GpUrV0bHjh3N7nnJycn4559/EB0djVq1aqF9+/aQxN+UERERFQmPHj3Cjh07EBsbixo1aqBdu3Zm97i4uDj8+++/iI2NRf369dG8eXMn1paIiJ40TnlUPz4+HgMHDsTPP/+MqKgoLFy4EH369EF8fDwAIC0tDaNGjcLcuXNx69Yt/PDDD3j55Zeh1+udUV0iIqJ8c+fOHTz77LNYsWIFoqKi8M0332D48OHQ6XQAMj5QDhgwAMuWLcOtW7fw6aefYuLEiU6uNRERUe5dvHgRzzzzDDZt2oQbN25g6tSpeOONN0zpt27dQu/evbFhwwbcvHkT48ePxzfffOPEGhMR0ZPGKUM4V65cCb1ej7Vr18LFxQVpaWno0qULNm3ahGHDhmHDhg24f/8+NmzYgBIlSiA+Ph69evXC1q1b0atXL2dUmYiIKF/88ssvqFKlCpYsWQIAiI2NRYcOHbBv3z506tQJS5cuhZeXF1auXAmNRoOxY8eiR48eOHr0KJo2berk2hMRETnu+++/R4cOHfD1118DAF544QV069YN586dQ0BAAObNm4f69etj3rx5AIBhw4ZhwIABGDJkCKpUqeLMqhMR0RPCKR2nI0eORHBwMFxcXAAArq6uqFixImJiYgAAe/fuRdeuXVGiRAkAgJeXFzp16oRdu3bZ7jiVpcx4mZmPdRgkcfxMU75c7IijsTABSKK4iYJ4jEoxTkVxFfV62wOMDQoxTh2NY6oU41QSDSJOUwvzGtwKWQxJpXCuXum2syYK9jU348IV8wrimApinEqujw+cpHq8TDLGOM1N3FURR2MDK8Q4VWtsn4iiGKei69HFhSPkyT7vvfee2RMVPj4+KF26tOmeuGfPHjz77LOmR/f9/PzQokUL7N69mx2nRERUpH3xxRdmoWkqV64MjUZjdg+cPHmyKb127dqoVasW9u7dixEjRhR4fYmI6MnjlI7TEiVKmDpFAeDChQsIDw/HtGnTAAA3btxAs2bNzPJUrFgRO3bssFmmm6SCh0oND1VGB5TxtyJndZyqbK+gEqS5KpSrFsS8M0iPe9HcJY3Zb5VCe+kF6ZJakKbQeS2ey0qc16AWTfqVi8mhMjvDrJ1LsqjjWmlCJEG6qA2dE1BDoeM0y75YveYKW8epwrERXTdqSfAlh2CbbpKd70H0xPPy8jL7+8CBA3j48CHatGkDIOOeWLFiRbN1KlasiBs3btgs02DQQ6/XmzpkGepGjO2kjG1kH7aTdQYD24Os8/HxMft7/fr18Pb2RsOGDZGWloa7d+/m+B6oN8jQ6w3Q6zP+hzP+JuvYTsrYRvZhO1mnN+TXjONUUJw+29KNGzfwxhtvYNy4cahTpw6AjEkwsk4UBQDu7u5ISkqyWc6MmuYdrYvqts/7yhZDP1Z6xtlVKPQWNm3j7CoUCQu1HZ1dBaIiLzw8HBMnTsT06dPh5+cHAEhJSYGrq6vZem5ubkhOTrZZzvUrl8z+vnw+PO8rWwyxnZSxjezDdiLKuf379+OLL77ArFmz4Onpibi4OACw+rlQdA+8eOOu2d+nI6LyvrLFENtJGdvIPmwnKm6c2nEaFhaGt956C6NHj8bLL79sWu7h4YGUlBSzdVNSUuDp6WmzrI8u/4drKQnwUKmxqG57vHJ2D5Lt+Xa7ED6qr3Kxneaq8Piv6JFjg958xOmPlZ7BuMh/kCKnIzVNfCrok2ynS6mCEae6XDyqrzTiVPSofh6NOF3YtA1ePbrfdC7JGkFmhcfBRelSUhEbcVricdgBD5UaC7Ud8erFXY+vucI24lTpUX3B9ShKkwQjWau4eOPj8m2F2yXKaufOnfjwww8xZcoUPPfcc6bl7u7uSE1NNVs3JSUFHh4eNsuqWqMW3D08odfrcfl8OGrWCYRaNLL9Ccd2UsY2sg/bybqU5KRsX+gQZbV27Vp8/fXXmDVrFtq1awcg4/4HINs9MDk5GWXLlrVZlraKHzzdXaHXG3A6Igr1/StCrXbSP9RFANtJGdvIPmwn65JS0rJ9oUNFi9M6To8ePYrXX38dn376KZ599lmztCpVqiAyMtJs2Y0bN1C1alWb5aXKBrOO0mSDvnB3nAoe/1UZBHFKZdtxMgFAI9veZ72c/c0rRU5HspyOVIVH6vWCdFHnpySIjaqUV7HjVPQIQG46TmXzFZINeiRnPnInfFRfqWBRR6SwHcTFirfpeFZhx6mVa8vsmitsHacKx0atEsQ4lQXXqmy73FTBtUhkadu2bZg6dSp++OEHtGrVyizN1j2xXr16NstTqdRmnTZqtZqdOHZgOyljG9mH7WROKSQUPdmWL1+O+fPn4/fff0fdunVNy93c3ODn54fIyEgEBQWZlt+8eRNt29r+clqtksw6bdRqFTtx7MB2UsY2sg/byZxaEIqRigandJzev38fb775JmbOnIlnnsn+qHj79u3x888/49133zU9prFz50783//9n+1C5Sw/sHidG6LrXdSxpDDCTTQCTjTBk1KsRkcvSVlpIi1RB6ig/1LQP5yRrtCxKqJOtn1w9O6iSY8UClZZ+W0sTpQ3FzFOReeZrDSSNb/eh0UdkVlG3hrPSZVGhiozfouoEzNXVRKOKrV9smk04hNRLbjmxGmCGKeC0d9EWUVERGDy5MlYtGgRGjdunC29ffv22LJlC55//nloNBrcunULR44cwbhx45xQWyIiorxz+PBhzJkzB6tWrYK/v3+29A4dOiAkJMQ00CY8PBxXrlxBhw4dCrqqRET0hHJKx+lPP/0EV1dXnDx5EidPnjQt12q16Nu3L/r27Yu//voLw4cPR6tWrbBnzx4EBARY7WQlIiIqyr799luUL18eO3bsMJsEsUmTJujUqROef/55bN68Gc8//zyCgoLw999/o3fv3mjQoIETa01ERJR7X331FSpXrox169aZLe/YsSOaNm2KN954A8HBwXjllVdQvXp1hIaGYuzYsdkmjCIiIsovTuk4bdiwIby9vbMtNz7S5OrqiqVLl2Lr1q24efMm3nzzTXTv3h0qFYd7ExFR8dK2bVtotdpsy433PG9vb/z1118IDQ1FdHQ0pk6dik6dOhV0NYmIiPJc9+7drU4ALEkZj1Q99dRTCAkJwZYtWxAbG4tZs2ahRYsWBV1NIiJ6gjml4zTrpBe2uLq6om/fvgVQGyIiIucZMWKE4jolSpRAcHBwAdSGiIio4IwdO1ZxndKlS2P48OEFUBsiIqLsnDY5VJ6TsvzA4rWIUsxQQboojqlSLFJRPEbhLN4KsSNFqXKW4J5yZuPIsmS23PaGHUxTCnWZP6EwlSdqEua1c1kO15EE6bImF/VViq0q4mDMVpX6cfxOVeaOqVQGqDJnuRKd/6J2UDq/ReWKYpG6uYgnVRPGMRXUSSPI56HWCbdJRERERERERIUbn30nIiIiIiIiIiIislB8RpwSERERERERERE94c6dO4dFixZh7NixVudTyA2dTofVq1cjPDwcZcuWxeDBg1G5cuU83UZhwhGnRERERERERERExcDmzZvx/vvvY/Pmzbh//36elz9u3DisW7cOTZo0QWxsLAYPHoyHDx/m+XYKi+Iz4lQlQ1LLppikklp+HC9REKNQFMPUWI7NTYriIgryKeUVx4fMn6CgSu0Awf4I43OmKgX+FKQpVEmUVZNo+zuBdC/bbQ8AcmYbZ/0t23EuKcZBFeYVpCl9vZGbOonONVGM0yxpqsztq1QyVHLGa3GcXkF1BNcFII436qLR20xz14hjnLqobed1UdlOUwnq465ijFMiIiIiIiIqODdu3MCKFSuwdOlStG3bNs/L37dvHw4dOoRdu3bBx8cHgwYNQmBgIFJTU/N8W4VF8ek4JSIiIiIiIiIiekKVLVsWixcvhqurq811rl69ig0bNuDevXuoWrUqhg4dCm9vb7vK37lzJ9q2bQsfHx/TsiFDhuS22oUaH9UnIiIiIiIiIiIq4jw9PYWdpqdOncKAAQOg0WjwzDPPICoqCv369UNiYqJd5V+7dg01atTA5s2bMXnyZHz77be4du1aHtW+cOKIUyIiIiIiIiIiomJu3rx56NWrF9566y0AQMeOHdG3b1+sW7cOI0eOxLfffouoqCireb///nskJiZi8+bNSEtLQ7t27XDkyBH069cP69atQ/Xq1QtyVwoMO06JiIiIiIiIiIiKuStXruDu3bsYP368aVlCQgIuXboEAGjRogUePXpkM7+Hhwfq1KmDDz/8EADw7LPPIiIiAqtXr8akSZPyt/JOUmw6TiW1AZLGAEmVMfOMpDFAMmRMNJObyWhEE+SIJsARTfCktF2l+XxERFvNOrGU8bUkyZAgK7aDwcV2umywXWNZIRiEJNqs0jxYonRBmuaRuFI638zJgIyrZV1dlFVh4i7hZEteticS0icrXKbCycTEWR2dHCpd97hO6Sp1xu90NdIzj6dGk2Z7k6JJ1RTaUCO45lwFEzx5utiuDwC4q21PHuUqSNMITuCSUvENjk1ERERERERFj6enJ+rXr4+uXbualvXv3x/lypUDALRr106Yv3r16tke669UqRJiY2PzvK6FBWOcEhERERERERERFXNNmjTB3bt30b59e7Rv3x7t2rXDmTNnhHFRs+rRowd27dqF6OhoAEBiYiIOHz6MwMDA/Ky2UxWbEadERERERERERERPqu3bt2P16tUAAIPBgNmzZ2PJkiVo27YtRo0ahTfffBNjxozBwIEDERAQgHPnzkGlUmHUqFF2ld+8eXP0798fzz33HBo1aoQzZ86gVq1aGDx4sGLeyMhI/Pvvvxg0aBBKliwpXDcxMRG7d+/Go0ePUL9+fdSrV8+u+uUHdpwSEREREREREREVcTVq1EDfvn0BwPQbACpXrgwA8PX1xerVq3Hu3Dncvn0bgwcPRv369SEpxvl77MMPP0RwcDCuXr2KcePGISAgQDHPoUOHMGXKFERFRaFr167CjtO7d+9i2LBhKFeuHKpUqYJZs2Zh9OjReO211+yuY14qNh2narUBao0e6syDrVYboFZlxDwUHX9RnFJAHKs0v+KU5oZou1n3xRTjVCVDJctQq8WxJQ0a2/tqEGQ1lFKI+xlr+xSUZCe1ojHOZtbflsusZVOoriQIjCHKKikcG3GMU6W4q4I04b4+TlNlFqKSHsfKFeUVxTEVxTAFAFeN7XijJQRxTL1dU4Tluqttx5h1U9mOneoiSPOCeJtEREREREREeal69eqKs9urVCrUq1cvV6M4/f394e/vb9e6p06dwsyZMzFz5ky88MILiuvPnTsXWq0WP//8MyRJwsmTJzFixAj069cPFSpUcLjOjmKMUyIiIiIiIiIiIspzpUuXxooVK1CpUiW71t+5cyf69etnGgXboEEDVKlSBXv27MnPatrEjlMiIiIiIiIiIiLKc5UrV4aXl5dd6yYnJ+PevXum0AJZy7h+/Xp+VE9RsXlUn4ioOElLS0N6uu3QBIWNRqOxeyZGIiIiexW1+yHAeyIREeWNJ/EemJycDABwd3c3W+7u7m5KK2jFpuNU42KAK/RwyRzK6+Kqh142xjjNRQxIQZpKIW9hk7W2phinkgwJsjBeKwBoBGeK6DI2KMT9lNWiRIX2TbddeG6io2piMnZWo86onCZWA40+o8T08rZjYYrinyoRnYcuHoJtAkjX2W5ExbiromtDFMNXyvr6cbxcKfOYia4N0bmmUduOGQoA7sIYp6k207w0ttMAoIQg3U1le5sukiDGqZwCiHfHprS0NJwJ/w+G/2fvvuOauvo/gH9uEjYIKApuAffee4+696rFWttaO+x4qj5P7fNUW7ts++u01lFta23d1lG3bd2rDtxbnKCCC5UNuff3RyAlQs4NCZBc+Lxfr7yAe3Lv/ebkJoecnPM9iqf6nV1EVp4cflAkIqL8kpaWhgOHIuHl4ebsUPKEbSIRETkqLS0NRw4egMHTy9mh5ImjbaCHhwcAIDXV8jN6amqquaywFZmOUyKioiIjIwOy4onKgTPgaYhxdjiqUjLK4+r9V5GRkcEPiURElG8yMjLg5eGGqXN34OrNB84OxyaVy/rj3Rc6sE0kIiKHZGRkwODphcjv3kXCjSvODscmvuWqoPG4qQ61gT4+PihZsiRiYmJQu3Zt8/br16+jY8eO+RRp3rDjlIjIRXkaYuDtfsXZYRARETnV1ZsPcP7aXWeHQUREVOgSblzBgyvnnB1GoWrbti02bdqEbt26AQDOnj2LK1euoF27dk6Jhx2nREQuSoYCGeI0Gq5AhrbSlhARkcZIEiBpZE1btTxJREREeaT1lmXXrl04ceIEHj16BABYuHAh/Pz80LJlSzRu3BhRUVHYt28fRo4cCQB49dVXMWzYMLzxxhuoUqUKVq1aheeeew7ly5d3SvzsOCUiIiIiIiIiIqJ8l5SUhPv37wMARo0ahYyMDNy/f9+cxzQ5ORkxMf+kqKtcuTJ+//13/P7773jw4AHef/99p03TB4pQx6lOJ5tukvzP34rp96K0wJMaWbHvuwidTq0e7Bv1prY2jtFNcF6VxyKJQnJkkJ6Uy8/M3yW9YMEktToUXWsOXIdu7tYXLzJmiFbfsn0BqJxlSo7fsxYaAwC94Lh6QT15qCwO5WWwvlCWt6DMzy1FeFx/g/XV+TwkweJQgoWj/GT7F4ciIiIiIiIiKgq6d++O7t27Wy2vW7cu6tata7EtODgYL7zwQkGHZpMi03FKRFTUyIoMo6KBqfoaiJGIiDRMkrQzBV4rcRIRkSawCXQ+jSQLIiIiIiIiIiIiIio87DglIiIiIiIiIiIiekyRmaqvkxToM28ATL8XwZWe7c1hCljmes2RvlMlx6Zeb73ckeHYGYLjql2d4tnB9gclG0wxyZmxyW4K5Ky8nA/drJ+xVKrwuKJ8urbmE83vfQtKcqq71bJSfolWyzwFeUoBwNfNeh2XEOQx9dWLnxt/vfUcp9466/u6SdaTmHrB+jGJiIiIiIiIyPUVmY5TIqKiRoYCWQNfAGkhRiIi0jIdIGllopxW4iQiIi3QQTsti1bizKui+riIiIiIiIiIiIiI7MaOUyIiIiIiIiIiIqLHcKq+CntzRCoO5CJ1JI+pvfSO5MLUWU82qter7Wv9vIrK1SkLQpYE51Wt3swcp0pmbIq7AiXzZIrB+mPVCeoBsD8XqVoOWdG+bu4Zwn3T061XlK0xZf0uSdly5wr29XFLs1pWwt16nlIAKOmeZL3MzXru1JIG62UA4K+3flwfO3OcukP8WGyhQIEM8XXlChRO1SciooIkwbGk+oVJI2ESEZE2sAl0PqePOH348CHS0nLvSFEUBQkJCYUcERERkXPEx8cjPT33RdJkWWabSERERda9e/cgy7l/YWw0GpGYKP4inIiIqCA4reNUURTMmDEDzZs3x8aNG3OUL1++HK1atULLli3RunVrrF69uvCDJCIiKgTp6emYOnUqWrRogcjIyBzls2fPRrNmzdCyZUt06tQJO3fudEKURERE+S8pKQmvv/46WrVqhevXr1uUybKMadOmoUmTJmjRogV69uyJY8eOOSlSIiIqjpzScZqRkYHXXnsNt27dQlBQUI7y48eP48MPP8Rnn32G48eP46OPPsKUKVNw7tw5J0RLRERUcBISEvDss8+iRIkSuZb/+eef+OGHH/DTTz/h+PHjGDduHF5//XXcvn27kCMlIiLKX3FxcYiIiECVKlVyLV+0aBE2b96MlStX4siRI+jXrx/GjRuH5OTkwg2UiIiKLad0nKakpKBx48b48MMPodPlDGHVqlXo2rUr2rdvD51Oh06dOqFNmzYcdUpExYoRCoyKBm7MceqQO3fuYNiwYXjzzTdzLf/tt98wdOhQ1K9fHzqdDkOGDEFYWBg2bNhQyJESETmJpNPWjWwWExOD8ePH46mnnsq1fOXKlXjuuecQFhYGNzc3vPTSSwDAmRdEVKxIGrkVVU5ZHMrX1xfPPfec1fKzZ8+iU6dOFttq166d6/RFW9i7wBPg2CJPIvYuAKVz4LFkrwe9ZMofpNfJ0CuyQ3UkCR6LWv15BlpfQCflnqdwX8VdUCbqyBEsSAUA0GctDpV5LE8jFNm0CJAkWBzKKFhoCQAM7tYXEhJRVMIVLzol3tfLM/dcigCQmmb97SH79ZL1uyQpkDLrvUqpe1b39TZYXxwqULD4EwCUdn9kfV/BAlABgsWf1Mo9Jet1pBMs3GSQHV8cioqHKlWqWB1pAwDnzp1Dz549LbbVqlULZ8+eLeDIiIiIClajRo0AALdu3cpRJssyzp8/j5o1a5q3SZKEGjVq4MyZM+jevXuhxUlERMWXUzpO1cTHx8Pf399iW4kSJRAfH291Hw/o4SkZ4CmZHlLWT8DBjtMC6jeX7TxufnWcPl5PBVVHetHy9gCMgnJJJ94X9nZqq3WcZpZ7ZZ7fK1scks76OSWV4xoEvZiO1L/omlA7blYHeu7Htf72oLO4lvQWPwHAA25W93UXdGq7QdAbDsAg6C3XKxlWy3SKeGV6SVAuQXCNila8VzyE5ySy1f3793NtEy9fvmx1H1k2wmg03QCYf1LuWE/qWEe2YT3lTpZZH5R3iYmJSE9Pz5HKxt/fX/i50CgrMBplGI2m/9OyflLuWE/qWEe2YT3lzihzdp7WuWTHqSRJOVZUVBQFkqDj6e3S7S3+/jKkR4HEVtR8EdxT/U7OUtnZAZjMq91e/U6EGRW7FfxJrA/+FJdplAIFsgamwQtHeJPDrLWJuaW6yXL10gWLvy+ePVkgsRU1rCd1rCPbsJ7ymSSpT6NxFVqJUwOyPvspj02/kmVZ2AaevxZr8feJqJj8D64IYj2pYx3ZhvWUv9gEOp9LdpwGBQXh7t27Fttu376NMmXKWN1n2u2duJ7xEJ6SAV+G9MD4W5uQkjkCjVP1TR4fcfpFcE9MiN2IFCWjwOoo3agy4jTDennKfZURewU14lT/z4jTebXbY8zpnUi2Yaq+6ohTN8G+jow4FZxXLSa9znpMaYKp+tnP6SnpMaNiN7x6/Q+kKKZ6qlTyvtV9vQRT9Ut6iKfUB7s/tFoWYLC+r79evICAfwFM1ddnVEJgwmTheYlsUbp06VzbxNKlS1vdp3JYNXh6ecNoNOLi2ZOoWrMu9HqVUfzFGOtJHevINqyn3KUkJ+X4QodIjY+PD7y8vHK0gXfu3EGNGjWs7le9UjC8Pd1hNMo4ERWDeuHlodcz96w1rCd1rCPbsJ5yl5SSluMLHdIWl+w4bdiwIY4cOWKx7fDhw+jYsaPVfdKQgVSkm3MspiIdqcicuivoO3KkY9Tezk/A/g5QSWVkl6gDLvssc33m/dKldKTD+hRnW44rmqqfJprKDMCosz51K1ntA4eoKkSdiXpxHeoyy/WZU/TT3NOQljXFTFi/4uOmi5KVCorUOlV1guPqVerfIChPFqaJzVmYohiRnPllhZtbgtV9fdys5/4MEHSMAkCAm/Vyf739OU5L6KzH5CZZf33oBU+cpAQKz0lkqwYNGuDIkSMYMmQIANPImyNHjuD111+3uo9Op7fotNHr9ezEsQHrSR3ryDasJ0s6tfRLRLmQJAn169fHkSNH0LZtWwBAcnIyTp06hXHjxlndT6+TLDpt9HodO3FswHpSxzqyDevJkl6Q7o+0wSkdp0lJSbh3z7R4jCzLuH//PqKjo+Hl5YVSpUph6NChWLBgAX788Ud06dIFa9euxaVLl/Ddd985I1wiIqICk5CQYJGr7c6dO4iOjoaPjw8CAwMxcuRIjBw5Ek2aNEHjxo2xYMECAECPHkxJQ0RE2hYfH4+EhATcuXMHABAbGwu9Xg9/f3/4+flh1KhR+M9//oNatWohNDQU3377LcLCwtCyZUsnR05ERMWFUzpO9+zZg3feecf896xZszBr1iy0bdsWX3zxBSpWrIi5c+fiq6++wk8//YTQ0FD88MMPKFWqlDPCJSJyCiMUGDWQP1QLMbqyNWvWYPr06QCAgIAAvP/++wCAQYMG4a233kL9+vXx5ZdfYtasWfjqq69Qu3Zt/Pjjj/D09HRm2EREhUeSAEkjo5eKaoK3AvLTTz9hyZIlAExt4GuvvQYAeOmll/Dss8+ia9eu+O9//4vp06cjPj4eTZo0waxZs4RrXxARFSW6zJsWaCXOvHJKx2m3bt3QrZt4IZlmzZph0aJFhRQRERGRc0RERCAiIkJ4n65du6Jr166FFBEREVHhePPNN/Hmm28K7zNkyBBzuhoiIqLC5pI5Tu2hQIKiSOZ8m4oi5cvCTqI8pvm1UFNeyhxJj5H9uFm/S5Kimjc1L8d9nEFvPYepGp8A8YI+yUnugpis76cTLIhk2tn0eHSZB9HpZOgko+px1di7AJTaOe29lgBxXQT6Wc8L+jDpn5FuuV1L/qI8pm6CRZwECzwBgJ9gkScfnfVFp0QLPAHiPKZ+guO6CXLEylIarO9JRERERERERK6uyHScEhEVNQoUyBqYBq9oIEYiItIySUNT4LUSJxERaYGkoSZQK3HmVVFNQUBERERERERERERkN3acEhERERERERERET2GHadEREREREREREREjykWOU4dWeBJVG7vYj9q+4oWgHLknNkfi868AJICXQHmJ9TpC+7YeoP1hafEi0PZFpM+s470egV6xXk5HNUXeLJerlfZV2/nvhUCHph/94AbAKCc/0OkwrQIUznPeKv7BhoSrZYF6FUWh9JZX3TKU7CIk14SLwjmKVgcSrQAlLvguEZJcXhxKKMCGJ147dnK6PohEhGRlkk6000LtBInERFpRhFNHaoZbNmJiIiIiIiIiIiIHsOOUyIiIiIiIiIiIqLHFIup+kREWqQAgkQBroMz9YmIqEBJkjgPkyvRSpxERKQJbAKdr1h0nKrlMRUR5Ze0N0+p+r6u1w1hb27VgnwsPl7Wj51u1Dt8fF1m/kqdXoZeMf1eUOkmRW8wqjlOBeUGvbjbzU1vPU+sl1u61bJ2pS/+cw7FA0gDWgddQoaUCgAIMjyyuq8oj6m3LlUULtwl6/F6StbjdRPkMFUjCzLKGO0sIyIiIiIiIiLXx6n6RERERERERERERI8pFiNOiYi0yAgFOg1MhDdqIEYiItIwSdLOavVFdZ4iERE5hZR50wKtxJlXGvkPhIiIiIiIiIiIiKjwFJkRpxIUSJJizgcpSQokG0ZBqeePtH9f8XELf4RW9njzWk+ieB35VkGvs56DU1bUjmx9X73qvtZlPVJzjlOdDJ1SsEv0iKIV1ZGp3Ppz424Q5/b0FuQx9TGkWS0zKv985yJl1rVRkWDM/C5GdL346ZOtlonylAKAXnCtivKYuglyo6pJV+z7fkn9+iUiIiIiIiIiV8YRp0RERERERERERESPKTIjTomIihpZAYwaSB8qayBGIiLSMEnSTu5QrcRJRESaoJPEM6FdiVbizCuOOCUiIiIiIiIiIiJ6DDtOiYiIiIiIiIiIiB5TZKbqm2bw5L44VFFd4Cmvsseb9btOUqCD4tACT47Ug1GwgI7qcYXd/tYXA1JsXLTHkLk4lEEnw5APi0OJnjvRNWrQixc20guO66ayr5fB+mJMvm6pVsvkbFdM1u8yJPPvqbKb1X2NgsWWPHXixaFERAtHFRSj4JXj2KvKRIZoCTTXoYUYiYhIwySd6aYFWomTiIg0o4jOgNcMtuxEREREREREREREj2HHKREREREREREREdFj2HFKRERERERERERE9Jgik+NUp1Og1ynmfI96nQK9op7z0Bl5SgH7c5WqxWtr7gsp20/JhuOKyDbmDM3vfUUx6/TWy9TOmZUDNbdrSZynVOW5EZSL8pQadOIMkqLjuuvEOU4NkvVyL739+UZTFOtvLY9kL6tlJXQpwuN66wR5VwW5Ux0hymMqSquaPzlOJfH5XYSsgRiJiEjLJA3lDmWbSERE+UeCaU0fLdBImHmmlf9AiIiIiIiIiIiIiAoNO06JiIiIiIiIiIiIHlNkpuoTERU1sgI4KZtInsgqMd64cQMxMTEIDg5GpUqVrN7v+PHj8PLyQrVq1Sy2R0dHIzo6GhUrVkT58uXzI2QiItISnWS6aYFW4iQiIk3ISq+oBVqJM6+KTMepTlLMN/PfogSE+cDePKWA/TlF1S5EW48rZdaNBMWmfUR5QUV7O5LDtKDqV2/jMdwzQ3c3ZEBWMkwx2XlOtXJHcqfae1xAnBdU9NwZs+0nZf5uVHQwZg5iFx33kdHTatlDnfUyAPCUrOdd1UnWc8Gq5j8V7Ksv4PeRosxoNOLtt9/G1q1bUbVqVVy8eBHNmzfHt99+C73e8pV49uxZjBgxAgMGDMBHH31k3v7ee+9hzZo1qFatGi5cuIDhw4dj0qRJhf1QiIiIiIiIqBgqMh2nRETkWlasWIF9+/Zh8+bNKFWqFOLi4tCrVy9s2LABffv2Nd9PlmW88847qFmzpsX+f/31FzZu3Ii1a9eiQoUKuHLlCgYPHowOHTqgVatWhf1wiIiIiIiIqJhhjlMiIioQjRs3xrfffotSpUoBAMqUKYOqVavi6tWrFvdbsGABypQpg+bNm1ts37JlC7p3744KFSoAAKpUqYLOnTtj48aNhfMAiIiIiIiIqFjjiFMiIhdlSiDg+plirCU6eDxX6d27d3H+/Hm89tpr5m03btzA999/jxUrVuCXX36xuH9UVBT69OljsS0sLAx79uzJl7iJiEgjJJ3ppgVaiZOIiDQhe0pKV6eVOPOKLTsRERW4xMREvP766+jUqRPatGlj3v7ee+/h2WefRbly5XLs8+jRI3h5eVls8/HxwaNHjwo8XiIiIiIiIqJiP+LUGQs8AY4tMlQQ1BZxKqgFoBwhXBTJzv2ylxsyx9EZJBlumYsHFdQiTs4iC2oq2ehmtSzB6GH+3U1xBwAkGj2QLpmO5yYZre7rJnlYLYs3elstAwAfXarVMj8pRbgvOU9cXBxeeuklVKlSBZ988ol5+/r163Hz5k08++yzue7n7u6OjIwMi23p6elwd3cv0HiJiIiIiIiIABfuOI2NjcUvv/yC6OhoVKpUCaNGjUJQUJCzwyIiKjRGSFA0MVXfeowxMTEYOXIkevbsiX//+9+QMjvXU1NT8fHHH2PEiBHYu3ev+b6JiYnYs2cP2rRpg5CQEMTGxloc7+bNmyhbtmzBPRgXdeXKFSxatAhxcXGoWrUqRo8eDV9fX2eHRURUOCQAkuu3hwC0kGFHc06fPo3ly5cjPj4e9erVw8iRI/klKhEVK2xanMslp+rHx8dj2LBhuH79Otq3b4/Lly9j+PDhSExMdHZoRERko5SUFDz33HMYMmQI/vOf/5g7TQHTyNEGDRrg9OnTWLJkCZYsWYKzZ8/i8uXLWLZsGQCgRYsW2L17NxTFNFo7IyMDu3fvRqtWrZzyeJzl+vXrGDJkCJKTk9G+fXscPHgQzzzzDIxG6yO7iYiIioITJ07gySefhKenJ9q0aYP169fj9ddfd3ZYRERUjLjkiNNly5ahdOnS+PrrryFJEvr3748BAwZg1apVGDlypLPDIyIiG8ybNw/JyckIDw/Hpk2bzNtDQkLQsGFDzJw50+L+n376KR4+fIiPPvoIADB06FD88ssvmDhxIjp37oyNGzdCURQMHDiwUB+Hs82fPx8tWrTABx98AADo1asXunTpgm3btqFr165Ojo6IiKjgzJ49G0OHDsVbb70FAOjcuTM6dOiAEydOoF69ek6OjoiIigOX7Dg9ePAg2rRpYx6dpNfr0bp1axw4cMBqx6kEBZKkmHNQSpICSZiZ0zb25tF05LiO5MIU5RvNXqZk1q0CCbKiXlMFlcfUGXli9Tpra4CbGDLL3TLP4KY3QoZpZJcuH64prchQrA9Iz57/NANugAQky25Iz6wfX73e6r7pivWyJNl6/lMASBSUe0rpVsv0xeh5cyWyLKNatWpYvny5xfamTZuiYcOGOe4fFhZmMbPA398fS5cuxY8//oh169YhNDQU77//Pjw9PQs6dJdy4MABjBgxwvy3p6cnmjZtir///psdp0REVKQdPHjQIj96yZIlUadOHRw4cIAdp0REVChcsuP01q1b6Nixo8W24OBgHD582Oo+7pIenpIBHpLpIWX9dFRBdZyKuiod6ZwT5RpUsk2T9ch86j1gACTx4k9qx3VEQXWciupXD5WO08xyizrK5JK5LQTUusTdBY9IlDnKLVupG9wsfgKAQbG+t14RPDeKeOqxpAg6zGTRvirXmSS6Jqzvq4iOq9IJbAtFkZy2+FpeKFZizOtUuqFDh+bYFhISgv/+9792xVVUxMbGIjg42GJbcHAwbty4YXUfWTbCaDSap/NzWr8Y60kd68g2rKfcycI22hY6QNLKf2FaidP1JScn48GDBznawDJlyuDmzZtW9zPKCoxGGUaj6f+7rJ+UO9aTOtaRbVhPuTPKjg3ikaCdHKdaiTOvXLLjNC0tDQaDZWgGgwHp6dZHlE0I6Gzx97SSfQsktqLmw5L9nB2Cy3unxGBnh6AJo6Qx//yRYf1+wjIiysGeNvHqpQsWf188e7JAYitqWE/qWEe2YT0ROS4tLQ2AafZhdm5ubuay3Jy/Zrmw5ImomPwPrghiPaljHdmG9URFjUt2nPr5+SEpKcliW0JCAkqUKJHjvrJs+jbjl0cHEGdMgLtkwGv+7fHtg51IUxzvodEJuswLasSp5MAoTGsjvwBYrM7tBj3G+XfAdw92IB1GG6bq2x2SkKh+1dg74lSnMlVfn1n/7tBjjG8XzEv4C2nmqfraonYtGXTWR4C4662/fvzcUv85BgzoIw3EOmUVMjJ7RQMMSdZ2hbfOemePrz5FFC5KGR5ZLSshJVstU5uqL6on0b7C0eHGckDiq+b3KCJ7+fr65lgcMSEhAX5+fjnum3W9hZSvCA8PTxhlGdFXolChSjj0Oq29gxUe1pM61pFtWE+5S01Nwa2Y62wTKU+8vb2h0+ly/VxYtmzZHPfPur4qhZSEp7sbZFnGxejbqFqhNHR8PVrFelLHOrIN6yl3KWnpuHbrHttADXPJjtOqVasiKirKYtvFixdRtWrVHPdNTTV14Dzt19xi+2v+7QsuwCJknH8HZ4fg8sb4dnF2CJrQR8q2YI9oRp6ozHqfqmalpqbC19fXrn2NkCBpYMKDooEYtaxq1aq4dOmSxbaLFy+iQ4ec799ZbeKtmOsW26OvROW4L+XEelLHOrIN6yl3dreJkmS6aYFW4tQANzc3VK5cGVFRUWjcuLF5+4ULF9CzZ88c989qA6/dumex/WL07YINtIhgPaljHdmG9ZQ7e9tANoHO55Idp927d8ekSZPw8ssvo3z58oiKisK2bdswd+7cHPf19/dHlSpV4OHhwW81iMhlyLKM1NRU+Pv7OzsU0rju3btj3rx5GDlyJAICAnD48GEcP34c06ZNy3FftolE5IrYJpK9unfvjiVLlqBfv37w8PDAxo0bER8fn2M9DIBtIBG5JraB2ueSHaedOnVC9+7dMWDAANSsWROnT5/GyJEj0bRp0xz3NRgMKFWqlBOiJCISs3ekKVF2Q4cOxa5du9CrVy+EhYXh1KlTeOutt1C5cuUc92WbSESuim0i2WPs2LE4cOAAnnjiCZQrVw7nzp3Dhx9+iICAgBz3ZRtIRK6KbaC2uWTHKQC8//77GD16NK5fv47Q0FBUqlTJ2SEREREVOoPBgJkzZ+Ls2bOIjY1FjRo1EBIS4uywiIiICpyPjw8WLVqEEydOID4+HnXr1kXJkiWdHRYRERUjLttxCgBhYWEICwtzdhhERE4hQ4I2liSTNBGl1tWsWRM1a9Z0dhhERIVPkgBJIy1NUU3w5kSSJKF+/frODoOIyCkkOLYweWHSSpx55dIdp3kVGRmJrVu3QpZldOrUCc2aNXN2SC4hISEBM2fORLNmzdCpUyeLsrS0NKxevRoXLlxA6dKlMWjQIAQFBTkpUue5fPkyNm7ciEePHqFatWro27cv3NzczOVXrlzBunXr8PDhQzRs2BA9e/aEVMz+MTYajdiwYQNOnz4NLy8vtG/fHg0bNrS4zx9//IFDhw7B29sbffr0QXh4uHOCJSLs3r0be/bsgZubG3r06IHatWs7OySXcPv2bcyZMwc9e/ZEkyZNLMoSExOxcuVKXL16FRUqVMDgwYPh5+fnpEid59SpU9i6dSuSk5NRt25d9OjRwyJf4JkzZ7Bp0yakpqaiVatWuS5UVtSlpqbi999/x8WLF1GiRAl069YN1atXN5cbjUasXbsWp06dQmBgIAYMGIBy5co5MWKi4mXz5s04fPgwfH190bdvX4SGhjo7JJdw+fJl/Pzzz3j66adz/J9+9+5drFy5ErGxsahatSoGDRoEd3d3J0XqPAcPHsTu3bshyzKaNGmSI5/u4cOHsXXrViiKgs6dO+eaTrCoS0hIwJo1a3D16lWUKlUKvXr1QsWKFc3lqampWLVqFS5evIjg4GAMHjyYI8VJ0zTy1a26NWvW4Pnnn4csy3Bzc8OLL76I3377zdlhOd3ly5cRERGBlStX4uTJkxZliqJgzJgxWLRoEYKCgnDs2DEMHDgQd+7ccVK0zrFnzx4MGDAAsbGxCAwMxNy5czFmzBgoigIAOH36NAYMGIAbN24gICAAn3/+Od5//30nR124jEYjxo4di7lz5yIwMBAJCQkYNWoUVqxYYb7Pxx9/jA8//BB+fn64e/cuBg0ahOPHjzsxaqLi6/vvv8eECRPg4eGB1NRUjBgxAjt37nR2WE53/PhxPPPMM1i5ciUuXbpkUZaSkoInn3wSmzZtQpkyZbBt2zYMHz4cycnJTorWOVavXo2RI0ciMTERvr6++PTTTzFp0iRz+a5duzB8+HAkJibCx8cHb7/9NubMmePEiAtfcnIynnzySaxcuRJBQUGIjY3FoEGDLF5jb775JubMmYNSpUohKioKAwYMwPXr150YNVHx8cEHH+CTTz5BiRIlcOfOHQwcOBCnTp1ydlhOt2PHDowbNw5LlixBbGysRVlWPR0+fBhlypTBsmXL8Pzzz5s/DxUX33//PcaNGwfAlCrprbfewpdffmkuX7lyJcaMGQNJkmAwGPDCCy9g9erVTorWOe7du4d+/fph+/btKFOmDC5cuIDevXubX2NGoxGjR4/G8uXLUaZMGRw+fBiDBw/G/fv3nRw5kf2KxIhTRVHwxRdfYPLkyRg0aBAAoGrVqvjkk08wcODAYr2q4oQJEzB58mR88803Ocp27tyJc+fO4c8//4Sfnx8URcHo0aPx888/Y8KECU6I1jlmz56Np59+GhMnTgQAdOvWDT169MClS5cQHh6O6dOnY9CgQZgyZQoA0+qeffv2xXPPPWfxzVpRdvz4cURGRmLHjh0oUaIEANM/EytXrsSQIUNw8+ZN/Prrr1i1ahVq1KgBAPDy8sL06dMxb948Z4auaTIkQNHCyGZO1XcliYmJmDlzJmbOnInWrVsDAIKCgvDll1+iffv2To7OedLS0vDWW29h+vTpeOWVV3KUr1mzBmlpafjpp5/g7u6OZ599FgMGDMDKlSsRERHhhIid47vvvsOECRMwcuRIAECjRo0wevRoTJkyBb6+vvjyyy/x6quvYuzYsQCApk2b4qWXXkJERESxWfhgx44duH//PrZs2WIejZWYmIjVq1ejffv2OH78OLZv344///wTZcqUAQC88cYbmDNnDj788ENnhq5dkqSdKfBaibOIio6OxuLFi7F27VrziEp3d3d8++23mD17tpOjc57o6Gh88803+PHHH9G5c+cc5QsWLEDlypUxa9YsSJKEp556Cl27dsWOHTtyjLgsqtLS0jBnzhx8+umn6Nq1KwCgcuXK+PDDDzF+/HjIsowvv/wS7733Hvr37w/AlFrwiy++QP/+/YvNbMS1a9ciICAA33//vfkxR0REYN26dahTpw7++usvXL9+HZs3b4aPjw8URcFTTz2FhQsX4tVXX3Vy9NokSYBOI5dXUX0ZFInPulFRUYiNjUWXLl3M27p06YK7d+/i3LlzTozM+ebMmWN1+sDevXvRvHlz8zRESZLQpUsX7NmzpzBDdLpffvkF48ePN/9dunRpAKYPQYqi4O+//7a4tsLDw1GlShXs37+/0GN1lkaNGuHIkSPmTlPANN01ODgYAPD333+jXLly5k5TAOjatSv2798Po9FY6PESFWdHjhyBJElo1aqVeVvXrl1x5swZ3Lt3z4mROZder8evv/6KatWq5Vq+d+9etGvXztwR5ubmhg4dOhS7NnHLli146qmnzH9ntYlJSUmIj4/H6dOnLdrEli1bwmAwIDIystBjdZYePXpg+/bt5mtFURSLNnHv3r2oX7++udMUML0G9+7d65R4iYqT/fv3o2LFihbT0Lt06YJ9+/ZBlmUnRuZcAQEB+PXXX60uLrl371506dLF3BHm6+uLFi1aFKs20N3dHQcPHrRo44KCgpCSkgKj0Yjz58/j9u3bOfoc4uLicOHCBWeE7BRZM3eyrpW0tDTEx8dbtIEtWrSAj48PAFMfQ+fOnYvVtURFT5EYcRoTEwNvb2/4+/ubt2X9HRMTg1q1ajkxOufK+sCTmxs3bqBs2bIW28qWLYuYmJiCDsvlZB+VPH/+fISGhqJOnTq4f/8+kpKScq2n6Ojowg7T6a5du4Y5c+bg8uXLCAkJwX//+18Aptfg43UUEhKC9PR03L59myuAExWiGzduICQkxGLkQ9brMyYmptjmmNLr9ShVqpTV8hs3bqBBgwYW28qWLVvs/tGXJMni2pk/fz6aN2+OMmXK4PTp0wBg8X4vSRJCQkKK5f8OJ06cwKJFixAVFYUaNWqYRzJb+//qxo0bkGW5WM+EIipoN27cyJFPuGzZskhJScHdu3eFn42KMrUZAdbet65du1aQYbmc7O/PiqJgwYIF6NmzJ/R6PW7cuIESJUpY1KWfnx98fX0RExNjkee6ONi7dy9+//13XLhwAe3btzd/6Xrjxg2LwTRA8e1joKKjSHScpqWl5Zq42s3NDampqU6ISBtSU1MtFkACTN+0paWlOSki51u6dCl+/fVX/Pzzz9Dr9ebr5/F6cnNzK5b15O7ujvLlyyMlJQUnTpzA5cuXUbp06Vxfg1l/8zVoPxkSFA2sTShpIMbixNp7e1YZ5c5avRXnOpsxYwb27NmDRYsWAYDVNrG41pO3tzfKly+PBw8e4OjRo4iJiUHNmjWtXkuKoiA9PR0eHh5OiljDJJ3ppgVaibOIYhtoH7aBloxGI6ZOnYqbN2/i//7v/wDkXkdA8a0nX19flCtXDrGxsYiMjMTt27dRvnx5XksFQIJ2VqvXSpx5VSQ6Tr29vZGUlJRje1JSknmIOOXk7e2dY9GLrMUeihtFUfDll19i7dq1WLBggfkbQ29vbwDIUU/F9doKCQkxj6iZP38+JkyYgF27duX6Gsz6uzjWE5Ez5fbenvV6LC45KO1hrd6K43tYeno6pkyZguPHj2PhwoXmUUjZ28TsH4qKaz2Fh4eb87V9+umneOedd7BixQp4e3sjPj7e4r6JiYlwc3NjpylRARO1gcXxfcpWbAP/kZiYiPHjx+Phw4dYsGCBeVZrbnUEFN96ql+/PurXrw/AtCDitGnTMGPGDF5LVCQVia9EQ0NDkZaWhps3b5q3xcXFITExEWFhYU6MzLWFhobi6tWrFtsuX76M0NBQJ0XkPB999BG2b9+OpUuXWkyz8Pf3R8mSJXOtp+J0bcXExODw4cMW2+rVq2d+nVm7lvz9/REUFFSYoRIVe6Ghobh586bFqPhLly7BYDCgUqVKTozMtYWGhuLKlSsW24rbez0AyLKMCRMm4Pr161i8eLHFlNdKlSrBYDBY1FNqaipiYmKKVT1dunQJJ06csNhWr149c73w/ysi57H2Xh4YGIjAwEDnBKUBbANNUlJS8OKLL8Ld3R3z58+3uGbCwsKQlJSEuLg487abN28iJSWlWNXT2bNncf78eYttj7eBvJaoqCkSHaflypVD7dq1sXTpUvO2hQsXolatWvyQKNClSxccOHAAly5dAgAkJCTg999/R48ePZwcWeFavXo1tm7dip9//tmc1Dq7rl27Yvny5VAUBQDw559/IjExEW3bti3sUJ3mxIkTGDNmDGJjY83b9uzZg/Lly8PHxwetWrVCcnIy/vzzTwCmEbyLFy/GE0884ayQiYqt+vXrIyAgACtXrjRvW7x4Mdq1a2ceMUg5de3aFVu3bsXt27cBmBbA27JlC7p37+7kyArX3LlzER0djblz51osCAgAXl5eaN26NZYtW2betnz5cpQqVQoNGzYs5EidZ+fOnRg3bhwePXpk3rZ7927zF6+dOnXCuXPncPz4cQCmlFIrVqwodtcSkTO0adMGjx49wvbt2wGYvgxasmQJX38qunbtinXr1plH5168eBF///13sau3Tz/9FB4eHvjqq69yzBCoWLEiqlevbtEGLly4EHXr1kX58uULO1SnWbNmDSZOnGj+gl5RFOzZs8fcBnbt2hX79u3D9evXAQAPHz7E2rVri921REWLpGT1Bmnc0aNH8cILL5iHi584cQJz587NsdBDcXL9+nV88803AEzJm0uVKoUaNWqgTJky+M9//gMA+OSTT7B69Wq0bNkSp0+fRrly5fD999/nmjO2qOrQoQMCAgJyrLQ8ePBgtGrVCnFxcYiIiICfnx/Kly+PvXv34p133sHAgQOdFHHhMxqNePPNN/H333+jTZs2uHv3Lk6ePImvvvoK7du3BwD89ttv+Oijj9C6dWvcvHkTjx49wsKFC4ttEn5HJCUl4cyZM0j2nwTFcNnZ4aiSMkLh9eAT1KpVix1zLmLr1q2YOHEimjdvjkePHuHq1atYuHAhKleu7OzQnObkyZOYP38+AOCvv/5C9erVzR+Cxo4dC0VRMH78eBw+fBhNmjRBZGQkmjZtis8//9xisaSiLDk5Ga1bt0aNGjVQoUIFi7Lnn38etWrVQlRUFEaNGoUqVarAz88PBw8etGgLioOUlBSMGTMG165dQ7NmzRAdHY3r169jzpw5qFevHgBg9uzZ+OGHH9C6dWtcvHgRXl5e+PnnnzlVMY+y2sPnvovE+ZsJzg7HJtXL+uLHcY3ZJjrRsmXL8Mknn6BNmzaIiYlBYmIiFi1aJFwgsKjbtWsX1qxZAwBYt24dWrZsiaCgILRo0QJDhw5Famoqnn/+edy+fRu1atXCvn37MGzYMEyYMMHJkReeGzduoGvXrmjevHmOGXPjx49HuXLlcPjwYbz44oto0KABFEXByZMn8cMPP5jf+4uD+/fvY9SoUUhNTUX9+vVx4cIFPHr0CD///DMqVqwIAJg6dSo2b96MFi1a4Pjx4wgLC8OsWbNgMBSJTJGFJqsNPPvhKCRfO+fscGziVakGar6zoMi1gUWm4xQA7t27h7179wIAWrduXWxXDs5y7949bN68Ocd2f39/9OrVy/z3iRMncP78eZQtWxYtW7Ysdqu9Ll++HBkZGTm2N2vWDFWrVgVg+jC5Z88ePHr0CE2aNCm2I5lPnTqFixcvwsfHB40bN87xGrt69SoiIyPh5+eHtm3bwtPT00mRahs7Tik/3Lp1C/v374eHhwfatWtX7PObRkdHY9euXTm2h4SEoFOnTua/Dx06hCtXrqBKlSpo2rRpYYbodKmpqRYjlbPr0KGDedr+o0ePsHv3bqSlpaFly5a5ztYoDiIjI3Ht2jUEBASgadOmOV5j586dw6lTp1CqVCm0bt0610VFSIwdp2SvK1eu4MiRI/yfNNO5c+cQGRmZY3t4eDiaN28OwDRQYt++fYiNjUXNmjVRp06dwg7Tqe7fv49NmzblWtajRw/ztP27d+9i7969kCQJbdq0KZYpIGRZxoEDB3Dz5k0EBQWhefPmOUboHjt2DBcvXkS5cuXQokWLYtfHkB/Yceo6ilTHKRFRUcCOUyIiInacEhFR8cWOU9fBsdJERC5KgQRZA6modSge05iJiMhJJAmQXL89BGCKlYiIKJ9ImTct0EqceaWR/0CIiIiIiIiIiIiICg87TomIiIiIiIiIiIgew45TIiIiIiIiIiIioscwxykRkYsyQoKsgUwxigZiJCIiDZMk7eQO1UqcRESkCToAOo00LUV1ZCY7TomIiIiIiArYn3/+iR9//BG3b99GtWrV8O9//xuhoaFW779w4UL8/vvviI+PR6VKlTBmzBi0aNGiECMmIiKiotohTGSXjIwMKIri7DCIiIicLiMjw9khEBUZhw8fxvjx4zFw4EB89913CAkJwejRo5GcnJzr/RcuXIg5c+bgnXfewbJly9C7d2+MGTMGly9fLuTIiYofRVHYBhKRGTtOiTIlJiaiTp06OHDggLNDIQIAyIoORg3cZIVNCVFRc/78edSpUwfR0dHODoUIkHTauuVi4cKFGDx4MIYOHYrq1atj8uTJMBgM2Lx5c673P3ToENq3b4969erB398fAwYMgI+PD44fP16QNU1EAHbt2oU6deqw85RchqSRW1HFT7tEREREREQF6NixY2jQoIH5b0mS0LBhQxw7dizX+7do0QIHDx7EvXv3AABHjhxBcnIyGjZsWBjhEhERUSbmOCUiIiIiIipAd+/eRcmSJS22BQYGIi4uLtf7P/nkk4iKikK7du3g6+uLtLQ0fPbZZ6hcuXJhhEtERESZ2HFKRERERERUgGRZhiRZTmSUJAmyLOd6/99++w1//fUXfv31V4SGhmL37t2YMmUKqlativDw8MIImYiIiMCp+kRELksGIEPSwI2IiKgASZLz85bafMs9y1tAQAAePnxose3BgwcIDAzM9f7z58/HM888g0aNGiEgIAB9+vRBgwYNsGLFinyvXiIicl2SpK1bUcSOUyIiIiIiogJUq1YtnD592mLbyZMnUbt27Vzvn5aWBg8PD4ttBoMB6enpBRYjERER5cSOUyIiIiIiogI0aNAgrFixAmfPnoWiKFi4cCHi4uLQs2fPXO/fsmVLrF69GomJiQCAq1evYv/+/WjZsmVhhk1ERFTsMccpEZGLkqGDUQPfb0kaiJGIiLRMS/P/co+ze/fuuHDhAp588klIkoSAgADMmDEDAQEBAIAjR47gjTfewNq1a+Hv74+JEydi6tSp6Ny5s3ma/9ixY9G1a9dCfCxERORsEqy1LK5HK3HmFTtOiYiIiIiICtirr76KsWPHIiEhAYGBgRaLRdWpUwfLli2Dn58fAMDPzw+ff/45jEYjHj16ZO5gJSIiosLFjlOiTJIkQa/X51jxlIiIqLhhm0hUMNzd3VGyZMlct4eEhOTYrtfr2WlKVMh0Oh3bQCIyY8cpUSZvb+8cSfuJiIiKo2rVqrFNJCKiYqlt27ZsA4nIjB2nREQuSlZ0MCqunz9Up4EYiYhIwySd6aYFWomTiIg0QdJQmm+txJlXbNmJiIiIiIiIiIiIHsOOUyIiIiIiIiIiIqLHcKo+EZGLkiFB1sD3WzKK6JwMIiJyDRK0M/9PI2ESEZE2SNDOiMei2gRqpf6JiIiIiIiIiIiICg07TomIiIiIiIiIiIgew6n6REQuSlYkGBXXn/Cg10CMRESkYZJOO6vVayVOIiLSBEnSULYajcSZV2zZiYiIiIiIiIiIiB7DjlMiIiIiIiIiIiKix7DjlIiIiIiIiIiIiOgxzHFKZlOmTEGdOnUwfPhwZ4eSZ1OmTEFaWlqO7dWrV8dzzz3n0LEkSYK/vz9CQ0PRu3dv+Pr6OhwvkS2M0MGoge+3tBAjad/s2bORnJyMN99809mh5NmsWbNw9erVHNt9fX3xzjvv2HQMWZYxZcoU1K5dG0899ZRFmaIoePfdd1GlSpVc27wPP/wQwcHBeOGFF+x7ACS0dOlSREdHY8KECc4OpehigjeiXN24cQPTp0/HgAED0LJlS2eHkydZsefmiSeeQOfOne067qVLl7Bjxw5ER0fDaDQiKCgIzZo1Q7NmzaDT8X9We+zevRvr1q3D+++/D3d3d2eHU+xImTct0EqcecV3DjJbu3YtDh486Oww8sxoNGLp0qWIiopCRkaGxU2W5TwfL6seso6Rnp6Oc+fO4eOPP8YTTzyBo0eP5v+DICIioR07duCvv/5ydhh22bBhA44cOeJQG6XT6XD58mV8//33OcrOnTuHpUuXYvbs2TmOef36dfzyyy9ITk626Typqal4++237Wo/C5szYs3tnLdu3cq1Y5yIqKA9ePAAq1atwuXLl50dSp5FR0dj1apVuH//fr58hktLS8PkyZPRq1cvzJ07F1euXMGNGzfw+++/Y9SoURg2bBhiYmIK4JEUrM8//xzXrl1z6jnj4+Nx/vx5TfxvQFQQOOKUNO/hw4cAgIiICAwYMCBfjtmoUSN8/vnnFttu3bqFwYMH491338WaNWvy5TxERFT0PXz4ED169MDbb7/t0HE6dOiAL774AlFRUQgPDzdv3717NypXrozr16/j5MmTqF+/vrlsz5495n1tceLECaxcuRIffPCBy4/McUasuZ3zjTfeKJRzExEVJVmf4d555x1UrFjR4eO9++67WL16NSZNmoSnn34aer3eXLZ371688cYbeP3117Fs2TKLMleWlJSEH3/8EV27dkWlSpWcds4+ffqgT58+hXJ+IlfEjlPKk5SUFGzZsgVnzpxBUlISypUrh759+6JcuXKIjY3FV199hf79+6NVq1YW+61btw779+/Hu+++Czc3NwBAZGQktm3bhgcPHqBs2bLo06ePudG8fv06vvvuO4wbNw779+/HyZMnMXnyZBgMOS/ZrEbXz89PGPuMGTOQnp5u9zTPkJAQdO/eHQsXLkRCQgLu378vjFH0+ADg7t27WLt2La5duwadToeaNWuiV69e8Pb2tvk+V65cwezZszFmzBhUrVrVvN/atWtx+PBhvPfee6p1qRYnOY8CCbLi2h0XgClOIlegKAq2b9+Ow4cP4+HDhyhdujS6dOmC2rVrw2g0YurUqWjYsCEGDRpksd/hw4exfPlyvP766yhXrhwA01S/DRs2IDY2FiVLlkS3bt1Qt25dAEBGRgbeeecdDBw4ECkpKdi2bRteeOEFlC9fPte4Hj58qNpG/fbbbzh48CA+/vhjq52A7du3xxdffIE9e/ZYdJzu2bMHrVu3xuHDh7F7926LjtO9e/ciICAA9erVMz+uP//8Ezdu3ICnpydq166N3r17Q6/XY+PGjViwYAEA4H//+x/q16+PiIgIAEBycjLWrl2L06dPQ6/Xo0GDBujVq5e5LVm8eDHu37+PQYMG4eeff0bdunXRu3dvq4/3wIED2LNnD+7fv4/AwEB06tQJDRs2tLiPPbEuXLgQcXFxOdr6SZMmoWvXrujataswVnvOmdtU/UePHuH333/H+fPnze13nz594OPjAwBYtmwZ7t69i+effx6//fYbzp49i5IlS6Jfv34IDQ21Wm/FlQQJkkamwEtsE8kF3blzB5s2bcKVK1egKArCwsIwYMAA+Pj4YP/+/Vi9ejX+9a9/ISQkxGK/GTNmQK/X4+WXXwZgShvzxx9/4NChQ0hLS0O1atUwYMAAcyqzXbt2YdOmTfjf//6H+fPnw2AwYOzYsbnGZMtnuPT0dEyePBktWrTAwIEDrd7vzJkzWLlyJUaPHo3Ro0fnKG/dujV++eUX+Pr6WnSaiuoly8WLF7Fp0ybExcXBx8cHzZo1Q8eOHVW/sLNlP9H5T5w4gZkzZ8JoNGLmzJkIDw/HW2+9he3bt2PTpk344IMPzJ+rAdNz5evri9GjRwufB3vOmdtU/fT0dGzevBlHjx5FWloaKlWqhL59+yI4OBiA6X+TDRs24KOPPsKWLVuwf/9+eHp6olOnTmjWrJmw7siSTjLdtEArceaV638iJ5dhNBoRERGBTz75BMnJyfDy8sLvv/+OHj164PTp0wgODsaRI0cwb968HPvOnDkTd+/eNb+5f/PNNxgxYgTOnz+PEiVKYNu2bejTpw/2798PwNSQrlq1CvPnz8cXX3yBc+fOWZ0akNXoGo1G/Pjjj3j33Xfx1Vdf4ezZsxb327p1q8PTPLNi0Ov1whjVHl90dDR69eqF33//HV5eXgCA6dOn48knn0RqaqrN97lz5w5WrVqFuLg4iziPHz+OdevWqdalWpxERFry3//+F2+88Qbu3r0LX19f7N+/H4MGDcLvv/8OvV6PBw8e4KuvvoLRaLTYb+HChThw4ADKli0LwDS1vm/fvti5cyf8/f1x9uxZDBkyBIsWLQJgagNWrVqFDRs2YOLEiTh79iwSEhJyjSkjIwNJSUnw8PDAsmXL8MEHH+CTTz7B3r17Le4XGRmJVatWCafB1axZE8HBweZRpIDpC81Dhw6hWbNmaNy4scVxZVnG/v370bZtW+h0Ouzduxf9+vXDrl274O3tjQcPHuB///sfxo4dC0VRUKpUKfMXc6GhoeYPP7dv38aAAQPw5ZdfwmAwQJZlTJ06Fc8884w5J/iRI0fwxx9/4M0338TevXuF0yG/++47PPfcc7h58yYCAgJw+fJljBgxAgsXLjTfx95Y//7771zb+lWrVuHMmTPCWO0958GDB/HHH3+Yz3Xjxg307dsXc+bMgcFggCRJ+Oabb9C/f3/cvXsXAHD06FFs3LgRL7/8Mnbv3g13d3esW7cOAwYMwM2bN63WHRFRXsXExKBv375YvHixudPw22+/Rb9+/fDo0SOEhYVh7dq1WLlypcV+d+/excyZM82f3zIyMvDCCy9gwoQJSEhIgJeXF3788Uf069cPsbGxAIDLly9jxYoV+OKLL7By5UphCoGHDx9CkiTExcVhxowZmDJlCr7//nuLzzWyLGPVqlU4cuSI8DFmvQdnfdmXm5o1a6JChQo21wsAbN++HX379sXJkycREBCA+Ph4TJgwQTU/uS37qZ3fx8cHAQEBAIDy5cubB7acP38+1/8Xtm7din379gGw/jzYe86LFy9i1apVSE9PB2BKi/D0009j8uTJSE5Oho+PD3777Tf06tULx48fBwBcvXoVK1aswKeffor58+dDr9fj8OHDGDlyJHbs2CGsPyJXwxGnZLPU1FS89NJLCAsLM490efXVV9GxY0f89NNP+L//+z8MGzYMn3/+OW7dumX+xvLs2bOIiorC+PHjAZi+EZw5cyaef/55/Oc//wFgahRHjx6Nd999F5s3bzY30IcOHcIff/wh/CYyq+N0woQJaNSoEUqXLo1du3bh+++/x4cffojBgwcDAF577bUcH5bzIjY2Fn/88QcaNmwILy8vqzHa8vj++OMPPHr0CIsXL4aHhwcA4IUXXsDUqVNx7do1VKtWzab72MKROImItKR79+4YNmwYGjVqZN42aNAgzJ07F/369cPw4cOxadMm7N692zx1PTk5Gdu2bcPzzz8PSZKQkJCAyZMno2XLlpg7d655ZMiHH36ITz75BD169EDJkiWh1+uxefNmrFixApUrV7YaU1Yb9dVXX6F27doIDQ3FuXPn8NNPP2H06NHm6fuDBw9GkyZNVKcPtmvXDhs2bEBaWhrc3d1x6NAhpKeno2XLltDr9fjtt9+QmJgIHx8fnDx5Eg8ePDA/1qCgIHz22Wfo2bOnefRe7dq18eGHH+LUqVNo3rw5Dh06hN27d2PMmDHm0aRffvklbt68iXXr1pmn7Q0ePBhDhgzB8uXLERERATc3N1y+fBkvv/wyXnzxReFjWL58OYYNG4YpU6aYty1cuBDR0dHmv+2N1RbWYs2vc37xxRd4+PAhtmzZgqCgIADAM888g169emH27Nn43//+B0mScO7cOQwfPtz8Qf+ZZ55Bly5dsHnz5lxHTBER2UOn02Hy5Mlo3bq1uVOsT58+ePLJJ7F+/Xo8+eST6NChA1atWoWXX37Z/P63adMmSJJkToW2dOlS7N69GzNnzkSXLl0AAC+//DK6d++Or7/+GtOmTTN/7khISMAff/whHCn+8OFDKIqCiIgINGnSBO7u7ti4cSPmzZuHH3/8EXXr1oXBYMC0adNUR+JHRUXBx8cnT9PZbamXFStWoF69epg9e7Z5v969e+O3335Damqq+TPa42zZz5bz9+zZEytXrkT//v1zzMoQsfY85Nc5Fy1ahCNHjuDXX381jx597bXX0KdPH0ybNg2LFy82n/PBgwfmL57T09PRqVMn/P777zanECJyBew4JZt5e3ujW7duFtt8fX1RtWpVXL9+HQAwcOBAfPXVV1i9ejVeeuklAMD69esRFBSEjh07AoB5JMjTTz9tPo5Op8PQoUMxceJEXLhwwby9R48eqtMbK1asiLfffhstWrRArVq1AJhG4Lzwwgv44IMP0LlzZ/M0QFsdPXoUkyZNMv997949HDx4EL6+vpg6darFfR+P0ZbHFxgYCKPRiHnz5uGpp55CYGAggoODMXPmTPM+ttwnL+yJ09bOWSIiV5DVzmTXoEEDrFq1CgDQqlUrVKpUCStXrjT/w75t2zakpKSYpwD+/fffSEhIQEREhMV0uieffBK//PILdu/ejX79+gEAmjdvLuw0BQB3d3e8/fbbqFatGtq0aQPAlFJg6tSpmD9/Pnr06IFGjRqhcePGaNy4sepjbN++PVasWIEjR46gRYsW2LNnD6pVq4ZSpUqhZcuWMBqNOHDgADp16oS9e/dCp9Ohbdu2AIDq1aujevXqOeoHMKXIyUpF8LitW7eiQ4cOFh9I69ati7p16+LPP/80d/ylpKQIR/tkCQwMxM6dO3HgwAFzZ/Hj+9kbq61yizW/zrlz50506tTJ3GkKAJUrV0bjxo3No4EA0wfb4cOHm/+uUKECvL29zSO3iIjyQ9myZc0zKrLUq1cPOp3O/Blu+PDhGDt2rHkGA2D6DNehQwfze9lff/2FcuXKmTtNAcDf3x89evTA+vXrMW3aNPP2wYMHq6bXaN26Nfz9/dG7d2/zObLWlJgyZQpWrlwJvV6fI71ObtLS0swz9LJbs2aNxfsuAAwZMgRNmza1qV4CAwPNU867dOkCDw8PtG3b1tyuWmPLfrac31GPPw/5dc4dO3agSpUqFlPuvb290b17d8yfPx9JSUnm7U899ZT5dzc3N4SGhuaYLUnk6thxSnny4MED/PXXX4iKisKjR49gNBoRHR1tzgmXlQdu5cqV5o7TDRs2oH///uaRGVkfCL755huLY9+7dw+AaVh/1ocza/nisqtUqVKOkRmenp4YO3YsxowZg0OHDuXo8M2rypUro0uXLujbt69FDtLcYrTl8fXv3x/Xrl3DvHnz8O2336JWrVro2LEjIiIizP842HKfvLAnTnacOpcROhg1kFFFCzFS8ZCWloatW7fizJkzePDgAdLT03Hs2DHzbANJkjB06FBMnz7dnFtz/fr1aNWqlfk98tatWwBMoyK3bNliPnbWMbKvnm5LG5WVbyw7SZLw6quvYvHixdi1a5fFCFk1bdq0gcFgwJ49e9CiRQvs3bsXrVu3BgAEBASgVq1a2LNnj7njtG7duihZsqR5/xMnTmDfvn2Ii4tDamoq7t+/DwBWUwQkJycjPj4ely5dsvhCETC1F1ltRtb5s/LciUyfPh3//ve/8fTTT8PPzw8tWrRAnz590LNnT4v75TXWvLAWq6PnTE5OxsOHD3PkCQSAMmXK4Ny5c+a/AwMDc4xa1ev1yMjIyMtDKRYkSUM5TjUSJxUv169fx7Zt2xATE4Pk5GRkZGRAkiTze1u7du1Qrlw5/Pbbb2jWrBlu3bqFyMhIfPfdd+Zj3Lp1C6mpqTnaggsXLuDhw4cW7YEt7WPTpk3RtGlTi20hISEYOnQoZs2ahXv37lm0XyJBQUF48OCBeTZGlqSkJPP7eEZGBnbv3o3mzZubz6tWL2+99RYSEhIwYcIEGAwGNG7cGN26dcOwYcMszvM4W/dTO7+jcnse8uOcsbGxubZzwcHBkGXZnJYGMLV92en1enPaObIdWxbnYscp2ez69esYPnw4DAYDevfujYoVK8LNzQ2HDx+2uN+wYcMwevRoHD58GAaDAdHR0ebp8gBgMBig0+nMucGyBAcHo1atWha5Z0QNkprAwEAAQGJiYp73bdiwIT755BOb7vt4jLY8PkmS8Prrr2Ps2LE4cOAA9u3bh+XLl2PRokVYsWIFKlasaNN9rMktJYE9cRIRaUVSUhIiIiJw5coVDBgwAOXKlYO7uzuio6MtOjsHDRqE6dOnm7/U27lzp8X7fdb0ttKlS5vbkSwvvfSSxbQ1R9oof39/SJJkMSrDFr6+vmjcuDH+/vtv3Lt3D+fOnTOnwgFMI3h27NiBlJQUHDlyxGIq+uzZs/HVV1+hXbt2aNiwIby9vc1T9azJSh3g4+OTo73o27evxQIattZHxYoVsWTJEkRHR2PPnj3YsWMH3nzzTezcudM8YsmeWK3JrSMyt1jz45xZ9ZWVBy679PR0i45SdrARUWHYsWMHXnnlFdSoUQPt2rVDmTJlzPmXs+h0OgwePBg//PADpkyZgg0bNqBUqVIW06kNBgM8PDxy/ezQtm1bi4WK8usznK0dpw0aNMCyZctw8OBB8+wOABgxYgRGjBgBwPRlX/YFjG2pF19fX3z11VeYPHky9u3bh927d+P//u//sHLlSixZssTq47RlP1vOnxe2fP7Lr3Pq9fpc27msvOds66ioYccp2WzFihV4+PAhdu3aZfFhcsmSJRb3a9myJSpXroz169fDYDCgUaNGFqv/hoWFQZZljBgxItdvqgBT0mtHnTp1CgBQpUoVh4+VF7Y8PqPRiOTkZPj6+qJ9+/Zo3749XnjhBXTo0AHr16/HSy+9ZNN9PD09ASDHt3aiROx5iZOISCt27NiB06dPW+TbAkypV7ILCgpCp06dsG7dOnh7e8PLy8tiVkJYWBgAoFOnTnlK8ZJXZ86cgaIoqlP9c9OuXTtMnz4de/fuhcFgsHi8rVu3xrx587Bjxw6kpaWhffv2AEwjJr///nsMGjTIYjrljh078P3331s9l7u7OypWrIiQkJAcK9XbK2sBigoVKmD48OEYPnw4ZsyYgRkzZuCdd96Bl5eXXbECgIeHh11tor318zh3d3eUL18eUVFROcouX75c6P+TEBHNnTsXVatWxYoVK8wpaO7evYuPP/7Y4n5DhgzBzJkzsW3bNqxfv95ixiBgah+PHz+eb22BNadOnYKnp2eODlqR7t2749NPP8XXX3+NZs2a5dqh+XhHny31ktWelCxZEr1790bv3r3Ro0cPjB07FseOHbO6Orwt+9n6vDwuK69q9hyr6enpiI6OVv1MZ+85HxcaGopDhw7BaDRa5Ga/fPkyvL29c4wyJdI6zq8km6Wnp0NRFPO3WUajEdOnT0dycrLFiBlJkjBkyBBs3rwZGzZssBhtCpgaNi8vL3zxxRfmb6UAUwLyH374Ic9TE5YuXYpWrVph586d5m1nz57Fd999h5o1a5rzk82YMQNfffVVnh93Xtny+P71r39h1KhR5hUbASA+Ph5GoxGlSpUCAJvuU758eeh0OosVlrdt24Zjx47lS5zkXLIiwaiBm6zwm2RyvqxRhdlHF65fvx7Hjx9HRkaGxfvcsGHDcOTIEcyfPx99+vSx+IDVqFEjVKpUCbNnz8aDBw/M248dO4ZvvvnGYpstDh06hJYtW+Lnn382t59xcXH44IMPzLnhAOC3337DpEmTbHrvbd++PdLT0/HLL7+gUaNGFilkshbY+OmnnxAYGGjOy6koCjIyMixGpERHR2PBggUAYG7Hs0YMxcfHm+83YMAAbN26FQcPHjRvS0hIwPTp03PMOlETExOD5s2bY/HixRbb79+/Dx8fH3h5eTkUa8WKFREdHY0rV64AMH2wnDFjhsVIqNw4cs7HDRgwAHv37sWBAwfM2zZu3Ijz58+jf//+wjjICkljNyIXkpGRAVmWoSgKANP797Rp0+Dr62sxMy8kJATt27fH3LlzcfLkyRyf4QYMGICYmBj88ssv5m1GoxHz58/Hxo0b8xzX//73P/Tq1QuXLl0yb9u8eTPWrVuHgQMHwt3dHenp6Zg0aZI5V7k1fn5+eP/993Hy5Ek8++yzOH78uMXj3bBhA0aPHg1fX19zKjK1ejEajejatSvef/99i7Yhaxq6tdGwtu5ny/NirZ0DgN27dwMwtV8zZsyw6f8He8/5uAEDBuDu3bsW18K5c+ewadMm9OnTR3WhS8obHQCdpJGbsyurgHDEKVnYvHkzdu3alWN7REQEhgwZghUrVqBXr14ICwvD9evXMWLECIwePRqffPIJXnnlFXz00UcIDAzE4MGDMX36dLi5ueXIWVamTBl8/vnnePvtt9GhQwdUrlwZt2/fRlxcHCZNmmSxGIct+vbti507d+KFF15AYGAg3N3dERsbixo1amDGjBnm6QFbt25FWlpagX9LasvjmzhxIsaMGYN27dohLCwMRqMRFy9eRIcOHcwfqmy5T2BgIEaOHIkFCxaYn7eAgAA8/fTT+PXXXx2Ok4jIlVy6dAktWrTIsb158+aYNm0awsLC8OKLL6JWrVq4ffs26tevj/fffx/PP/88nn/+eUyaNAl16tRB27ZtUb58eZw9e9ZidCFgmn727bffYty4cejUqRPCw8ORlJSES5cu4amnnoK/v3+eYm7SpAkGDBiAzz77DNOnT4e/vz9u3ryJoKAgzJw50zyDIzIyEqtWrcKHH36o+v5bs2ZNhISE4OjRo/jXv/5lUebh4WFehKhfv37mY+n1ejz77LOYPXs2Tp8+DXd3dzx48ACzZ8/G2LFj8d133yExMRFNmjSBJEno378/6tatizlz5mDs2LG4dOkSRo0ahbCwMHh7e+PixYuoUKGCTYt2ZFe+fHmMHz8e06ZNw7x58xAUFIS4uDgkJCRYPHZ7Yx0+fDiWL1+OAQMGIDQ0FDdu3MD48eNx8OBB8wfF3DhSP4978cUXcf78eYwePRrVq1dHamoqrly5ghEjRmDYsGF5qi8iIlt89tln+Prrr3PdPmrUKPz73/9G165dUbp0aURHR+Ojjz5CSkoK1q1bBzc3N0yePBmA6YvFl19+OceMQcC0AONrr72GTz75BD/99BPKlCmDK1euwGAw2DU45cUXX8S4cePQs2dPhISEICUlBfHx8XjiiSfw1ltvATDNBli1ahXc3d3Nizha07NnT5QoUQIff/wxhg4dCk9PT3h4eODhw4cwGAzo1q0bZs+ebZ7pYUu9fPDBB5gwYQL++usvVKxYEQ8fPkRMTAxeffXVHPWTRa/X27SfLed/9dVX4evri/HjxyMoKAgbN25E+/bt0aBBA0ycOBGzZ8/GvXv30LlzZzRr1kzYzjlyzsd17twZL730Ej777DMsXrwY3t7euHDhApo1a4b//Oc/qs89kdZIitqri4qN33//3eqCBNWqVUO9evXw8OFDHD58GCkpKahbty4qVqwIWZaxa9cupKWloUOHDuaRO71790b9+vVzfCjNkpiYiMjISNy/fx8BAQFo1KiRedX3hw8f4s8//0SLFi1sSi4OmHKwnj9/HklJSahYsSLq169v8eFz27Zt5m8A1eqhXLlyOZKVP04tRtHjA0z/CBw7dgw3b96Eu7s7wsPDERoaanEMW+4DAKdPn8aVK1dQsmRJNGvWDJcuXcLp06fRv39/h+OkwpeUlIQzZ87gks8nSNHnz6qaBcnTWBFhiZNQq1atHIunEeWXHTt2WCw2kF2ZMmXQtm1bpKWl4eDBg4iPj0d4eDhq1qwJADh8+DDi4uLQokUL80iPCRMmICoqCqtXr871mOnp6Th69Chu3boFb29v1KtXz2Lq2erVq1G9enXUrl3bpvjv3r2LkydP4sGDByhdurR5ZGiWyMhIXLlyBQMHDrQpH9i+fftw8+ZNtG7dOsfUvBMnTuDChQuoX78+qlatalF29uxZXLp0CSVKlECzZs3g4eGB2NhYHD58GFWqVEHt2rXN9wkLCzPXIWCagnfmzBkApoUZ69SpY441MjISsbGxOb4stebevXs4ceIEHj16hFKlSqF+/foW+VIdiTUhIQGRkZFISEhAvXr1ULFiRWzcuNEcsyhWe8556NAh3L9/P8dClBcuXMD58+eh1+tRt25di9zh1mJYu3YtKleujPr169tUj0VdVns45qcLOB+b7OxwbFI92Avznq3GNpEKRXx8PLZu3Wq1vGXLlihXrhxiYmJw8uRJ6PV6NGvWDP7+/khISMDu3bvh7+9vzv0ZHx+PFi1a4MMPP8TQoUNzPebt27dx7NgxpKSkIDg4GA0bNjSPUrx06RKOHj2KXr16mVOKqTl9+jSuXbsGWZZRs2ZNc8ocwDR6c82aNQgNDc3TQooXLlzAtWvXkJKSglKlSqFevXo52hgANtVLcnIyjh49irt378LPzw+1a9dG6dKlVWOwZT9bzh8TE4Njx46hdOnS5tQARqMRhw4dwp07dxAaGoratWtj//79yMjIQNu2bYXPgz3nvHjxIo4fP45+/fpZpG+4desWjh07hoyMDIv/uwDr18Lu3bthNBot8udS7rLawJhPRiEt+pz6Di7AvUINlJ+0oMi1gew4pQJx5swZDBgwAAsXLlTtgCQiS+w4JSpYCQkJaN++PcaPH4+RI0c6OxwisoIdp0SF69dff8UXX3yB3bt359rRSESFhx2nroNT9SnfJSUl4f3330fjxo3ZaUrkABkSZA1kipGZ0I00RJZlfPzxx/D09MzzNHMichJJQyszayRMosddv34d3333HSIiIthpSuRCdNBO7lCtxJlX7DilfDVp0iRs374der3eIlk0ERGRs82ePRtLly7FnTt38N133xWpb8KJiIjsYTQaMWTIEFy+fBnVqlXDuHHjnB0SEZFLYccp5atBgwahQ4cOaN26dZ4X0CAiIipIHTt2RMWKFdGoUSOUK1fO2eEQERE5nU6nw4svvghPT0+0adPGnK+UiIhM2HFK+ap58+bODoGoyJAVHYyK6094kDUQIxFgWo0++8IFRKQNkiRpZqq+VuIkyiJJEnr06OHsMIjIGgnQTNOilTjziJ92iYiIiIiIiIiIiB7DjlMiIiIiIiIiIiKix2h+qn5GRgYePHgADw8P6HTsByYi1yDLMlJTU+Hv7w+DQfNvtaQRbBOJyBWxTaTCwDaQiFwR20Dt0/yz9uDBA1y5csXZYRAR5apKlSooVaqUXfvKkCBrIFGMFmIsLtgmEpErs7dNlKChHKdsE52GbSARuTJ720AdtDNVXCtx5pXmO049PDwAAF9dOomYlCR46PT4qGYT/O/sYaTKRtOdRM+eThGfQPS/j2hfleNKkvVyqaCutmyn9NDp8UHFFph8/W+kykbIsso/eRmCoEQPVVE5riwuFtILTmzv8wZAZzAF9XgdAYBOtK/gOVUl2FVRWXhHeFaVkGTR85ouKMv2vHlIenxUqwn+d+YwUhWj+ISO0ovKBBeT6FoBIAkqShFdTILDlnf3wZsV6pvfo4gKQ9b1Vq5CFXh4ekKWjbh66QIqh1WDTid6ARVvrCd1rCPbsJ5yl5qSghvRV9gmUoHKur6qlC0FLw83GGUF56/FonqlYOh17NC2hvWkjnVkG9ZT7pJT03Hl5l22gRqm+Y7TrGkYMSlJuJScAK/Mf1KvJCcg2dxxan8Hp6jTVXKk41RQLjyuI7J1YmbV09XUR0iWjZCN4jc2RdTBJur8dFbHqb3PGwCdmymox+sIAHQ66wE78rwpoo5T2ZGOU3H9y4LOUSVNcF5jzmvJ4jVnL7X2VfScGwTPjVrHqaDTWxHVoQ1POaeKUWHKut48PD3h5e0No9H0mvT08oZez04ca1hP6lhHtmE9ibFNpIKUdX15ebjB28sDRqPpf0NvT3fo9bz2rGE9qWMd2Yb1JMY2ULs033FKRFRUyYoEo8qIY1cgq31BQkRE5ABJ0tBUfY3ESURE2iBJppsWaCXOvCoeHaeOTCUXDAUUjRJUnbVtb0yOTAcvKA6OyCuQ8zpw4qznNftP0XNtK7tHNRYk0fXkSEii+nLBN1NHR5USERERERERUdHj+kOZiIiIiIiIiIiIiAoZO06JiIiIiIiIiIiIHlM8puoTEWmQEToYNfD9lhZiJCIiDZPgkql+cqWVOImISBMkSYHOFVM25kKUnlDLik7HqSxBMkqQMnMVSkYJkmz6Xby2isoTK/rnR7ZeqHq5iNKYOpJUUZCrMXuezjzn77Q3JNVcrw78dynbl59T9ZRZ103mQRRZgpK5TdZZ31mnUpGiRMmiMofeelTeuCTBgr+KaCV6Y7aApWw/s34X5vAVhiQmC8qMwhdVgRA+5YL3ByIiIiIiIiJyfU4fJpSRkSEsT09PL6RIiIiInIttIhERFVdsA4mIyBU5reP08uXLGDFiBBo2bIimTZti+vTpFuVr1qxB+/btUb9+fXTs2BEbNmxwUqRERM6hKBJkDdwUR0aOEwDg+PHj6NevHxo0aIBWrVrh119/tSj/6aef0KpVK9SvXx/du3fHvn37nBQpEVHhkyRJUzfKm927d+OJJ55AgwYN0LFjR6xfv95cpigKvvzySzRt2hQNGjTAgAEDcOrUKSdGS0RUuHQauxVFTnlcaWlpePHFF1G/fn0cOXIES5cuxcKFC7Fjxw4AwKlTpzB58mRMmTIFx44dw6RJkzBp0iRcvHjRGeESEREVmAcPHuCFF15Av379cOLECcyaNQuffvopzp49CwDYtm0bZsyYgenTp+PYsWMYNWoUXnnlFdy9e9fJkRMRETnm+vXrePXVVzFu3DjzZ8C33noLt2/fBgAsWbIEq1atwq+//orIyEh06tQJL7/8MlJTU50cORERFRdO6Tjdvn077t69iwkTJsDNzQ3h4eHYtm0bOnToAABYuXIlOnfujK5du8Ld3R09evRA69atsWrVKmeES0REVGDWrFkDf39/jBkzBjqdDg0bNsTff/+NmjVrAgBWrFiBoUOHolmzZnB3d0dERAQqV67MmRhERKR5ixcvRr169dC/f38AQJcuXXDgwAGULl0agKkNfP7551GzZk14enritddeg9FoxM6dO50ZNhERFSNOWRwqMjISLVu2hLu7O4xGI/R6Pby9vc3lp0+fRqdOnSz2qV27No4ePWr9oHLmLfviNJkLyUiClWHUFwoSlOkEK8OoLQwjXLxItOiRA9N/lFx+V7LdRESndShewb5qT46oXPS8qR0262fm8RXjP4tDKYLHo6h8DyELgtIJriVJJ3owgCLb//2HJDqvwfp5lewLMWUdQ6/8cy0InhtJuKiayoVo54JLjizWZrd8mL5uhKSJFeuNXELYIZGRkWjXrh0AU343g8Fg0SaeOXMGTzzxhMU+tWvXxunTpws1TiIip5GgnSnwGgnTVURGRqJz584AcraBsizj3Llz+M9//mO+v06nQ82aNXH69Gl069bNKTETERUmSXKsG6gwaSXOvHJKx2lsbCw8PT3xyiuv4ODBgwCAAQMG4K233oLBYEB8fDz8/f0t9vH398f9+/etHtNTr4eXXg8vnWmZ8KyfAMSdlKLOT0DcOSoqU+s3FXaUFdDq4NlO6ZlZP1k/FZUDK4q9nTc29lLmWubAg7X3ectWntu1pBM8N6LOTwCQJOt1qBN0Pssq7z6y6HpxgKyzHq+SrU5yfc0JYhJ/kaHy3IgeqiPPeQFUoYfg+SbKLjY2FiVLlsSoUaNw+vRp6PV6PPPMM3jllVcAAPfv30dAQIDFPv7+/rh8+bLVY8qyEUaj6QbA/JNyx3pSxzqyDespd7LM+qDcxcXFISEhAYMHD8bly5fh7e2N1157DcOHD0diYiLS09Pz/LnQKCswGmUYjaZBAFk/KXesJ3WsI9uwnnJnlFWH8ZCLc0rHqdFoxO7duzFv3jzMnDkTly9fxtNPP40KFSrgmWeegSRJkGXLF5uiKMJvmj+s09ji77lN2hRI7EXN3Kqd1O9UzM2r2cHZIWjCvLrtnB0CkSYZjUZs3boVP/zwA8LDw3H8+HE888wzCA8PR/fu3e1qE69eumDx98WzJwsk9qKG9aSOdWQb1hORbTIyMrBt2zbMnTsXISEh2LlzJ15++WXUqFEDVatWBWBq87KTZVnYBp6/Fmvx94momPwPvAhiPaljHdmG9URFjVM6TgMCAtCkSRM0aNAAABAaGoq+ffti3759eOaZZ1CqVCncu3fPYp+7d+8iKCjI6jHfORWJK0kJ8NLpMbdJG7xweA+Ss77dFo041TtpxKngvJJe8A1NPo44nVu1E164uA0pstE8Fd3qrkbB6DnRF0pqU6udMeLUxufcS6fHvJodMObsDvO1pBNMX9epHFcSjKYUjVaVVepQLqAVzeUMwYjTVMsRp/PqtsOYk7v+ec1l2DlVv6BGnKo95wVQhVU8ffFR1Wb5f2AqcgIDA1GrVi2Eh4cDAOrXr4+OHTti79696N69O4KCgnJtE7Pyv+Wmclg1eHp5w2g04uLZk6hasy70er3V+xd3rCd1rCPbsJ5yl5KclOMLHSLA1Ab26NEDISEhAID27dujfv362LdvHxo0aAAvL69c28Bq1apZPWb1SsHw9nSH0SjjRFQM6oWXh17PmUDWsJ7UsY5sw3rKXVJKWo4vdEhbnNJxWr9+fSxYsMBiW0pKCjw9Pc3lx44dsyg/fPgw2rZta/WYKUYjkjOMQOb/qMlGI5KzpkkJXrOPf4OZg+j17sjUYFFno1E0jV+lc05QrmTrYMv6PdkoI1m2ZSi9IN+lqONOrVNPdGpHOgSFx1V5brI62XKpI0lwYEnluKLOUZ0wYDFZkONU+NwAKvlprcckZ+vcz8rBmqJPR4pkes2JOtolQYesagemvWk31GYIOvAliDWpgtexrRToINudJqPwqOX3JbF69erh+PHjFttSUlIQHBxsLj927BgGDx4MwNRuRUZGYty4cVaPqdPpLTpt9Ho9O3FswHpSxzqyDevJkk7nWF1IkDST41SUkohyql+/Pm7evGmxLTU1FV5eXpAkCXXr1sXRo0fRpo1pNmFKSgpOnTqFl19+2eox9TrJotNGr9exE8cGrCd1rCPbsJ4s6R1MrSfBSau626GotoBOqf8ePXrg/v37mDVrFu7du4fdu3djzZo16NmzJwBg2LBh+Pvvv7Fw4ULcunULc+fOxblz5zBkyBBnhEtERFRghg4disOHD2P58uW4d+8eNmzYgF27dqF79+4AgIiICKxZswbr16/HzZs38emnnyIjI8PcZhIREWlVREQE1q1bhy1btuD+/ftYuHAhLl68aF4oeOTIkfjpp5+wc+dOREdHY8qUKahYsSJatmzp5MiJiKi4cMqIU19fX/z444/46KOPMH/+fAQHB+Ott94yf0isXLkyZs2ahS+++ALTp09HaGgo5s2bhzJlyjgjXCIislNycjLWr1+PmJgYBAcHo3fv3vDz8zOXP3r0CBs2bMDNmzcREhKCnj17mheBuHLlCpYuXZrjmBEREahQoUKhPYaCFhwcjLlz5+Kzzz7D559/jgoVKuCLL75Ao0aNAACNGzfGtGnTMGvWLMTFxaFWrVr44Ycf4OXl5eTIiYiIHFOzZk18+eWX+OabbzBlyhSEhoZizpw5qFy5MgDTgJu7d+/io48+Qnx8PJo0aYLZs2dDJ1jElIiIKD85peMUAKpVq4b58+dbLW/dujVat25deAEREbkYIyQYNTDhwVqM9+7dw/DhwxESEoKmTZti3bp1mDlzJlavXo2SJUsiJiYGw4cPR506dVCnTh1s3LgR3377LVauXIng4GBcvHgRv/32G55++mmL4xbFD0uNGzfGkiVLrJb36tULvXr1KsSIiIhchyRpaKq+RuJ0JR07dkTHjh2tlkdERCAiIqLwAiIiciGSZLppgVbizCundZzmO0X655b9bwCQBTlDVTolFOHqRQV0VQhOqZqzUpQr05FwRbkwRX0Yamkeha8slZ3tzYGqmk402zUEAEbJvMiVMJejICdo9sPlWiZYMEk9r60g/6wg/6lDsseU9btOgfk5Ey1+ZhQtDpWH8+Y4sMq+ImqLmNlDLa9qMbBo0SL4+Pjg559/hk6nQ0ZGBrp164aNGzciIiICCxYsQI0aNTBnzhwAplVyu3btii1btuDpp59GQkICgoOD8dprrzn5kRAREREREVFxVHQ6TomIyKW88soreP75580jRA0GA0qXLo1Hjx4BAN5++22kp6eb75+amoq0tDQEBgYCABITE+Hr61v4gRMRERERERGBHadERFRAdDqdRR7O48eP48yZM5g2bZp5m5ubG/766y/s2rULp0+fRv/+/c1T0hMSEuDt7Y0//vgDZ86cQalSpdCjRw+UKlWq0B8LERERERERFT9FL1EcEVERoSgSZEXn8jf1FCLAuXPnMG7cOEyaNAnh4eEWZQaDAW5ubsjIyMC1a9eQmJgIwNRxunfvXqxbtw46nQ5//vknevTogaioqAKpbyIiclGSxm5ERET5RKexW1HEEadERFSg9uzZg/Hjx2PChAkYNmxYjvIOHTqgQ4cOMBqNGDFiBL777jtMmjQJzz77LEaOHIng4GDzfZ977jnMnj0b//d//1eYD4GIiIiIiIiKoSLTcSrJmTfJ8m/TH4LFaFQWIBItHuXQwlGixZZEC9WI9oPKIkOCBXtUiUISLNijOhLNgZDs3lctpscXE5Pxz4JSogWeBIseZR3GKsE1qlNUrlHBaXUq14ssqAuV0wrp77lZL3RggSdFcAfhripffSmiehIdmKNKVO3YsQMTJ07E559/jg4dOliUnTt3Dn5+fihXrhwAQK/Xo2HDhrhw4QIAoGTJkjmOV6dOHRw9erTA4yYiIiIiIiIqMh2nRERFjRESjBronbUW482bNzF+/HhMnz4dbdq0yVH+3XffITU1FXPmzAEAyLKMw4cPo0mTJgBMi0fJsoxPP/0UAJCRkYE9e/agWbNmBfRIiIjIFUmSBEn0TbEL0UqcRESkDRIAnUaaFo2EmWfsOCUiogLx3XffwcvLC5s3b8bmzZvN2+vUqYPhw4fjX//6F0aOHInhw4ejRo0aOHbsGJKSkjB27FgAwMiRIzF69Gg888wzCAsLw6FDhwAAL7/8slMeDxERERERERUv7DglIqIC0aFDB4SGhubYXrp0aQBAWFgY/vrrL+zcuRN37txBhw4d0K5dO7i7uwMwdbBu3boVO3fuRHx8PDp16oTWrVvDYGDTRURERERERAWvyHz6lBTrOU4VQX5DSZRPFOI8psL8pyq5JcVJQ+3Mf6pGyuUPWTLdRHkn83Tcx4pUjivMo6mSM1RYTzas8q26b/af5t8F+wmTmEKcH1XwvCpqCTp1aie2k/CxZo/3sWsJgM4oOKzoGnYgD68o/6laQlzhtDp7LyVHXqtFRLdu3VTv4+Xlhe7du1st9/PzQ+/evfMzLCIiIiIiIiKbFJmOUyKiokZRdJBF3/y4CEUDMRIRkXYxxykRERVXkiReENqVaCXOvOKnXSIiIiIiIiIiIqLHsOOUiIiIiIiIiIiI6DFFZ6q+ku2GXH63+7iixJSC/KdquVNFuT9F+6rlIhWl/cx23Kx8kIosQZElSGo5We0dcq16XAdySzryvNpLlE5UJV5hbk9BPlf11Kn2f/8hzK1qtH5cJVu8SuZrRDFK5uNJGYKT6gVlqmP77XvSxflPIb5O7c5xaud+2RgVCUYNTIM3OpJTmIiIyAacAk9ERMWR638a/IeWYs2Lovq4iIiIiIiIiIiIiOzGjlMiIiIiIiIiIiKix7DjlIiIiIiIiIiIiOgxRSfHKRFREaNAgmx3ktXCo5pDloiIyBES7M85Xti0EicREWmCJNmwFIiL0EqceVUsOk5F676oLTUj3Fe4AJT4yJLgvypF70DAti5m9fgiWmqLu6gt8mQvRxblEb0qC2rhKFE9GdX2FRSJFgASLEIGqCw0pka4OJT1Mq8r7v/8btADdQGva+5AhqkSFMECULKgIlTXGBI855KonlRX2BLUg71v/lwwiYiIiIiIiEjTikXHKRERERERERERERW+7du3Y8mSJXj48CHq1auHcePGoUSJErneV1EUrFixAn/99RcSExMRHh6O5557DpUqVSrkqE2Y45SIyEUZFZ1mbkRERAVFkiRN3YiIiPKLBFPHnRZu1lrA3bt34/XXX0ebNm3w8ssv4/z583jllVesPubvv/8es2fPxjPPPIN3330XXl5eeOqpp/Do0aM81Fz+4addIiIiIiIiIiIiynfz5s3D888/j6effhrt2rXDt99+ixMnTiAyMjLX++/YsQMDBw5Eq1atULVqVfzrX//C7du3cebMmUKO3KToTNWXYJk43sYk8mqpO0VpE4UZTlVzkdpZpvaghHkes+8r/bNNltRTnDqSi1R4XOtlqoPYRPUkTE7rQMDC503tuRGUCXJwql5LjoxsEOQxFeU/lTKy/Z5tm3m76HIRPK+qqXRF9SS6EFVf6IJdRdXrwCmJiIiIiIiIijJFUXD06FGMHTvWvM3X1xe1atVCZGQkGjdunGOfunXrIjIyEunp6XBzc8OxY8fg6emJsLCwwgzdrOh0nBIRFTEKJMgaWGRK2GlNRETkIC1NgddKnEREpA06STvrDufWBD569AjJyckoVaqUxfagoCDExcXlepyJEydi4sSJaN++PUqWLIl79+5h1qxZCAoKKoiwVbHjlIiIiIiIiIiIiPJVRoZpWqper7fYrtPpYDQac91nxYoVOHPmDD777DMEBwdj/fr1eO+997B8+XL4+/sXeMyPY45TIiIiIiIiIiIiyle+vr7Q6XQ5FnZ69OiR1U7Qb7/9Fq+//jratWuH6tWr480334Rer8e6desKI+QcisyIUwWm4ctZQ5iz/y7MNaiSh1AS5VQUdTur5ru0fmJJkFtS0dufqzHX+ymZN8HjBABFMO1I0jmQzNGR3KnC3JPCDLTi44qeOwcOK6xjYbLXAsrJCvG1JopXl+2LoazQJeM/24UPVZQ7VS6gxKCOXEv2pstVeU0RERERERERFWXu7u4IDQ3F+fPn0ahRIwCALMs4d+4cRowYkeP+iqIgISEBJUqUsNju6+uLhISEQon5cRxxSkTkooyQYIROAzeNJN0hIiJNyspxqpUbERFRfpE0dstN3759sWDBAjx8+BAA8OuvvwIA2rVrl/PxShIaNWqEDRs2QMkccHj58mWcPXs214WkCkORGXFKREREREREREREruP555/HmTNnzIs9paSk4Ouvv4aXlxcAYMeOHZg1axaWLFkCAPjggw8wfvx4PPHEEyhdujSioqLwyiuvoFmzZk6Jnx2nRERERERERERElO/c3d0xffp0xMbG4sGDBwgNDYWbm5u5vEGDBpg8ebL578qVK+O3335DXFwcHj16hPLly8PT09MZoQNgxykRkctSFAmyBqbBK47k4SUiIlIhQTtT4CUNtNtERKQdkqSyhIsrUYkzODgYwcHBObYHBAQgICAgx/YyZcqgTJky+RSc/SJUkl0AAQAASURBVIpFx6m9a/2oH1hwXLX1h+xdsEptwRmdjavcPL44lFrHh3C1H1GZ/Yv9qL05CBfnEp1XtCASAGQtUCRl+5n1u6ieHFnXyKEFzOxb4EltX5/r1ivYohrkbD8zfxc9d5JRUKaWddnuOhY/5/ZeS8K1zQponSsiIiIiIiIiKhxO6Tg9f/48vv/++xzbx40bh9DQUADAvXv3sGTJEkRHR6NSpUoYMWIE/P39CztUIiKiAnXw4EEsXbo0x/b//ve/KFmyJADgxo0bWLZsGeLi4lCtWjU8+eST5pxAREREWvXnn39i06ZNObZ/9tln0OlM32xHRUVh5cqViI+PR7169TBkyBAYDMVi/A8REbkAp7Q4V69exZ49e/D6669bbPf19QUAJCQkYOjQoQgPD0f79u2xfft2rFmzBitXruQHRSIiKlLOnj2LEydOYPTo0Rbb3d3dAQA3b97EoEGD0KZNGzRq1Ajr1q3Dli1bsHDhQvOHSiIiIi06cuQIrl69ikGDBllsz0rNcPbsWTz55JPo378/atSogYULF2Lfvn345ptvnBEuEREVQ07pOE1ISECZMmUwYsSIXMuXL1+OEiVKYNasWdDr9Rg+fDj69euH1atXW92HiKiokaGDDNfvGNNCjK4sISEBVapUsdq+zZ8/H/Xr18cXX3wBABg4cCC6dOmCHTt2oFOnToUZKhGRc0hwML9WIdJKnC4iISEBtWrVstoGzpo1C/369cPUqVMBAN27d0eXLl1w5swZ1KpVqzBDJSJyCjYrzueUT7sJCQnw8fGxWr5//360a9cOer0eAODm5oa2bdti3759+R6LIolv9h9Y7SY4aVauyFxukiIJb3l6sBbbbIk595siS1ZvBVfBgKRTrN7gyC3rn/Os8B7/21o8Ks9NQd1Ez40kS8KbeF/bb0Ae7p8huknim1FwE51TUbkJYxbVX8Fd31R82NImdujQwfy3j48PmjdvXiBtIhERUWHKaxsYHByM2rVrsw0kIqJC47QRp3q9HosWLcLZs2dRsmRJDBw4EJUrVwZgyuXWvn17i31CQkJw9OhRq8f00OvgpdfDM7OzNeuno0QLvAj7RQpqYSOVrm5FZ9uKNF46vcVPqO0nKhfEJKkd1+4KFhMuvqX65JjKc9SRyr6qHdcFRbj4mdpjtV7kabBtcShPg97iJwAogpefInjXkVXekYTH1Vt/MKL9APtfy6L9PDiFmmyUkJAAWZbx008/4dKlSwgJCcGQIUPMK07evHkzx+qTwcHBuHnzptVjyrIRRqPpBsD8k3LHelLHOrIN6yl3ssz6oNwlJiYiNTUVs2bNwo0bN1CpUiUMGzYM/v7+SE5ORnx8fI42MCQkBDdu3LB6TKOswGiUYTSavt3P+km5Yz2pYx3ZhvWUO6NsWx8NuS6ndZweOXIENWrUQJ06dXDo0CH06dMHixYtQr169ZCWlgY3NzeLfdzc3JCWlmb1mB82aGzx99wWrQsk9qJmXoO2zg7B5c2r187ZIThHq7zd/ZuBLQsmjmJMVgCjBiZn8H8BxyQkJGDHjh2oUKEC6tSpg+3bt2P+/PlYuXIlKlasaFebePXSBYu/L549WSCxFzWsJ3WsI9uwnvKZ9E/OS5enkTBdRUJCAtavX4+nn34atWrVwoYNG7Bw4UKsXr3a/JzntQ08fy3W4u8TUTH5H3gRxHpSxzqyDespf+kAti1O5pSO0zfeeAOvv/46PDw8AADDhw9HYmIifvjhB3z99dfw9fVFUlKSxT6JiYnmxaNy886xSFxNTISnXo+5LVrjhb/3IiUfvu3X3IhTwai77Lx0esxr0BZjju1GsmwsXiNOZZXjGv8ZcTqvXjuMObHLVEcq+0pGJ72bCR6rakyCfX2u2z7i9JuBLfHGqv1IyTDVk90jTt2sl6ke18VGnFb28cEHjRuJT0wE4OOPP4YkSebFoIYPH46hQ4di4cKFmDRpkl1tYuWwavD08obRaMTFsydRtWZdc/obyon1pI51ZBvWU+5SkpNyfKFDBADz5s2DXq83d44OGTIE3bt3x+rVqxEREQFJknJtAx8fhZpd9UrB8PZ0h9Eo40RUDOqFl4dez5lA1rCe1LGObMN6yl1SSlqOL3RIW5zScerm5ob09HSLbaGhoTh50vTtfFhYGC5dumRRHhUVhfDwcKvHTDXKSM7WUZpiNP7zt6hTxIF+PRFhxyhgyllqdV/BSVVGvSuiHsNcYkqWjaZOQfHcdnE9iYabqfUlCjusCmiIv1pnYkZmRelM90vOkJEsm2IRdkSqhetAR7uQ4LxqHae+V61fqCmw/sVDbh2GKRnGfzpORde36PJWu76Fna6CjlO1y9ve50ZQlsYpKmQjNzc3yNkufkmSULlyZdy/fx+AqX28fPmyxT6XLl1Cq1bWh4XrdHqLThu9Xs9OHBuwntSxjmzDerKk07EuKHePjyZ1d3dH+fLlcf/+fbi5uaFSpUq4fPkyGjX658voqKgoPPHEE1aPqddJFp02er2OnTg2YD2pYx3ZhvVkSa/jcFGtc8rV/Oabb+L11183d/IlJSVh27ZtaNiwIQCgW7du+OOPPxAXFwcAuH79OrZu3YqePXs6I1wiIqIC89RTT+H99983/33v3j3s378fDRo0AGBqE9esWYOEhAQAwMmTJ3HkyBF0797dKfESERHlB0VR0K1bN8yZM8e87erVqzh58qS5DezatSuWLVtmnpq/bds23L59Gx07dnRGyEREVAw5ZcTpv/71Lzz77LPo168fqlSpgmPHjqFy5cp48cUXAZg+JG7ZsgUDBgxAvXr1cPToUQwcOFA4uoaIqKiRFQmyBhLayM5aGK2IePvttzF27FicOnUKISEhOHz4MJo3b47hw4cDAJ588kls374dffr0QfXq1REZGYnXX39dOAuDiKgokSRJMzlOtRKnK5AkCVOmTMH48eOxZ88elChRAgcPHsTAgQPRuXNnAMBLL72E0aNHo2/fvqhQoQKOHDmCKVOmoGTJkk6OnoiocEiSAsneqdCFTNFInHnllI7TKlWqYMuWLYiMjMSDBw/wyiuvoFatWuZySZLw+eef4+TJk7h+/TrefPNN1KxZ0xmhEhERFagGDRpg+/btOHLkCBITEzF+/HiLTlF3d3f88MMPiIyMRFxcHN5++22EhoY6MWIiIqL80bFjR2zfvh1Hjx5Feno6/vvf/6JixYrm8hIlSmDZsmU4dOgQ4uPj8eGHH6Js2bJOjJiIiIobp3ScAqZ8Ni1atBDep27duqhbt24hRUREROQcXl5eaN26tdVySZLQpEmTQoyIiIiocJQoUQLt27e3Wm4wGNCyZctCjIiIiOgfTus4dRn2Lzru2I7CBasEhSpDnyXBvhYLRynZfipqAUFlASgHpiQJd1U5rr3DwFUWTMpaUCmrLiWjBEnO3Mf6ekkqKwypEC0IpsaR0fCifR2ZaaaxWWqiS6nA3gNsICs6yM5JRZ0nsupqeERERPbjVH0iIiquJGjr43VRnKzPT7tEREREREREREREj2HHKREREREREREREdFj2HFKRERERERERERE9Jiik+NUgmXyB1sTQajdR5iL1Ibj5ze1c8rWi6RsDybrd0k25e9U1PKFivI1OS3hhn0nljJU9svKgaqX/vk7K++pIMepWhUKU6DKBVOJfpfE343YnZrSgXCF9eBINQjqX5cu3lUWvRM6MaGMDAlGDWS0kTUQIxERaZiWErxpJU4iItIEneTYsjKFSZHEy8JoFUecEhERERERERERET2m6Iw4JSIiIiIicmGPHj3CvXv3ULZsWbi7u1u937Vr15CSkpJje2BgIEqXLl2QIRIREVE27DglInJRMgBZmNvANQgyhBARETlMggRJI/MUJStz9RVFwfvvv48VK1YgMDAQiYmJeP/999G7d+9c7//BBx8gKirKYtu9e/cQERGBf//73/keNxERuSYtZaspqopMx6mi++dm/js/cpDqBAcRHV+ls0MtH6a9xxUGlb13I+swimm7tX/yctw/r1T2U82tau9pBTlD1XKcShmmn7rM+tKlA7rMRB2S0f63LHv/31d7yv0uF0z+WbvznwLCy1D0lKu+ZgXl+lTrD1ZReaeT7HyzcEaaYyIiItKelStX4s8//8SmTZtQvnx5bN26FW+88QYaN26MsmXL5rj/3LlzLf6Oi4tD//79MWjQoMIKmYiIiMAcp0RERERERAVq3bp1GDZsGMqXLw8A6Ny5M2rUqIHNmzfbtP+nn36KgQMHIjw8vCDDJCIiosew45SIiIiIiKgAXbhwAdWqVbPYVr16dZw7d0513+PHj2PPnj14+eWXCyo8IiIisqLITNUnIipqFEUHWQPfbynQMfEOEREVGEnSUI5TK3E+ePAAvr6+Ftt8fX0RExOjeswZM2Zg1KhR8PPzy5cYiYhIOyQJ0GmjCYQgY6Kmuf4nciIiIiIiIg0zGAwwGo0W24xGI/R6vXC/a9euYe/evRg2bFhBhkdERERWFJ0Rp3oFMCj/LOakV2xbgcmR1V1Evemy+MCKYGeH1kuycQXurMWgJKMEySip7iZc0Ee0r8qDKajRA6JFnLIWf7Jenlk3mQ9MypDMx5MEy4c78ryJ6tDvutrOopWYHDixSLbdsupEkrPVj/C1ITisSjj6dMFhBe9mitqy76Jyfr1EREREDipTpgzu3Lljse327dsIDg4W7vfHH3+gadOmCAoKKsjwiIiIyAp2CRARuSgZkmZuREREBUb6Z7q+q9+sNYmNGzfGgQMHzH+npaXh0KFDaNasmfCh79q1C82bN8/P2iQiIg3RaexWFBXVx0VEREREROQSnnnmGWzatAnz5s3DoUOHMHHiRAQFBaFz584AgKSkJJw/fz7HdP6LFy8iNDTUGSETERER2HFKRERERERUoGrWrIl58+bh4MGD+Pjjj1GiRAn8+OOPMBhMuYYuXryI8ePHIzEx0byPoigoVaoUQkJCnBU2ERFRsVd0cpzqFCiZNwCm37MSmApTQBbQFFO1LmlBXkrFgaXIhHk2lVx+V0w3R/JzCvd10gqoklFUJo7JnLNT+ufvrG06QX5UW4+bG/d40Z4qeWJF17egHgCYXy+5EU2/zn7OrBX+dMZ/6keUMlSUVtWQJtgR4jymwte5yvUtzOErvL7Fx3WUrEgwamAavAzrUxOJiIgcZZ4GrwGiOJs1a2Z1an79+vWxbt26HMdas2ZNvsZHRETaIklO61bJM63EmVcccUpERERERERERET0GHacEhERERERERERET2GHadEREREREREREREjyk6OU6JiIoYWZEga+D7LVE+XCIiIodJ0E4uba3ESUREmiDhn3VFXJ0DS+e4tKLVcZr9n6rHf7dCEa78ApUFZwQHduTCFizYo0b0cKTsi049Xk8FdYUX5CvH1oWw8lKGfxZUkrL9bd6WYf2J1aWLj2tIFpfbS7TolHCVJogvU52goiw6yuRsP7MW1hKc15BivUzWCwKCykJY9i7wBIhXrCqyb/9EREREREREJOL6Q5mIiIiIiIiIiIiIClnRGnFKRFSEmKbqu/68DC3ESERE2iVBgiRpo62R2CYSEVE+YrYa5+OIUyIiIiIiIiIiIqLHFJ0RpxJMSQ6zEh1m/72AcpEqDuQ+dMa30Uq23KlZvys6xZTnVZjjUZw/0hVTQFrkc81RZtu+UmYGZkmR/tkm2Ff9uHbuq5qf087j2nJsa8fNdkFIsmL+mfU7jNbrXxF9XVNQLwu14wqTpzpwXCIiIiIiIiLSLI44JSIiIiIiIiIiInpM0RlxSkRUxCjQRo5TRQMxEhGRdkmShnKcaiROIiLSBraBzscRp0RERERERERERESPKTojTqVsNzz2u0NJOAsoP6oiiEmUblElF6nNctSTuI5EI8qEEalVvWhnlX3tzbuqmovUmPlT+ufvrG26DOv76VPExxXHZL3QkdypOqNKSHZeTorbPzsqmblgFZ2U7XfBvg7kOBXFKyzTqxxXUC48rr2Pk4iIiIiIiIhcnkt0nJ45cwYeHh4ICwuz2B4XF4eYmBhUqFABpUuXdlJ0RETOISvamKqvhRi15NixYyhVqhQqVKhgsT0mJgZxcXGoUqUKAgMDnRQdEVHhk6R/vtB2dVqJ01UdOnQIlSpVQpkyZSy2X7lyBfHx8ahatSp8fX2dFB0RUeFjG+h8Th8TdeHCBQwdOhQ//PCDxfZp06ahW7dueO+999C1a1d89dVXToqQiIiocOzbtw/Dhg3DqlWrzNuMRiMmTJiAPn364L333kOnTp3wyy+/ODFKIiKi/Ld69WpERERgx44d5m3Jycl47rnnMHToUEyePBnt27fHhg0bnBglEREVN04dcSrLMiZPnoxq1apZbN+xYwdWrVqF33//HZUrV0ZUVBSGDBmCNm3aoHnz5k6KloiIqOCkpqbivffeQ40aNSy2r1ixAkeOHMEff/yBoKAgHD58GM888wzatm2L0NBQJ0VLRESUf+7du4dvvvkGVapUsdj+/fff4969e9i+fTt8fHywadMmvP3222jVqhVnXxARUaFw6ojTxYsXw8/PDy1btrTYvmnTJjzxxBOoXLkyACA8PBydO3fmt4tERFRkzZgxAy1atEB4eLjF9k2bNmHQoEEICgoCADRp0gQNGzbE5s2bnREmERFRvps2bRqGDBmCkiVLWmzftGkTIiIi4OPjAwDo0aMHgoKCLEalEhERFSSnjTiNjY3FzJkzsWTJEixatMii7OLFi+jVq5fFtvDwcOzbt8++kzmSZ0G4ApGAQ4siWS9U1A4sWCjIYmGp7ItD6cT7Ze5sPSZH1t4SnVJWWynIvjK1xZayFoDSZfvbvC1NvK+I6FISLvAkWJBKbV+v2+KdjV7WvztJKm3/9yrCRZNECzGpndLOkIweKouf2buwlE5wXFGZjRRIkDWwypRSVJPZFKKzZ89i9erVWLt2LaZOnWpRdvHiRQwdOtRiW1hYGC5evFiYIRIROY8kQdJKW6OVOF3I7t27cfz4cXz00UfYvXu3eXtGRgauXLmS4wtFtoFEVKwwyanTOa3jdOrUqRg5ciQqVqyYo+zRo0fw9va22Obj44OHDx9aPZ6HTgcvnR5eOlMvR9ZPp3Gkz8Tepc5VZO84zVFPjpyyoDpOVV50wo5IQR3qVa56vcG0r6dBb/ETAHSCfSWV/i2doFwnqES1bjNRx6mHSoeh7G796LKboEz/T/16uuktfpr2tX5Oh1ait7ND1qAv/I5TD9ETTpRNVtqa8ePHIyAgIEd5QkKCeaRNFh8fH8TGxgqOaYTRaLoBMP+k3LGe1LGObMN6yp0ssz4od8nJyXjvvffwwQcfwN3d3aIsKSkJsizn+XOhUVZgNMowGk3/JGf9pNyxntSxjmzDesqdUS6gDhMqNE7pON28eTMuX76Mr7/+OtdyNzc3ZGRYjpTLyMiAm5v13piPqje1+HtevXYOx1kczG3cxtkhuLxve7ZUvxPhy+GsJyJ7/PLLL/Dw8MDAgQNzLTcYDHluE69eumDx98WzJx0PtBhgPaljHdmG9URkm+nTp6NBgwZo1apVjjKDwfRR9fEvIjIyMnJ0smZ3/prlF4snomLyIdKij/WkjnVkG9YTFTWF3nGalpaGDz74ACNHjsSRI0cAADdv3kRSUhIOHjyIZs2aITg4GHFxcRb73bp1C+XKlbN63P+dP4QryQnw0ukxr147jDmxC8nO/HbbWSNObZyq76XTY27jNnghco+pnhyK14F9BdSm6kuCWehShmDEabr4vPqUf0acftuzJV7buB8pGaZrSZciOKdaCgDB5ahLF4w4VYnXkan6sqdgqn6Q7SNOvxzeEuOX7kdKuulButqI03S/wh9xWtnHBx80bCw8rxpZkSAX0Aj0/CQ7NGS9eLt79y6++eYbvPXWW/j777/N2zw8PHDs2DE0aNAAISEhOUaXxsbGomzZslaPWzmsGjy9vGE0GnHx7ElUrVkXer2TZ2K4MNaTOtaRbVhPuUtJTsrxhU5ecJZi0RQVFYVFixZh2rRp5jYwISEBly5dwpkzZ1CrVi2UKFECsbGxqFOnjnm/W7duoUGDBlaPW71SMLw93WE0yjgRFYN64eWh13MmkDWsJ3WsI9uwnnKXlJKW4wudPNFQG1hUPxY6peO0Ro0aOHjwIA4ePAgAuHz5MoxGI3744Qc0a9YMzZs3x59//ok333wTgGka4549exAREWH1uKmybNFRmiwbbes4VXti7e0ULKgLRjUe206clSs1CRlIhoMdzA48VtGUerXHqhN0rOqMgrJ0ccCG1MxfMqslJdVo7jgVdbqKOnIBQBJ1nGZYf7Ci/QCg1KlEq2VGT/FL/H4ND6tlSob1EyvZn/TMNjHFaERK1hRFUR5T0VOudn0LOolTAwR5eFXyjQo7bO28vlNUEwcTmaYhNmrUCFu2bDFvu3TpEm7evAlJktCgQQM0a9YM+/btw1NPPQUASElJwYEDB/DRRx9ZPa5Op7fotNHr9ezEsUFRraf7idYbr0AfwTdduSiqdZTfWE+WdM5OoUUuKTExEU2bNsVvv/1m3nbr1i3s2rULkiShVq1aaNasGfbu3YvOnTsDAO7cuYPTp0/j3XfftXpcvU6y6LTR63XsxLEB60kd68g2rCdLel0R7U0sRgq949TX1xc//PCDxbZPP/0UDx8+NH8IHDp0KBYsWIC3334bXbp0wYYNG5Cenm51GiMREZEWVaxYMUeb+OabbyIsLAyvvfYaAGD06NEYMGAAPv30UzRu3BhLlixBxYoV0alTJ2eETERElC/q16+fow0cMWIEBg0aZF4UcezYsRg1ahRKliyJsLAw/Pjjj2jfvr3FCFQiIqKC5BJfA1SsWBGhoaHmvwMDA7F48WK4u7tj8eLFKFmyJH799dccicGJiIiKmvDwcJQvX978d6VKlbB48WLEx8djyZIlqFWrFn744QfouAAZEREVMbVr10bp0qXNfzds2BA//fQToqKisHz5cnTs2BFffvmlEyMkIqLiximLQz0ua/phdhUrVsTUqVOdEA0RkWuQIWkif6gWYtSSV199Nce2GjVqYNq0aU6IhojI+SQAkkYSvGkjStc1efLkHNuaNGmCJk2aOCEaIiLnkyBpqA3URpx55RIdp05VQAsbOcSRa030eKRcfpey3eylkj9SRBEtZqUSlCLI/SnKo6lTyUVqUTdZPzN/F+XCVKtCnVGwAJQgptK7VBJJuwvy08lqiyIJcpwKcrHI+py/y3pAznw+RQsqCctUKjEt0PrjkQ2Cx6oyME+YA9XO/KeKKB4ionx04VaCsPyZHw9aLRvUupJw33rBPgAASZFRAcBf52KhSKY3Rr1k/Q2yUYUA4XFL+VpfEZuIiIiIyFWw45TIiUZ1bYKeTaoKO7wNif/0qm7fcBIrfthZCJEREREVLr2kQK+ofbv5D6NkgFHtGzciIiINUAyeUPKQSVGCDCkjpQAjIqIs7DglcqLgQD/UrFIeD/YuhDHlkdX76T394N86AicOXy/E6MjZFEWCrIFOAaWITskgosKlkyToDe42t4mKUYFgMgcVIZJkummBVuIkItciSXpIbl42t4FITy7E6MiZTG2gNhoXjYSZZ+w4JXKin/84hKHt6iE15hTurLOevzCoz9vISEvHink7CjE6IiKiwpMuA3rJaFObqMhGpMtcII2IiIqIjGQoeneb20Apgx2nRIWF/3ESOVFcfAJ+23MKAT3/DZ2nX6730Xn6IaDHRKxfegB34x4WcoRERESFx6joUKr3f4RtYqne/4FRlHSciIhIaxQZkjHNpjZQMqaJFwshonxVdEacPr7Ika0LHjlripdk54kdmbab/ZxZv0uK6ab2+UO0eI4jw7EFixep5nixc02kDC/xYfWZiyLJma8O2QMwZi2AJHjF6AWLHgGAPjX37T9tOoAhrWujZNdXc/12sWTXV6Hz8MPy6VsgJedykAzrq2TdbV1SHJSIqPqzl2V/zeke25YL0SWcFqCymJWdizipLdRk9+JQIg4smpZF1shUfZlT9YnyRWqG9Q9BUbGJVstGzNwrPG5czB2rZTNv3Bfu6+1rajS9DBJ+HlYG/1t0HMkZpvc3vd76G6ROsMAgAPxnUM3cz+emx/AGZcVtoqcfUgQLRFLRI+kk1WvKVUgaiZOIXFBGMnSe/qptIFIfOCE4chodtDPkUStx5lERfVhE2hEXn4AN83choPvEHN8uZo023bBwH+7GsoEkIqKiLSndaHXUKUebEhFRkSYYdcrRpkTOw/88iVzAsq83Qufpi5JdX7XYnjXadNnMv5wUGRERUeFKl00fEHNtEz39kM7Pi0REVFRlJAvbQDC3KVGhY8cpkQu4c+N+jlGnOk8/BHTnaFMiIip+Hh91ytGmRERULOQy6pSjTYmcqwjlOFX+uWX/W3W/gg3LLqKY1OK1NRep7rGfenFdSXrBG3QB1aGiEpPiZv3EwjJ38YcuOdW0r0Fv+pnuJyPdaHr8Uob14xrThYdFxR/PWy/08MCymX+h1+j25pw2Wd8qLpuxBTAWUDI3US5SUc5QXc7fFV223+28JtQ+DyuCdyxRHlO1HKfC69/e/L5ujuc4VRRoIseps1JFE2lN7AMrya4zvb3hjNWynfuuWC0zuIv/nctIz7CrDAAyMvNoZ0im96IMoxEZmTlOFcGLXxEVAhjRqJKwPF0GPDNH3GRvE5nbtHiSJNNNC7QSJxG5sMdynTK3afEmSRIkjTQuWokzr4pOxymRxt25GY8Nv+xBj6ETEb/nZwR0n4j1v+zB3VtsIEm7Tp48iZ9++gkxMTEoU6YMRo0ahaZNm5rLDx48iF9//RW3bt1CcHAwRowYgVatWpnLz549izlz5iA6OhqVKlXCyy+/jKpVqzrjoRBRIcsadRq/52eONiUiouIj26jTrDaQo02JnIf/gRK5kGXfboHO0w+VJ20zjTadvtnZIRHZ7ezZs4iIiEBoaCgmTJiA8PBwjB49GufPm0ZfHzx4EGPGjEGjRo3w73//GzVr1sSYMWNw4sQJAEBsbCxGjhyJkJAQTJw4ESVLlsTIkSNx7949Zz4sIiokWblOs9pE5jYlIqJiIzPXaVYbyNymRM7DEadELiRr1Gm/5ztizQ/bcffWA0gGvbPDIieRIWliqr5sZUrGsWPHMGDAALz6qim5fbNmzbB161bs2LED1atXx6lTpzBmzBiMHj0aANC0aVNs3LgRBw8eRL169bB06VLUqVMHb731FgCgRYsWOH78OFasWIGxY8cWymMjIucyKjp4hFRHhpFJQYozCRqapuiSecCISHMyR516hFQH0pM52rQYk6CdNDAaCTPP2HHqCEdykdp9TrUPDoITZ8/VmPW7TgGgQDKI34h1ghyQojRmkig/pArJgatTlq3Xg5wuHmhtzMyBasy8m9HXCKOcmVRNkONUMoqfdMlg2wNa8u0W+AZ4Y+m3WyDpbRgULngCVHOG6gS5YAX9tao5Tm3Mj5qjTCUXqSjvrXBftRy+7taT5kmCfUXXt86DifiGDx+eY1t6ejo8PT0BwNxhmuX8+fOIjo5G/fr1AQCRkZEW0/oBU+dqZGRkwQRMlA9EHXyT1lvPYQoAm/44ZbUsLSXNapmS+FAclM76G3qCSg7trByoGe46AGXx6P4jJKWZ/mdwc3ezup+vv684JhuZRpkqHG1KRETFT3oSFEmClJ7k7EiIijV2nBK5mPtxD/F/ry1wdhhE+W758uW4f/8++vTpY7F93rx5WL58OZKSkvB///d/5s7SO3fuoFSpUhb3DQoKwv79+wstZiJyPnaaEhFR8aRASktwdhBExR47TomIqMCtWLECn3/+OebMmYPAwECLsm7duiE8PBx79uzB1KlTERoaivDwcGRkZECvtxwpp9PpYFQZIUdERERERESUH9hxSkTkomRIkDWQKUYtxq+//horV67Ezz//jJo1a+Yor1y5MipXroxOnTohLi4Os2bNwueffw5/f388/H/27jusqesPA/h7kxD2EAVUxL2toyruvUUFrXVv/dVZO7XT0dphW22drVr3rLt11lUV96jWva0DF6KAgIxAcn9/ALdEyE0II4P345NHuOfem28OSU5y7jnfE6M/BTk2Nhaenp65Gj8REVk3QbCh/G42EicREdkIwXbyfNtrI2hCAkUiIiLzzJo1C7t27cK6desydZpu2LABx48f19vm4+ODuLjUKUmVKlXCjRs39MqvXLmCKlWq5G3QREREREREROCIU+PMXQDK6CJOZpJZzAeA7EJBGReAEtLOI6h0EHQ6KIwtDpVXj0fmvMYuVihMXHjKx8kN419rg2mX9iEiMQ5alfyJderUqcHKtPMrXVOgTFscSm5hKVFm4SgAgJOj4bIcXJl50q64wTKd0shjNbyuB3Ryi0MpM/8sKuUXCpPIhCQ6mL84FGSeD4KD/PNbJbM4lEJp+Fi5P5tKxenkZ86cwerVq/HHH3+gWLFimcqvX7+O5cuXY8WKFfD29sbjx4+xZ88eDBo0CADQpUsXDB8+HFeuXEHVqlVx5swZHD58GBs2bMjvh0Jksj3Xww2WbVn8h/zBDk6Gy5Ryb9hG3m8SUkduFy3sjm/HdMBnP+/Ck+exqWVeRWUPTUlb2DAl7Vp7iiYFKWmLQylVhhsKOx1wQERENkuAqHaFoHkJII++2xKRXWLHKdm9kZWbIKRUDcQkJ+Lr87ssHQ6RyURRgE60/t4H0UCMy5cvR3JystQRmq5Zs2aYOHEi3n//fYwfPx4tW7aEj48PwsPD0blzZ2n/wMBAjBo1Cr1790aRIkUQGRmJTz/9NMvp/kRk3MeDWqBP+1qIjk3ABzO2WzocIpMJNjRN0VbiJCpwHFwgqJxSR3okv7R0NESmEyA/aM+a2Eqc2cSOU7Jrfs7u6FG6Nm5ExKFnmdr49fpRPH7JlQmJ8sP48eMxatSoTNvd3d0BAK6urvjll18QExODZ8+ewc/PD66urnr7Dh8+HH379kV4eDiKFi2aqZyITOPv44EhwXVxIyIOQ0MCMX3lITx6FmP8QCIiIlsnKKBVOuJ2RBzKFXaBMiUBEOVnpBERpWOOU7Jrwys1RlySFi1/OY64JC2GV2ps6ZCICoyAgABUqVIl061EiRJ6+3l4eKBs2bIGO0Xd3NxQrlw5dpoS5cC4Ac312sNxA5pZOiQiIqL8oXJGbFobGJukBVTOlo6IiGyI/Yw4FaA/hPnVn809rVwezZzk/TRz+q1oJB+LINMVLmTID5n+uASlCEEQoVAYyXEqUw+GpumaQshBHcodq1Jp4efkgR6la+PL3TfxKCYRPx78F5Pb18bCG0fwNDHW4LHatIejTJtqpVTqoFSk5Y+TuU8dZJKCAhBdZHLXydHK/23k8piKRl7hool5TDOVZbzLDK+z9O2i3CUZuRynKiPPB7nXo0z+U0EmTykg/zpXGjnWkDzLC0xEFhcRk2Sw7MOlZwwf6OIlf+LkRMNlSpk39Jinsqf193LEkC518eW+tPbw0L+YHByI6Qt34FFMhOyxKSma1P8dUxuFlBfPkZKUnlO1iOHjPJjnmYiIrEDaaNMf9l7Ho5hE/HDgFr7qUImjTonIZBxxSnZrWIWmiEvSYu6RuwCAuUfupo06bWLZwIhMpEvLcWoLNyKyXuOGtEWc5pX2UKPFuCFtLRsYkanScpzawo0roxFZmbTRphnbQI46JVti6XYt2+2gHWLHKdklPycPdC9ZBz8e/BexSSkAgNikFPx48F/0KF0bvk7uFo6QiIgo7/n7eGJIt0b48dAr7eGhfzH0jcYoXoTtIRER2an00aYHbum1gT8cuAWt0lF+uiYRURq+U5BdenW0aTqOOiUiooJk3MAWeqNN00mjTvs3tUxgREREee2V0abpOOqUiLLDjnKciqn5D9NzFSpEIC0fqOxoYbmciTCSgzMno5BF83KG5uQuM+ZxlHKcKkQIEGVzmALydSjzUIzS6eROLP9odbqs77ioszu6l6yDL3fflK4spksfdTq5fW3Mu3IM4QmZc52KutTrCTqFIMWoS9+WYvhag5giH2+Kp+GGWZCpxKd13GTPK5dPVGfkFS5Xbmr+0/Q8qqIKUgZeuT9don+y4UJjl3Jk8pjK5Z81lofX3Oew7HtLLsxSEEXYxDT4nLwHENkiHw9Hg2WXvg8yWNbom/2y571z46HBspREw3lV4emX5Wb/Im4Y0rUBvtx7I+v2MD3X6abzePQ8Lutzq13S/k97g1Y7SznhHJ0N14NSKZ/3myg7bGkGvK3ESWT3MuQ2zaoNZK5TshVsAy2PI07J7oyo3DjL0abp0kedjqjcKH8DIyIiykfjetVDXFKKkfYwBeN61cvfwIiIiPKagdGm6TjqlIhMZfKI0169eiEiIuuVV728vFC/fn2MGTMGbm7yo+OI8lJRZ3f0LPt6lqNN02UcdbrgWtajTomI5LRq1cpgWeHChdG8eXO89dZbcHQ0PCKPKC/5F3HDkI41shxtmk4adRpUA9PXnTI86pSIKE1ERAR69eplsNzX1xcdOnTAgAEDOPKcLEdmtGk6jjolIlOZ3HH6+PFjBAcHo0iRIpnKXr58iZ07d+Lu3buYN29ergZIlB3/q9QQjkoVqhV1x7zu1Q3u5+6kgqNShf9Vaohvzu3JxwiJyB48fPgQH3zwQZYdoy9evMDmzZvx/PlzTJ482QLREQHvvxkIRwelae2hgxLv96iL8fMP5l+ARGSTtFotnj59inHjxmVZ/vz5c/z6669ISkrCiBEj8jk6ojQqZygVCpPaQKVCkTrqNPllPgZIRLYkWzlOg4ODUbFixSzL+vTpg6ZNmyIxMRFOTk65EhxRdj2Jj8X5549RvZQCgDrLfcS05JPnnz9GeDxHm5L10omCTeQ4tYUY80KfPn3g4eGRZVn79u0xZMgQdpySxTx4Fou/bzxBJTcV4CKTmUmhwN83nuDhM442JeslABBsJHGabUSZMw4ODhg8eLDB8jp16mDWrFnsOCWLEUUtkpM16FHd1+i+mmQNHERtgXjtkm0SINhQG2gbcWaXyR2n06ZNg7+/v8Hyly9fQhRFy/1BBf2kuXoJdGUWjZFd/AmAIJcF1sixsuQ6GgwsegQYX+RG7vEoMpQpMvyvEESjSXzlzit3rOziT/hvIaYsy8xY0GfBlVNYcOVUNpMS608j0qUt8qRVpMamTVZAm/Y3ERMNTzkSkuRTBqe4yxTK/M11DrKnlV0cSjTyCjd7YakM9Zt+DlGh/7NZjL2mzHwewshpdTLPQ63WzOe+0j4bDUq1cOFCuLi4GCyPi4uzmQ84lLuUCsN/95MTW8seq0kxPE1wy+VHBstWn8y8qNRZDXB2710AwKH9lw0e6+Ty38VuJ9+imcrLVSqeWpbWJlSpWxGJabMe937QzOB5H0QmGCwjItvl7e2Nn3/+WXafly9fsg0kixJSEqFGoqXDICI7YXLHaf369Q2WLV26FEuXLkWbNm1Mzud29OhRrFy5Ek+ePEHRokXRt29fNGv23wfwS5cu4ZdffsGDBw9QsmRJvP3226hcubKp4RIREeWZjO3Vq2bMmIG1a9ciJCTE5PP9+eef2LhxIyIjIxEQEIDBgwejdu3aUvnx48exePFiPH36FBUqVMB7772HgICAHD0GIiKi7FKr1WjUKOsFVjUaDX744Qds2rQJY8eONel8oihi/fr12LlzJ2JjY1G2bFkMHz5cb5bj7t27sWbNGkRHR6N69ep4//33Ubhw4Vx5PERERMaYOy5Mz/3799GzZ09MmzbNpP2PHz+OMWPGoHnz5pgyZQoCAwMxatQonD9/HgDw6NEjDBw4EBUqVMCUKVNQunRpDBw4EM+ePcuNcImIbIIIAaJoAzc7nZJhrrCwMIwaNQoff/yxSftv3boVkyZNQnBwML788kuULFkSQ4YMQVhYGADg8uXLGDFiBBo3bowpU6bAyckJgwYNQmIiR1IQUcEgCLZ1K6jUajXu3LmDzz//HEOHDjXpmIULF2LevHno168fJk+eDEdHRwwYMAAxMTEAgNDQUHz00Ufo3LkzJk+ejKioKLz11lsQs5p6RkRkjyzdqLERzF6OU0Oym8Pt9u3bGDlyJPr06QMAqFGjBrZs2YIzZ86gZs2aWLduHWrVqoX3338fAFCrVi2cOXMGGzduxMiRI3MjZCIiojzx008/ZWv/u3fvYvz48dII1Ro1amDDhg24ePEiAgICsHLlSnTq1AlDhgwBANSsWRNt2rTBrl270LVr19wOn4iIyGyLFy/O1v6PHz/G559/jrZt2wIAKlasiI0bN+LmzZuoU6cOli5diqFDh6JHjx4AgCpVqqBx48Y4ceIEGjZsmOvxExERvSpXOk6zq3///nq/X7p0CWFhYahVqxYA4Pz586hbt67ePnXq1MG5c+cMn1QQ/7tl/B2Qz9JurENcLqeikUPlmHuN1FhOVvnHKmb+WRABiMbPKxuT2YfK1oNoONVbarnW8IBpmZSh8vllASA5tVxUpOYzFROVUiyCxvB9Ciny542qkPViVQCQLJP/VNDKntb8PKUARMMpW2XHo4uK/ypYVIrS/+lX/2WrWK7Q2NNQ5jkhd1rZXMUARK3hg1NguJIUMq8bnTJXBvRTAfDOO+/o/X7kyBEkJCTgtddeAwCcO3cO//vf/6RyQRDw+uuv459//mHHqZ1Rqwy/b/SoWcJgWcdKmXOTZuTyluF0S8akp2zVarW4fvk8dr3XFEqlXOORqryfq9n3SUQFx6sDcP788094enqiXLlyAFK/F2ZsA52dnVGtWjWcO3eOHadERJQvLNJxmm7BggX47bffkJKSgh9//FHK5xYREZEpb02RIkVw7Ngxg+dyEpRwVqTeAEj/A5DtTBQU8j01cuU56jiV6eXJ0cQTmaCUGXo4nQSl3v8qwUgvpQy5abpaI72qOplFNIwvDiW3sJTcgUb+cmkxZfVcEmQ6wwQjC2E5ORg+VinzSjTWMZ2jxaFyoePUKe0LtFOGL9Jy1S/bn2jk9ShbnqPXueGD5Y6VeyiOMh2uRFn5+uuvsWvXLjg5OWHp0qUoWbIkAMNt4v379w2eS6fTQqtNvQGQ/qes2Xo96YzErZW5OGSMmKHjNOP/lDXWU9Z0OtYHyXv//fdx4sQJ+Pj4YPXq1fDy8kJcXBzi4+MztYGFCxdGRESEwXNpdSK0Wh202tTvN+n/U9ZYT8axjkzDesqaVnZUF9kCi3acdu7cGdWrV8fRo0cxYcIELF26FJUqVYJWq4VCod8lIQgCdDrDL8Cvy9TT+31hxRZ5EbLdmV9GfoVfAhbVaJo7J8o6j77d+KUVr/rnNhECdDaQP5Q5TnNH//790bx5c+zZswfjx4/HmjVrULRoUeh0umy3iff+van3+61rl/IkZnvDejKOdWQa1lPuEgTBZlZpt5U4rc3o0aPRtWtXbNq0CR9++CHWrFkjtXOv1qlCoZC9OHHjfrje7xdvP8z9gO0Q68k41pFpWE+5y5ZSh9pKnNll0Y5Tf39/+Pv7o1GjRnj06BF+/fVX/Pjjj/Dy8pISgqeLiYmBl5eXwXNNuHMKd5Ni4axQYmHFFnjrxkEkpF/dtrURpzm5QCM34lT534mdBCXml2mNkXf+QqKohUqVgxGnMo/F2CgXnVw9GDlWbqp+jkacJv834nRRjab434XD0nNJSJYZcZosf173O4aPTXYzfFxOpurn14jTX1o1xOj9x5GY9iFWrvo1RWQekNJCI06Vhp//gkxMciNOS6nd8WVAA9n7JcqodOnSKF26NJo2bYo+ffpgxYoV+Oijj+Dp6ZntNrFU2QpwcnaBVqvFrWuXUL7yayZNry6obL2eXiamyJY7O5r/cS/jVH1brqP8wnrKWmJCfKYLOkQZVahQARUqVEDTpk3Rrl07bN68Gf369YODgwNiY2P19n3x4gUCAgIMnqtiST+4OKmh1epw8fZDVC/nDyVTKBnEejKOdWQa1lPW4hM1mS7okG2xSMfpb7/9hoCAADRp0kTa5u3tjYcPU69MVKpUCdeuXdM75vLly6hSpYrBcyZCiwRRK/XYJIhpvwMQ5LpxjPQXyh2bZx2nORjJLZerVJnhwabfexK0SEQKlEZ6a2VzoMpUhFx+SADQ6mTylMp0jAKAKPc9Ue7hGJuymF6e9m0xIVmHhPQr3lqZ54ORqfrJhQ0HpUyUmSqek6n6xo6Ve67JddhmMd0gUatFQorWaEyaFJkTG+u/l+tYlXuOGmm3BZnnvyBTSXIdsgk5ugJCBcmvv/6K+vXro2bNmtI2b29vvHz5EgBQuXJlXLt2DV26dJHKL1++jG7duhk8p0Kh1Ou0USqV7MQxga3Wk4dr/sVsq3WU31hP+hQK1gVlptVqMWfOHAQHB6Ns2bIAUkeTenp64uXLl1AqlShfvjyuXbuGwMBAAIBOp8O1a9fQs2dPg+dVKgS9ThulUsFOHBOwnoxjHZmG9aRPKZMWjmyDRZ7Nd+/exTfffCPlpgkLC8OuXbvQoEHq6KyQkBD89ddfOH/+PADg+PHjOHr0qOyXRCIie6MTBZu5kfkuX76M7777ThpVeuXKFRw7dkyvTdy0aRPu3LkDANi2bRvu3buHoKAgi8VMRJSvhP+mKlr7jdlrTKdUKnHixAn89NNPSEhIAAAcPXoU165dQ/36qYvade3aFcuXL5e+Ny5ZsgSCIKBZs2YWi5uIKF8J/6WssfabvbaBFhlx+t577+Hp06do1aoVvLy8EB0djTfeeAP9+/cHANSuXRvvvfceBg0aBFdXVyQkJGDy5MmoWLGiJcIlIiLKM5MnT8ZHH32Exo0bS6lqhgwZgo4dOwIAOnbsiMuXLyM4OBju7u7Q6XT48ccf4ePjY+HIiYiIcuaHH37ARx99hIYNG8LNzQ0JCQn45JNPpEWDBwwYgOvXr6Nly5Zwc3ODk5MTZs+eDWdnZwtHTkREBYVFOk6dnZ0xY8YMJCYm4vnz5/Dx8YFardbbZ/DgwejduzciIiLg5+eXqZyIiMgeeHt7Y9GiRXj58iVevHgBX19fqFT6zfO4ceMwatQoREZGomjRonBwcLBQtERERLmnZMmSWLt2LWJjYxEXFwc/Pz+9BRGVSiWmTp2KTz/9FDExMShevHimBROJiIjykkUXh3JycoK/v79suVzibyIieyaK8vmQrUVO8jLTf1xdXeHq6mp2ORGRvZKmANoAW4nT2ri7u8Pd3d1guYeHBzw8PPIxIiIi6yClgrEBthJndlm04zQ3CRAhCKK0iFHGn3O2ipPMIk5yi9EYO62Zhxp7IipkYlJkWMgmfT+FQoRCFPXKsr5fw+VyHTvGzqszsmK8LLlTyy3UlIOOKFFmcSK5MgBQvDD8chNlXolGF3iSKzfyHM2NPjkxw/+mnE+QXRjNyAtDbmEv2cEHRs4rl7BbLia5B8zORCIiIiIiIiKbxnkORERERERERERERK9gxykRERERERERERHRK+xmqj4Rkb3RiQJ0NpDj1BZiJCIi2yXAdvKm2UiYRERkM2woyamdtoL203EqZLhB/+ccPcfMzWOaVx0JRuJRKHUGy5QZyhSCTtpfKeqgUBg+DpDPYyqX/1SuzJRys8mdNid3KZtPVP5QUSbfa571Oxk7r7n3m/GhZPGakz1vTv42cueVy2trhCj39JfJfyowkSkRERERERGR3eJUfSIiIiIiIiIiIqJX2M+IUyIieyMKsqO9rYYtxEhERDZLEAQINjJN0VbiJCIi2yDY0Ex9W4kzuzjilIiIiIiIiIiIiOgVdjPiVFAAgkKEkJZHUlCIuZJ/0NxUjXlFYSzHqUwezYx5TKUcpwodFKLOaKpLZnJMI5vjVL6WdA5m3mdeVr65T/AMx4mK//4Xxczl2bpPY0yMKVvHmVJORERERERERAUOR5wSERERERERERERvcJuRpwSEdkbHQTobCB/qPEx60REROZjfjciIiqomOfb8jjilIiIiIiIiIiIiOgV7DglIiIiIiIiIiIieoXdTNUXMtzwys+2RnZ0cw4WhxKy+Dm9noytjaPTGQ4qJUVp+D6NxCtaYhpyXi0EZOQyhGjuZQoLLVwk9zzMGFJWi0OleGjNvNMcltsZUcyw4JYVs4UYiYjIhtnQNEXO1SciotzEqfqWxxGnRERERERERERERK9gxykRERERERERERHRK9hxSkRERERERERERPQKu8lxmldkU/fJ5OfMUc4/mbygcjlMAeM5RQ3R6eT70LVaw+U6mTJtivx5FUqZeHNShznJnSpk8b/0s0xQxvK5qiyQCNLYXco9h029C+V//0vH5NUlGTOf3znJnSr7mpItM3KfJhAhQGcDiV1FG4iRiIhslyDYTupQW4mTiIhshA21gfb6tZAjTomIiIiIiIiIiIhewY5TIiIiIiIiIiIioldwqj4RkZUSRUDMSdqJfJKj1CRERERGCAAEG5mnaBtREhGR7eBcfUsrEB2n5uYpBXLQIWDsOJm7VcjkTVQqdbKnlTtWzOJnMf1mJF6dznDAOq1MnkyZMgAQ5eKVuc/UOzbzRWlqvsssc5zKHGdk/LbO3FebuXk9AaPPb8HcJ3iGw8S0PLWiSvzv7ymXi9cS76XGcgPLvK4EmTy8cq83uTIiIiIiIiIisn6cqk9ERERERERERET0CnacEhEREREREREREb2iQEzVJyKyRTpRgM4GcpzaQoxERGS7BBtK72YrcRIRkW1gG2h5HHFKRERERERERERE9Aq7GXGavvp0+grUGX82dpz8DnLHmt+dLsidWG7BGSOL3MgtSKOVidfYYxF1hvvYRa1M/7uRxaF0SQ6GCx3kF8KCXHFercsj93CMLQYks8iQ3HnlFtAyzsixcs8JmfsVMizMJS0O5SBCTH9+GvvbmcvM15xg7HWjknvNGX4sSrmFo4ws5EZERERERERE1s1uOk6JiOxN6gUhS0dhnC3ESEREtksQBJuZ/ifYSqBERGQTUqfq20bbYiNhZhun6hMRERERERERERG9gh2nRERERERERERERK+wm6n66VNa06eMmjzF1VgKyLxaLVrmtHLDmwUj+S7lyoUMj0XI8L8AQKczkotUplxMkTk2xUjfvNzDMZIf1dx8l0bzn+Ykj6nc3crl2ZS9T7Pv0ihRMDMPp4v2v5/TH5drCqBL3Z5X+T3lXo+yz325/LIAVCqtwTKlzGORyzksd5zpTMvVbHm2ECMREdkqTtUnIqKCKnWqvqWjMI2txJldFus41Wq1OHLkCB48eAA/Pz80a9YMarVar/zQoUN48OABSpYsiaZNm0Kh4ABZIiKyPxqNBocOHUJ4eDgCAgLQpEkTvTYvKSkJBw8exNOnT1G+fHk0bNjQgtESERHlnpcvXyI0NBTR0dEoV64c6tevr1ceFxeHgwcPIjo6GtWrV0fNmjUtFCkRERVEFuk4jYuLw6BBg5CUlITXX38da9aswYwZM7Bu3Tq4ubkhOTkZQ4cOxdOnT1G3bl2sXLkSJUuWxK+//srOUyIisisRERHo27cvPD09UaVKFSxZsgRFixbF8uXLoVKpEBcXhz59+kCpVKJatWpYtGgRGjVqhKlTp1o6dCIiohy5ffs2Bg4ciLJly6JkyZL4+eefUbduXcyaNQsAEB4ejt69e8PPzw9lypTB3Llz0adPH7z77rsWjpyIiAoKi3Sc/vbbb3j58iW2bt0KtVqNxMREtGnTBjt27ECvXr2wbds2PHz4EFu3boWbmxuio6PRuXNn7N69Gx07drREyERERHliwYIF8PPzw8qVKyEIAiIjI9GiRQscPXoUzZs3x4oVK+Dg4IC1a9dCrVbj0aNHCAoKQo8ePVC7dm1Lh09ERGS2GTNmoEGDBvjxxx8BpHakBgUF4dq1a6hcuTJ+/vlnVKxYEfPnz4cgCLhy5Qp69OiBN954AwEBARaOnoiICgKLdJz27NkTwcHB0tR8JycnlChRAs+ePQMAHDx4EG3atIGbmxsAwMvLC61atcLBgwcNd5yKGW7Q/9kSOQKN5iKVyY0omzcxBzlO5RirI1EuV6lMLlLBWJ5SuTSQGvnRxaJj3uTRlCWXf1YuhykAhWeywTJtrIPMgUb+pnn19JZ5LinU/+UETR8ErlBroUjLcWpuSOY+fwH5+jeWb1Qux6lKYfhYuXgdlIbPaSpRtI0cp7YQozUbPXo0RFGU8uJ5e3vD29tbahNDQ0PRuXNnqc0sXrw46tWrh9DQUHacElHBYEP53Zj2O3smTZqkl66tTJkyUKlUeP78OYDUNvDjjz+W2siqVauiXLlyOHz4MPr27WuRmImI8hWTnFqcRTpOPT094enpKf1++/ZtXL58GRMnTgQAhIWFoW7dunrHlChRAvv37zd4TkeFEs4KJZwUSgCQ/gdsr+PUQebJ5mjkvAqZxX5ShP/qxElQ6f0vCPJPBVWG+nyVKJM+QTCWWkF2USQjnbkKmXK5ajLWN5cWsnPaY3bO+Nhl7lOQiwfyHeJamfq1xo5TZYaYnNKeV06CUqo7sztOzTwOkH/NKY0sgqWUea6pZI6Vu0+1IPM3JcrA29tb7/dTp07h+fPnaNy4MQDg/v37KFGihN4+AQEBuHfvnsFz6nRaaLWpNwDS/5Q11pNxrCPTsJ6yptOxPoD/8lVHRESgQoUKmXJ5ZuXs2bO4evUqfH190bx5c71ORnvg6+ur9/v27dvh5uaGWrVqQaPR4MmTJ1m2gffv3zd4Tq1OhFarg1ab+hku/X/KGuvJONaRaVhPWdPqzB8cRNbBYotDpXv48CFGjx6N4cOHo1q1agCA+Ph4ODk56e3n5OSEhIQEg+f5upT+B4+F5VvmfrB2aGax9pYOweotqtnE0iHYhF/LtrJ0CEQ279q1a/jggw8wceJEFC1aFACQkJAAR0dHvf0cHR1l28R7/97U+/3WtUu5H6wdYj0ZxzoyDeuJXhUTE4O+ffvCwcEBVatWxYIFC9CiRQt89dVXBo/57rvvsH37drRp0wabNm3C/Pnz8dtvv9ld52m6U6dO4euvv8Z3330HV1dXvHjxAgAytYFOTk6Ij483eJ4b98P1fr94+2HuB2uHWE/GsY5Mw3oie2PRjtNLly5h5MiR6Nu3L0aPHi1td3JyQlJSkt6+iYmJcHZ2NniuCfdO4l5SLJwUSiws3xJv3TqAxLSr21Y54lQpM+JUZfgKjaPa8HRvAFDITCtO0eqPOJ1ZrD3ee7wbiWIKkpJkpooDSE40/FQRk2RGnMpN8QfkR3/qjIw4VctcycqlEaeLajbB/84fQUL6SAkHmdGHMn83AFCoZEac2thUfaVLivSzk6DEr2VbYfi/+5Eo5nCqvpnHATmbqq/Mg6n6JVSe+KRwc9n7NUYnCtDZwDR4W4jRFhw9ehQffPABxo0bhx49ekjbHR0dodFo9PZNSkrKdIExo1JlK8DJ2QVarRa3rl1C+cqvQankKGhDWE/GsY5Mw3rKWmJCfKYLOtkhQLCZ2X+CgU8zy5cvh4uLC1avXg0HBweEhYWhU6dO6NGjB2rUqJFp/3PnzmHVqlXYuXMnSpYsCY1Ggy+++AKPHz9GqVKl8vph5Ltt27bhq6++wtSpU9GqVerF+PR2LqvvhUWKFDF4rool/eDipIZWq8PF2w9RvZw/lEouMGwI68k41pFpWE9Zi0/UZLqgkx2pbaBtNIKG2kBbZ7GO03PnzuGtt97C559/jq5du+qVBQQE4MGDB3rbHjx4gJIlSxo8X5JO+1/nFoDEDL9bZcepTHmKaLgTRxRTDJYBgEKU6TgVM99nopiSdpOvo2SZTkxRZui5YGxYulx/lrGOU10edZy+IiHjc0vmPgW5eAAoZOpCq5NrWKyw4zTjlLu00BPF/+rJIjlOs3h+p1PKvC4AQCXzmlPJHCvI/G00MucketX+/fvx8ccfY/r06WjeXL/DPas2MSwsDJUrVzZ4PoVCqddpo1Qq2YljAtaTcawj07Ce9CnkUhIVEAcPHkRwcDAcHFIvlgcEBCAwMBAHDx7MsuN0586daNSokfT9R61W49tvv83XmPPL+vXrMXPmTCxatEivLhwdHeHj44MHDx7obQ8LC0OjRo0Mnk+pEPQ6bZRKBTtxTMB6Mo51ZBrWkz6lkZR+ZP0s0nEaGRmJkSNH4ssvv0RQUFCm8mbNmmHRokV4//334eTkhLi4OOzfvx+fffaZwXOKEPQWUsmtRVXkOnIEmfcCYx1ACpkRcHKj44ye18yHLBrtpJQplysz0ndkdPEouWPjDf8BdM4yHWXG3sPT6zjj/9LPMocZHRkqUy53rMzoZGMxGe0kls0xa9oITmXaC0GpEKVconL5XGVfU0YvOBgukxttbWyhJge516PMeeUWa3NWyl/kIEp3584djBs3DvPmzcsy312zZs2we/duDBw4EEqlEuHh4Th16hRGjhxpgWiJiMgcYWFhmXJ1lihRwmC+6ps3b6JWrVq4ceMGTp8+jSJFiqB58+aysw1s0d9//41p06Zh9erVqFixYqbyZs2aYdeuXdJ3xmvXruH27dto1qxZfodKREQFlEU6Tn/55Rc4ODjg9u3bmDNnjrS9XLlyCAoKQteuXbFu3ToMGDAAjRo1QmhoKMqVK4cOHTpYIlwiIqI88+OPP8LPzw+nTp3CqVOnpO01a9ZEs2bNMHDgQGzbtg1Dhw5FrVq1sGvXLrRp0wa1a9e2YNRERJQdhtZwiIiIyHL/Fy9e4OTJkzh//jwqV66MzZs3Y8aMGdiwYQPc3d3zI+R88d1336F06dLYvXs3du/eLW1v3LgxateujdGjR6NHjx4YPXo0ypYtiy1btmDIkCEICAiwYNRERFSQWKTjtHLlynBwcMiU1Ds9h5tarcbq1auxbds2PHjwAIMHD0anTp2gMLZCOxGRHRHF1Ju1s4UYrVndunUREBCQqU1MTk7Nae3l5YXNmzdj69atePr0Kd5//320b8+F/Yio4BAE+Vkn1sRQnNldw8HBwQFqtRpLliwBAKSkpKBz58747bffMHz48FyN2ZKaNWuGhISETG1gSkrqzJ0SJUpg27Zt2L59O6KiovDNN99wtCkRFSw21AbaaYpTy3Scvvnmm0b3cXJy0lscg4iIyB4NHjzY6D7u7u7o169f3gdDRER5wtAaDlnlNwUAf39/eHl5Sb+rVCpUq1Yt0zls3TvvvGN0nyJFipjUVhIREeUFiy0OlZ9k8yYa6RGXzdUol/vQSF5KuTymcjkVc7J4To6YW4fG8szm1cPJyZUOhYH/AfPrwVix3PPFSO7UHF19kn1+Gy5TZViFXpUWgEqllRZZks3TKxeOkee3Sua8Kpk8po5GcpzKveZUgnmvRydFsux9EhERUcGRnq+6f//+UCgUePz4MU6fPo13333X4P7z589HSkoKVCoVdDodrl69iu7du+dz5ERERAVbgeg4JSKySSJyZZG7PMep+kRElIcEQbCZaYqCgUAHDRqE7du3Y9iwYahRowZ27tyJoKAgacTp48ePsXPnTvTr1w9OTk7o3LkzNmzYgP79+6N58+Y4fvw4UlJS0KtXr/x8OEREZGGpbaBtNIK2Emd2seOUiIiIiIgoD3l7e+P333/H1q1bERERgXHjxqFdu3ZSeXJyMp49ewYxLXG4SqXC0qVLsWPHDty9exfBwcHo1KmTwZyoRERElDfYcUpERERERJTHPDw80L9//yzLSpYsiY8//lhvm1qtRrdu3fIjNCIiIjLAbjpOFQoRCqUo5SRVKMX/8iXK5CFUyOQ2TD+vIbJ5SmXKAEBpZkwKIyOfdWZOmZXLZwkAgkwOTtFIPte8IpcOUxmnMFim9ZDPdynl/dT7P/VnuXoyNipdNj+tmblGU3cw8z4BCIarSfbYjK+L9P0EhQhF+igJufy/cq8pI69HuXK1TB5TZ5V8vlG5eFWC4fPKHecqJBksIyIiIiIiIiLrZzcdp0RE9kaEYBM5TsUcrcZGREQkTxByuBhmPrKVOImIyEYIyNni1/nJVuLMJpnxZkREREREREREREQFEztOiYiIiIiIiIiIiF7BqfpERFbqv+y61s0WYiQiItslCIDCRubA20iYRERkIwSFAEFhG2MeBWOL8tgou+k4VSh0UCi10ocqhUIHRdqiLnILPMmVAfKLPMktDGN00akcLOgje16ZpBKKDOdN/1khiFBANLqYlVZluFynk3kwxl7gcvkb5UMymzJGKVuuLZy2kFAWi0PJ5uwwuhCT4XInN43BMk2i/MtUboEnYzEpZMrl4k1J+a8OtULqz9oUJVLSFodSq1IMHiu3wJOxxaHMXQDKRWW4fgHASWk4XgeFzOJQguF43SB/n0RERERERERk3Wyj25qIiIiIiIiIiIgoH7HjlIiIiIiIiIiIiOgVdjNVn4jI3oiiAFEunYWVsIUYiYjIdgmwndyhNhImERHZDMF2GkE7bQXtpuNU5aCDGlo4pD2hHNRaaMXU3IRyOUPl8pQC8rlK5fKUGsvVmGdkHqsuQ5EiLTejQqGDQtRBpTKcxxGQz2NqODskoPOUz7EpRjsYLBOMdcbk0Yo06bk9M/4vQH9blscZO6/M30b2eaY0kjtVLo+psRynMo9HLu9txvdt6bmk1EEppv1s5mtDLocpADjK5CJ1c0gyWOauMlxm7LxqhUz+U8FwvO5iYp7l6SUiIiIiIiKivMep+kRERERERERERESvsJsRp0REdkdEno2szlW2ECMREdksQRBsZpaircRJREQ2QrChqfq2Emc2ccQpERERERERERER0SvsZsSpgzIFajEF6rQObrUqBToxNTehXA5IpUyORwBQGMkRmd9k81lCfpEWhZD5Z4WQ2nuuNHJeBwf53JOGGDtKqzRcJhqrernUnkYOlSNEqlP/V6YGJ0SpIWjT8uX6JRo+zkheW7mLL3JlTs4a2fNqNIZfxnI5TFPLZXKrGjk2XfpzUhD+ywUrl8dU7rmmVsg/Y1xUhuvCVabM3cHw3w0AXBSGj3U0M8eph445TomIiIiIiIhsGUecEhEREREREREREb3CbkacEhHZG1EUZEeRWwtbiJGIiGyXIOjPnLJmdprejYiILEQQFBAE2xjzaCtxZpd9PioiIiIiIiIiIiKiHOCIUyIiynMJCQlwdnbOVnlycjJiY2Mz7evh4QGVis0XERERERGRrYiJiUFsbCyKFi0KpVJmwZs0Go0GT58+hY+PDxwdHfMhwqzZzTdPpVKEStRBJaSuxqJS6qASU3/OqwWejC3UZG0yxvvqgj5yiwQB8k8UQW24HpKNTFdKURo+1mj1yk0PllljyNisYlGR+f/0harkFjYytpiSuc8XY8c5OSYbLEtOkX8zMnc6mdxzCQBUMs8nB6XhP46jyvBCTADgojL8WF2Vhhd48lQlyJ7XTWl48SgnQW5xKMNlLtp4wHC4phFNWCTNGsjEuHz5csyfPx+xsbFwdnbGkCFDMHr0aJPKjx07hhEjRsDBwSHTOWvXrp0nD4WIiKyPIAg2MwXeVuIkIiIbIcB2GhcDYWo0Gnz66afYvXs33N3doVQqMW3aNDRs2NDgqTZv3oypU6fC3d0d0dHReO+99zBw4MA8Clye3XScEhGRdfnrr78wc+ZMLFq0CHXq1MHp06cxZMgQVK9eHU2bNjVa/vLlS5QuXRq7du2y9EMhIiIiIiIiMyxevBhXrlzBoUOH4O3tjVWrVuG9997D/v374erqmmn/K1euYPLkyViyZAkCAwNx7tw5jBw5Ep07d4a3t3e+x88cp0RElCfUajUmTZqEOnXqAAACAwNRrVo1XLhwwaTyuLi4LBtSIiIiIiIisg3btm3DkCFDpE7Pfv36wcHBAYcPH85y/3Xr1qFx48YIDAwEANSqVQsnTpywSKcpwBGnRERWS4RgEyvWiwbmZDRt2lTvd41Gg4cPH6JkyZImlcfFxcHNzQ0AkJiYCCcnp9wOnYiIbIAg2NAsRRuJk4iIbISNN4LJycm4c+cOKlSokGE3ARUrVsT169fRoUOHTMdcuHABXbt2RUpKCsLDw+Hj4wO1Wp2nocuxm45ThSBKN+l3/Jd70V7kVSeK0kh+ToVgOGeloDMck6iSSTYKACrD5xWNDYiWDVkmJmNVqEo7cXqdKEUp4WpyjOEXq3Nh+TyacjlQ5Z6jxsKVy+FrLO+qHFNjEjL8n/7zyyTD9eThaDifqIvKcJ5SAPBwMHysl0O8wTJPpfzfxlNp+FgnheFEpXI5Th0Fw+csiERRxJdffomiRYuiY8eOJpXHxcXh4cOHaN++PZ48eQKlUomePXti/PjxJiUTJyIiIiIiIsuJjY2FTqeDu7u73nY3NzfExMRkeUxERATCwsLQpUsXAMCjR4/w1ltv4e23387zeLNiNx2nRERkndKTgd++fRtLliyBSqUyqbxChQpo0aIFBg0ahICAAFy6dAn/+9//4OPjg2HDhlnioRAREREREZGJ0ge8aLX6g+p0Op3sYJhLly5h48aNcHV1xcWLF9GnTx8EBgaifv36eRpvVpjjlIiI8kx8fDyGDh2K6OhorFq1KlNeGrnyjh07YsKECQgICAAAvPbaa+jWrRuOHDmSr4+BiIiIiIiIss/DwwPOzs549uyZ3vanT5/Cz88vy2MKFy6M1q1bS+tdVK9eHa+99hr++eefPI83K+w4JSKyViJSc0tY/c1A+KKIsWPHonDhwliwYIGUr9TU8l27duHEiRN62168eJFpmgcREdk3wcb+ERER5RYBAgTBRm5ZtIGCIKBWrVo4ffq0tO3Fixe4evUq6tatm+Vjrl27Nv7991+9bdHR0fD09MzdyjWR3UzVFyBCEEQpL6MgiBDkk2BalCUWfMmYC/PVXLA5yQMrCIb7340+TqXM/erkYxJlnr2i3HlNzHEqKlJ3FNU6iLrUXKyCg0yuVyN1KJszNEf1b/hYRwfD+TkBICnZwez7lSMXk5tDksEyTwf5XKQ+6liDZYVULw2WecnkMDVW7iSYl+NUKco/loJg06ZNuHnzJpYtW4YnT55I211cXODt7W20/O7du1i1ahW+//57lC1bFidOnMC2bdswZ84cSzwcIiIiIiIiyqZhw4bh7bffRokSJVC6dGn88ssvqFOnDmrWrAkgdW2LiIgIlClTBgAwcOBA9OjRA9WqVUOdOnWwfft2PH/+HG3atLFI/BbvOE1MTIRKpcqU8y5dcnIyHBzypnOHiIjyzvHjx5GUlIQ+ffrobW/VqhWmTp1qtHzEiBFQKBT44Ycf8OLFCwQEBGDGjBlo0aJFPj6K/BUfHw9HR0eD+X7YJhIRkb16+fIlXFxcIBhYPZptIBGRbWratCmmTZuGVatW4cWLF6hbty7effddqfzUqVOYOXMmtm7dCgAoU6YMFi9ejHnz5mHjxo0oW7YsfvvtN/j4+Fgkfot2nC5cuBA//fQTvvvuO4SEhOiVbdmyBT/++CMiIiLg5+eHjz76CEFBQRaKlIgo/4li6s3aGYrxxx9/lD3OWLkgCBg+fDiGDx9ubmg2Q6vVYtq0aVi6dClWrFiRKen50qVL8euvvyI6OholS5bEF198gYYNG1ooWiKi/CUAUNjIDHgbCdOqJCYmYsKECdi2bRv27NmDUqVKSWWiKGLGjBlYs2YN4uPjUbFiRXzzzTeoVq2aBSMmIspHgiL1Zgtk4mzXrh3atWuXZVmrVq3QqlUrvW01a9bE/PnzczU8c1mk9rVaLT744APcvHkz00IhAHD58mVMnDgRkyZNwvnz5/HJJ5/gk08+wa1btywQLRERUd6Jj4/H//73P4MjbA4cOIC5c+di9uzZOH/+PAYOHIjRo0fj+fPn+RwpERFR7nr+/Dn69++PwoULZ1m+du1a/P7771i1ahXOnj2Lli1bYtSoUUhKMpz6iYiIKDdZZMRpQkICKlSogFGjRqFZs2aZyjdv3oxWrVpJ+Qs6dOiAP/74A7///jvGjx+f3+ESERHlmfDwcAQFBaFHjx5YsmRJpvKNGzeiR48eCAwMBAD069cPGzZswM6dOzFgwID8DpeIiKzU4sWLERUVla1jhg0bhkKFCuVRRMbduXMHo0ePRtWqVbFs2bJM5Rs3bsSwYcNQuXJlAMDYsWOxfv16HDp0CG3bts3naImIqCCySMepm5sbRo0aZbD8ypUraNmypd62qlWr4ty5c3kcWe6xxOJPgPyiPBkXh1IKqYscKRU6KEVdzqYVKQwvmKQ1Mq/KvZDhRXliI12N3K95C0AJcgtHARCUqY9HkXZ+hVMKFDpt6s8yx6akZJ2TMJ2jo/xCTZbg5mT4an28xnAOKbmFxgCgcuGnBo91d0g0WOajjjNYBgC+DjEGy+QWePJSGl44CgDcFYZjUkNrsEwhGH7uQ8GREGSaMmXKSInQs3L16tVM01qqVq2KK1eu5HVoRERkQ9auXYtq1aoZnMHwqlOnTqFHjx4W7ThNX1E54yKR6XQ6Ha5fv46PPvpI2qZQKFC5cmVcuXKFHadERJQvLL44VFaio6Ph6empt83T01P2CqpaUMJJUMFRSH1I6f9bimihDEemdpw6pv3pHaEChJzlY9LKHK0Q5DsTlTLlKQr5Y2XJdZzKdbgCENI6e53S7t8pQxwKmWMVRvKOqGX+NrLxGClXynTeyT0fAMBB5lidzGtImeG8Tmn7OWXYXw3Dna4OMHyfKlFtsAwAlKKjwTKFKFMPouHOTwCATq6WZY6V6zjVOcnfpynEtJu1s4UYbVhUVBS8vLz0tnl6euLOnTsGj9HptNBqU28ApP8pa6wn41hHpmE9ZU2ny1l9CIIAE/sCLc7ScU6fPt3goruvGjx4cN4Gk0MvX75EcnJytr8XanUitFodtNrUz2np/1PWWE/GsY5Mw3rKmlaXwy9Liv/6KKyejaRizS6r7DgVBAE6nf6LTRRF2aunH3rpJ5Kd6t0lT2KzN994B1s6BMOKWzqAVAvLtzS+E+Gnoh1yfpKUHJYT2SFz2sR7/97U+/3WtUt5Epu9YT0ZxzoyDeuJLGH69OlQKk0fePDJJ5/Az88vDyPKmfR2TnxlFUqdTifbBt64H673+8XbD3M/ODvEejKOdWQa1hPZG6vsOC1cuDAiIyP1tj1//hxFihQxeMyP0fvxUPsCjoIKU7274NPIbUgSLdfLYu1T9R2hwjfewfg8ciuSkJKzEacyj1VjZPp6stZweVyUi/wdy13IyoWp+k4KJRaWb4m3bh1AoglT9eVGowKA2syp+kZHnMqkSjA64lRp+Fi5qfqvjjj9qWgHfPBkFxLTXnOVZKbquzkYnsJexEF+qn5R9QuDZZ7KBJky+an6boLhmMyeqp9SGg4xX8veL5EpihQpkmWb6OPjY/CYUmUrwMnZBVqtFreuXUL5yq9l68t0QcN6Mo51ZBrWU9YSE+IzXdCh3FezZk3pZ61Wiz179uD27dtITtb/DDpkyBB4eXlJeUOtlaurK5ydnbNsAytUqGDwuIol/eDipIZWq8PF2w9RvZw/lEo7HQaVC1hPxrGOTMN6ylp8oibTBR2yLVbZcVqjRg2cP39eb9uZM2fQpEkTg8doRK3UaQMASWKK3u/mskQHqKmdn1keK1OmyDCfNv3nZCQj2YRhfHL3myIaflNMlutYApAiGO6USlIaeXrKfB+RmzWvkOloBABFWmeiNm3quValQUrac0muc9RYJ6VOZiq5HKMdpzLnVRl5rJCpf7luXm2Gv2t656FG0ECT9lxyk+ng9FAZ7uD0UhvOYQoAXirD53WX6zhVGM5/CgCuguFHK5fOQCEzR12ni4NG9l6NE0XBYhdhssMWYrRl1atXx/nz59G9e3cAqSNvzp49izFjxhg8RqFQ6nXaKJVKduKYgPVkHOvINKwnfYqcpF9C6vR3S0+BN5W1xDl9+nSsXbsW1atXh4OD/sVwjSann1DyhyAIeO2113Du3Dk0btwYAJCYmIjLly/LrpehVAh6nTZKpYKdOCZgPRnHOjIN60mfMqfT7NkIWpxFOk6TkpIQE5PaQaLT6RAXF4eIiAg4OjrCw8MDPXv2RHBwMFavXo3WrVtj27ZtuH79OmbOnGmJcImIiPJMQkIC4uL+G2394sULREREwNnZGW5ubujXrx+GDBmC+vXro3bt2li+fDlSUlLQsWNHC0ZNRETW7NixY5g/fz7q169v6VBkxcXFISEhQRpVGhUVBRcXF7i6usLFxQX9+/fHhAkTUL16dZQtWxazZ89GQEAAGjRoYOHIiYjImvTu3TvbFwbXrl0LtVp+nRXAQh2nBw8exCeffCL9Pn36dEyfPh1NmzbF7NmzUapUKcybNw8//vgjZs+ejTJlymDRokXw9fW1RLhERER5ZsOGDZgxYwYAwMXFBR9//DEA4M0338Tnn3+O2rVrY+rUqZg3bx6ePn2KKlWqYPHixXB2drZk2EREZMWcnZ2tOn9punnz5mHNmjUAUtvAIUOGAABGjx6Nt956Cx06dMDz58/xzTffIDo6GnXq1MH8+fOhUHA0GxER/ef+/ft4//33Td5/1qxZmXJoG2KRjtP27dujffv2svs0atQIjRo1yqeIiIiILGPgwIEYOHCg7D5BQUEICgrKp4iIiMjWDRo0CEuXLsWECRMyTdW3JuPHj8f48eNl9+nXrx/69euXTxEREZEtcnV1RY8ePUzef/ny5bILDWZklTlO81Ne5eYzlu9Sjlw+UWPRyh2bsSw9N6NCEGXzNJpCLo9mTs7s6SmflzIu3tFgmVwuUqXMgkjAf2k5lGm5LZUqHVRpeUTl/q45+ZvL5qY1ltdWplxu4ShAf5GnV3m7Gq7/l5r/hrOn5wB1UOqgTaunwmrDizzJLeLkrZRfHEouj6mrYHhYvoNMLlcAcJQpd5WpQ7mvIckKEc9l79VEOXt5EhER2TyFICCnKeLyi7XEuW/fPhw+fBjbtm3LNGtvyZIlKF68uIUiIyKi7FHIL+JiVSwX5969e7O1//bt203et8B3nBIREREREdmTli1bGswD6uHhkc/REBER5Z87d+5g3rx5CAsLg1arP0hq1apVJuU1zYgdp0RERERERHakc+fOlg6BiIjIIj799FMolUq0bNkSSqVSr+zV303BjlMiIislikKepRPJTbYQIxER2TDhv1RKVs+K4ty1axf++OMPhIeHw9vbG82bN0ffvn2hUvErIBGRrRAEweRcnJZmLXHevn0boaGhcHFxyZXz2UqiBCIiIiIiIjLBtm3bMGHCBPj6+qJ9+/aoUKECli5diu+++87SoREREeWpYsWKIT5efs2c7LCby40iUkdmiWmXeXNrpJa5C/4YXdDHzGONnddcOVnYSI6DkcWJckKtNrygj9zjMbUOpcWhFDooxbx7HICRBcGMxKuUWwjL6LEyCx8pDNdvCfdo6Wd12hJJxd1eQINkAEARleFFnrxlytwVhhd/AowtAJVisExpZIUlJ8FwPTjJXDWTu/IkWsnVNiIiIip4/vjjD8yYMQNNmzaVtg0fPhwdO3aUpjASERHZo48//hhTpkzBW2+9BR8fH72yokWLZvt8dtNxSkRERERERMDTp09RtWpVvW3e3t4oVqwYnj9/Dl9fXwtFRkRElLdGjhwJjUaD3bt3Zyq7cOECHB0ds3U+dpwSEVkrMe1m7WwhRiIislmCYD1504zJq1lc2eXv74+TJ08iKChI2hYeHo4nT56gSJEiFoyMiIiyRYDtJPq2kjB37txp8HODWq3O9vnYcUpERERERGRHBg4ciBEjRmDXrl3w9/dHTEwM9u/fj169ekGh4DIXRERkvwICAgAAoigiOjoaXl5eOboAWyA6TnNy5Vc296SZx5lSbvA+c+kqtpA2REwQROlnOWbnVs1BvMae1gqnJINlmhTDeZtMrUNV2gtLpdRCJabm+8yr1cPlYpLLYZpabjg/p8pIjllHpeG8oC4qw/lEm3vf/O/+RUcgAWhS6Da0QurfxEcVY/BYb6XhHKdOimS5cKGG4byrTjI5Th0Fw8cZoxVl6l/mzTdvs+ISERERGdaoUSOsXLkSGzduxPXr11GoUCF89tln6Ny5s6VDIyIiylPR0dGYMmUK9u7dC41GA0dHR7Rv3x4TJ06Eh4dHts9XIDpOiYhskwCrme8gyxZiJCIiW8VZiuapVasWatWqZekwiIgoJ1Lz1Vg6CtNYSZzTp0/Hs2fPMGPGDPj6+iIiIgLLli3D1KlTMXXq1Gyfjx2nRERERERENm7kyJEYPHgwGjRogJEjR+Lx48dZ7jd//nwUK1Ysn6MjIiLKH8ePH8fmzZvh6ekpbWvQoAHat29v1vnYcUpERERERGTjOnbsCH9/f+nn2NjYLPdzc3PLz7CIiIjylUajgaurq9629N9FUcx2vlO76TgVIKbm6xQy5+40N0+psWPNzvsJy6y4mTFeRYa6UeQwx6kpxxs8Vmn+sXJHqmWe2ToT85Qq0x6zUiFCKeW5zJu/m1yeUqWR54pcHlO5HKYA4OZgOE+su0yZMQ4yOUXl8pi6CobzqgKAUqb+HQTz61COXNZVufynyXK5UU0lwjZWrLeFGImIyGYpBAEKK5n+Z4xCACzVMIaEhEg/+/j4oEuXLpkWgtq0aRPc3d3zOzQiIjKTICggCLaxqJ+1xFm2bFmsWrUKgwYNkjpJN23aBD8/P7MWibKbjlMiIiIiIiICJk+ejO3bt8PR0VHalpKSghkzZqBFixYoXLiwBaMjIiLKOx9++CGGDRuGhQsXwtfXF8+ePUN8fDwWLFhg1vnYcUpERERERGQH+vbtixs3buDly5do3LixXplGo4GTk1Om6YtERET2pEaNGtizZw8OHTqE8PBw+Pr6olmzZvD29jbrfOw4JSIiIiIisgNr1qxBWFgY+vXrh4kTJ0Kl+u/rnkKhQJUqVeDk5GTBCImIiHJfVFQU3N3doVKpEBUVBVEU0bRpU719IiMjzeo8ZccpEZG1Yo5TIiIiAMbXJaD/BAQEYO3atXB3d4eLiwuUSiUAICIiAj4+PhaOjoiIskcAbCTPtyVb62bNmmHNmjWoXr06mjVrBo0m6/VTLly4oJfGxhR203GqEEQo025A6mIwShMWh7LlBZ5ycuyri0PlZIGnHNWDzEJNxs4rt+CPILNgkqnROqS9OTkotdCKhhc7MpXcW4jcAk8OSvn7VsosiuRkZHEouXJ3VaLBsmRRKf2sS/s5WVRCi9SfX+rUBo+V+7juJLOoFJCzhcjkGK5ByD9hZP6o7EskIiIiS3nx4gW6du2Kbdu2wc/PDwAwePBg9OzZE4MGDbJwdERERLlr69atKF68uPSzTpf1t3y12nBfhSHWseQVERERERER5Yqff/4ZAwcO1BthOm3aNMyePRspKfIX1omIiGxNmTJlpJGkW7ZsQbly5fRuZcqUweLFiyGYMXrXbkacEhHZH0F2VLb1sIUYiYjIVgmCYNYXHUtIDdPy807u3buH8ePHQ6H4b5xM1apV4e7ujufPn0ujUImIyLoJCkBQ2EgbaOGhmZcvX4ZOp8P69evRunVrvbLIyEhs3boV33zzTbY/U7DjlIiIiIiIyI4UK1YMV65cQalSpaRtjx49QkxMDIoUKWLByIiIiPLG/PnzceHCBTx//hxvvvmmXpkgCGjXrl3BHnGqUohQiToo07IVKgUdVDK5H9NZIk8pYH6uUmPHmZoDUpHhfwXEPKsH0choOV0ORtMpZfKCKg2WGI8pnSrtuaQSdHDIheeSXB5Tub+b3OM0dl6VQj5nqINMubMy2WCZIkN9KNLqSSHoIKZt18lkAYnXGU7E7CEkGSwDABeF4all2jwamWlu/lOt5QebEBERUQE1cOBAjB07Fvv27YOfnx+ioqKwf/9+9O7dW1osioiIyJ7MmTMHANCzZ0+sXLlSr0ypVEKlMq8LlDlOiYiIiIiI7EiTJk2wbNkyODs749q1a9BoNPj8888xfvx4S4dGRESUp9avX48TJ07A0dFRuu3btw+iaN7oJrsZcUpEZHdEwMz39vxlRTFOmjQJly5dytYxU6ZMwWuvvZZHERERUU4phNSbLbCmOGvWrImaNWvqbTtw4ACaNGkCBwcHC0VFRETZkprk1NJRmMZK4ly2bBnmzp2LU6dOSbm+f/rpJ1y9ehXvv/9+ts/HjlMiIrIbd+7cQYcOHVCoUCGT9t+0aRNevnyZx1ERERHlv7CwMFy/fh0pKampjhISEjBnzhz89ttvXByKiIjs1po1a7Bs2TK9BRLnz5+Pvn37FvCOUyE1T6eQNvQp4895xdw8pTk51lgOU1NzlWa3nuTygupkVtTOSQ5To/lc8zg/rUPa43JQaqFDai5QufrPSY5TObmV1zYrcnlB5f52OvG/NyAh7WedqJBym2pEw28tMTong2XuigSDZQDgKBrOyaqUqSej+U9lipXWNJySjFIoFOjUqRP8/f1N2v+ff/6xmZWaiYiITLV//36MHTsWzs7OiI+Ph5ubG5KSktCmTRv4+PhYOjwiIqI88+LFC5QrV05vW+nSpZGYmAhRFLP9/c86xtESEVFmog3drMTy5cuNdpquXr0aDx8+BAB8++23qFevXn6ERkREZhIEwaZu1mDlypWYPHky/v77bwQEBODkyZMYPXo0GjRooDcCh4iIrJwg2NbNClSsWBFbtmzR27Z27VqULl3arHbafkacEhERvWL37t04ffo0NBoNACA5ORkHDhxAzZo1TR6VSkREZGuePn2K5s2bAwDUajWSk5MxfPhwtGrVCiEhIVCr1RaOkIiIKG+MHz8ew4YNwy+//ILChQsjIiIC8fHxmDdvnlnnY8cpERHZpS1btmDy5Mlo3Lgxzp07h+rVq+PixYsYOnQoqlWrZunwiIiI8kyRIkVw7do1+Pn5wcfHB3fu3EGlSpXg4uKCyMhIFC1a1NIhEhER5YkaNWpg7969CA0NRXh4OHx8fNCsWTMULlzYrPOx45SIiOzSX3/9hW+//RZBQUEYPHgwJk2ahJiYGMyaNctqplISERHlhV69emHMmDE4ePAg6tWrh3HjxqFmzZp4+vSp2V8ciYiIbIWXlxdCQkL0tk2bNg3jx4/P9rkKfMepJRZ4AnK2yJC5Mi7wJKathiOKgvSznLxaACqv5KR+0/+uDmkpgB0Uuv8Wh5I5Ni8XccorGRd5etVLraPBsljtfws8qcTUqV5xWkekpHVGOQiGF3GKFwyfN1rnYrAMAFyFZINlLkKK7LE2SQRgha+vTKzvqQ0AiI+Pl6bjOzs7IyEhAZUrV0ZUVBTu3r2L0qVLWzZAIiIyGa93ZU9QUBB8fX3h6emJwYMHIyoqCnfu3MGMGTPg4OBg6fCIiMhEAqwnf7Yxggl9S/khOTkZ69evx61bt6DVpvZNxMXF4cCBAxg3bly269NqO06fP3+ONWvW4MGDBwgICEC/fv1QqFAhS4dFREQ2okyZMvj9999RqVIlFCtWDGfPnkW5cuWg0+kQExNj6fCy5cGDB1i3bh2ePn2K8uXLo1+/fnBxkb/QQEREBVdMTAxq1KgBBwcHODg44NNPP7V0SGa7efMmNm3ahOjoaFSvXh09e/Zk5y8RERk0depU7NmzB3Xq1MGhQ4fQqFEjXLp0Cd9//71ZndBWuaRibGwsevTogStXrqBWrVq4ePEievfujfj4eEuHRkRENmLgwIE4cOAAbt++jY4dO2LKlCno0KEDwsLCUL58eUuHZ7JHjx6he/fuCA8PR61atXDgwAEMGTIEOp3O0qEREZGV6tevH44ePWrpMHLs6tWrePPNN5GcnIzq1atj/fr1+OCDDywdFhERWbG//voLa9euxaxZs+Dj44Off/4Z7777Lm7cuGHW+axyxOn69evh5eWFn3/+GQqFAj169EBISAj++OMP9O3b19LhERHlDxHIo8wductKYwwICMC+ffsAAA4ODliyZAkuXryI1q1b29RozaVLl+L111/HDz/8AAAICQlB69atcfDgQbRq1crC0RER5T1BsKFpilYS5ptvvokdO3agfv36NtXmvWrevHno2rUrJk6cCABo164dWrZsiStXrqBq1aoWjo6IKB8IsJ7GxRgrCTMxMVFK2Zb++aFLly5o2bIl3n777Wyfzyo7Tk+ePIkmTZpAoUgdEKtSqdC4cWOcOHHCrI7TvMpjmpOclXJ5NnMSr7n5RuVymObkvDl5LEbPbWYeU5VCfpSWSkgt/y/HqRailOPUtkZ45aT+k3VKg2XxOrX0c3qO03idGilpTxN3MdHgsRrR8NvOS53h/KcA8FJheFqWg2j4b2ON+WUp750+fRq+vr4oVaoUACAwMBCBgYEWjir7Tp48iV69ekm/u7i4IDAwECdOnGDHKRERZenixYs4d+4cGjRoAD8/PyiV/32uW7JkCYoXL27B6Ex38uRJfPvtt9LvPj4+qFatGk6cOMGOUyIiylKpUqWwfv169OrVC66urrhx4wZKlCiBhIQE6HQ6qa/RVFbZcfr48WM0b95cb5ufnx/++ecfg8eooYQjVHBMe0iOGR5aTjpN5I7NSZ4DQfa8Oeg4lekAzbgI1Kv1ZKw70FjHqiF52WElV/9y9asy8miVaeVqOOj9n3qfttVxauyxqmVqUS5zVHpnacafM25TioY7QBWizHNfNLyoFABA52ywSJR5LKKR56Eo08Esd6zsWWXqgPLH0qVL0aBBAwwcONDSoeTIkydPULRoUb1tfn5+ePz4scFjdDottFqtlAw9/X/KGuvJONaRaVhPWdPpWB/5rVmzZqhXr16WI3U9PDwsEFH2JSQkIDo6Oss28NGjRwaP0+pEaLU6aLWpn4PT/6essZ6MYx2ZhvWUNa2Og3jy2zvvvIP3338fvXr1QqdOndC7d294eXmhWrVq2e40Bay041Sj0WRK+O3g4ACNRmPwmLHubfV+/8KrW57EZm8+8+hu6RCs3ocuvYzvVNBk8f2nq+5t2fK8lpz/d0lWbuzYsZg7dy6KFy+OSpUq6bUr3t7eUKvVMkdbD41GA5VKv7k21ibe+/em3u+3rl3Kk9jsDevJONaRaVhPZAnLli1DSEgIChUqhCdPnmDo0KGZ2g9bkt7OZbcNvHE/XO/3i7cf5n5wdoj1ZBzryDSsJ7K0Jk2a4NChQwCAYcOGoVixYnj27Bm6du1q1vmssiV1c3PDy5cv9bbFxcXB3d09077pi2OsfXkCEbpYqKHECPdWWBC7H5r06dU5mlJvuCxHI05lRrjJlRkjykypz1jmACWGubXG4ri/kAyt0XGUMoMEZeVlKg7ZEacydag0MlU//fniABUGOLfDyoQ9SEZKapmNTdU39lgdZB6PWplisMxD9d9UfKXogJZiLxwQ1kErpHZfeqteGjoUTgrDH3TdlYan+ANAEUWs4WNlzmvsaag0MyWH3HNQm+KPhLh3c7aAjwirzR+qx0pj/Pbbb3Hq1Ckpz2lGK1asQP369S0QVfYZahPd3Nwy7Zv+fCvqHwBHRydodTo8uHsbJUqXg9KMq6sFBevJONaRaVhPWUtKSsSTh2Fmt4kKIfVmCywZ56ZNm+Dh4YGGDRti7dq16NChQ5YXCX18fPSm7lsrFxcXCIKQZRv46ihU4L82sGRRbzipHaDT6XDrQQTKl/Axa4RRQcF6Mo51ZBrWU9YSNcm4/yTS/O+FgiL1ZgusJM69e/eiXr16cHZOnbEaFBSUo/NZZcdp2bJlcefOHb1tt2/fRrly5TLtm5SUBADo7dpAb/sId+Z9M8Uwt9aWDsHqDXBuZ+kQrE8W7/ktxV7/daAZ7sPMM4a7eY3Ly9GqSUlJWXZwUd6bNm0akpOTs5ym6OPjY4GIzFOmTJlMbeK///6Lxo0bZ9o3vU188jBMb/uDu7fzLkA7wnoyjnVkGtZT1tgm5q3u3btjwoQJUqqItm3bZrnfnj17pPzf1szBwQElS5bEnTt3ULt2bWn77du30aFDh0z7p7eB959E6m2/9SAibwO1E6wn41hHpmE9ZY1tYP754osvMG/ePNSoUSNXzmeVHaft27fHhAkTMGbMGPj5+eHevXvYv38/5s+fn2lfT09PlC5dGo6OjryqQURWQ6fTISkpCZ6enpYOpcDKajQKAKxevRotWrSQVlq0du3bt8eKFSswcOBAuLu748KFCzh37hymTJmSaV+2iURkjdgm5o/BgwejZ8+eiIqKwoABA7Bo0aIsR5waah+tUbt27bBu3Tp06dIFarUa+/btw/Pnz9GiRYtM+7INJCJrxDYw/02aNAnLly9H7969Ubx4cb1ZFua0gVbZcdq6dWvs27cPISEhqFatGi5evIgePXpkOa1SpVKhcOHCFoiSiEhezq8oCoBM+g3rYb0x7t69G6dPn5ZyoSUnJ+PAgQOoWbOmzXSc9uzZEwcPHkSnTp1QoUIFnDt3Du+99x7Kli2baV+2iURkrXLSJgoCspw9YI1yknIrp7Zs2YLg4GC4uLhg+fLlKFGihGy97du3D/Xr188yHZq1GDlyJE6cOIGgoCCUKFECFy5cwOTJk1GoUKFM+7INJCJrlaPvhYJgM21gnuZqzIZx48ZBo9Fg+/btmcouXLgAR8fsLeRslR2ngiDgu+++w7Vr1xAWFoZPP/0U5cuXt3RYRERkQ7Zs2YLJkyejcePGOHfuHKpXr46LFy9i6NChqFatmqXDM5larcbChQtx4cIFPH36FF988QUCAgIsHRYREVmZuXPnolOnTlCpVCa1E6tWrUKFChWsuuPUzc0N69atwz///IPo6GhUr14dfn5+lg6LiIis2Pbt26FQKLLscDZngWCr7DhNV7lyZVSuXNnSYRARkQ3666+/8O233yIoKAiDBw/GpEmTEBMTg1mzZtnOVds0giCgZs2alg6DiIisXHZWDH740DZWvlYqlahbt66lwyAiIis2YMAALFmyBA4ODpgyZQoWL16ca+e26o7T7Lp06RIOHDgAnU6HFi1a8Etmmvj4eCxcuBCvv/46mjVrpleWkpKC7du349atW/Dx8UFwcHCWU1/s3YMHD7B7927ExMSgYsWK6NChg14ejIcPH2LHjh2IjY1FrVq10Lp1wVtUS6fTYd++fbh8+TJcXFzQtGlTVK1aVW+f0NBQnDlzBi4uLujUqRNHxZFFxcfHS9PxnZ2dkZCQgMqVKyMqKgp3795F6dKlLRtgHjt16hSOHj0KBwcHtG/fHhUqVLB0SFYhMjISixcvRtu2bVGrVi29ssTERGzduhX37t1DiRIlEBwcDFdXV8sEakE3b97EgQMHkJCQgNdeew2tWrXSu9hw69Yt7NmzB0lJSWjYsCEaNGggczb7lJycjD///BO3bt2Ch4cH2rRpo/eeotPpsHv3bly+fBmFChVCly5d4Ovra7mAqUD4+OOPM61Ab0yRIkXyKBrLOnjwIM6cOQM3NzcEBQXxM2masLAwrFq1Cr1790aZMmX0yqKjo7F161aEh4ejfPny6Ny5MxwcHCwUqeVcuHABR44cgU6nQ506ddCwYUO98osXL+LAgQMQRREtW7bMtcVnbElCQgK2b9+O+/fvo3DhwujYsaPeSPDk5GSpj8HPzw8hISHM70l57u7du/jpp59QoUIFXLx4EZs3b85yv5CQEL2+HlPYTdbsnTt3on///njx4gUSEhIwePBgbNmyxdJhWVxYWBj69++PNWvW4Pz583ploihixIgRUuL4w4cP44033kBkZKSBs9mnkydPonPnzrh16xZUKhVmzpyJESNGQBRTc1Rdu3YNnTt3xvXr16FQKDBlyhRMnTrVwlHnL51Oh9GjR2PmzJlwcHDAo0eP0KtXL73X2LRp0/D5558DSH3TCg4OxpUrVywVsn0QbehmhcqUKYPff/8diYmJKFasGM6ePQsg9fkcExNj4ejy1rJlyzBmzBhoNBo8e/YMb775Jo4fP27psCzu6tWrGDBgANasWYObN2/qlWk0GvTt2xcbN26Ek5MTtm7dij59+iAxMdFC0VrGjh070KNHDzx+/Bg6nQ6TJ0/GhAkTpPLjx4/jjTfewOPHj6HVavHuu+9i6dKlFow4/yUlJaFv375YsWIF1Go1bt68ic6dO+u9xsaPH4+ffvoJDg4OOHfuHEJCQmxmdJ+1EmzkZklt2rRBSEhItm72eHHou+++w8SJEyEIAv79918EBwfj2rVrlg7L4o4fP47hw4dj+fLlePz4sV5ZZGQk3njjDRw8eBBOTk5YunQpRo4cKX0fKiiWLVuGoUOHIiYmBvHx8Rg7dizmzJkjlW/duhUDBgxAbGws4uPjMWjQIOzYscOCEee/qKgodO3aFdu3b4eTkxPOnj2L9u3bS68xnU6HoUOHYunSpXBycsLBgwfRvXt3vHjxwsKR2zBBABQ2crPgrL6JEyfizJkzmD9/PuLi4jBv3rwsb1qtNtvntosRp6Io4ocffsBnn32Gnj17AgAqVaqEadOmoUuXLgV6VcV33nkHH330EX755ZdMZUeOHMHFixexZ88eeHl5QRRFDBgwAMuXL8f7779vgWgtY/bs2ejTpw8+/vhjAEBQUBCCgoJw584dlC1bFnPmzEGXLl2kFaw7deqEbt26YeDAgTazuExOnTt3DseOHcOhQ4fg5eUFAHBxccH69esREhKC8PBwLFu2DBs2bJBGobq6umLWrFlYsGCBBSOngmzgwIHo378/evTogY4dO2Lo0KFYvHgxXrx4Ydd5s+Pj4zFr1izMnDkTzZs3BwD4+flh2rRpBq+8FgTJycl47733MGPGDLz77ruZyrds2YLY2Fhs27YNTk5OGDFiBEJCQvD777+jT58+FojYMn766Se89957GDx4MACgfv36GDp0KD777DO4urpi+vTpGDFiBMaMGSOVv/POO+jZs6dddsBkZf/+/Xj8+DH++usvaXEBjUaDjRs3omHDhrh06RL27NmDvXv3Siu3vv3221iwYIH0WYKI8sajR4+wYsUKbN68WUr55uTkhNmzZ2f5faigePjwIb777jssXLgQ7dq1y1S+YsUKFCtWDIsWLYJCocDAgQPRrl07HD58ONOMRXul0Wgwa9YsTJ06FR06dAAAlC9fHlOnTsXYsWOh0+kwbdo0TJw4Ed27d5fKf/jhBwQFBdlcGihzbdmyRepcT+9n6dOnD7Zu3YrKlStj//79uH37Nnbv3g13d3dotVr06dMHq1evxujRoy0cPdmzdu3aSe9v3bt3x6ZNm3Lt3HbRo/jvv//i8ePHeo1A27ZtERERgRs3blgwMsv75ZdfMk0vSHfs2DHUq1dP6ggTBAFt2rTBkSNH8jFCy1uxYgU++OAD6ff0aQZxcXEQRRHHjh3Te25VrFgRAQEBOHbsWL7Haim1a9fGP//8Iz1XgNTpPOkrl544cQJFixbVm7rfrl07HDt2zKwrOkS5ISAgAPv27UPFihURGBiIJUuWoGfPnli7di1cXFwsHV6eOXfuHACgadOm0rZ27drh8uXLiIqKslBUlqdQKLBy5cpMKUbSHTt2DM2bN4eTkxOA1MTxLVq0KHBt4u7du9G/f3/pdz8/P4iiiLi4OLx48QKXLl1C+/btpfImTZpAEARpRHdB0LFjR4SGhuqtyPrixQupTTx27Bhq1KghdZoCqZ9LC9pzicgSTpw4gYCAAL11Mtq3b49jx44VuNGTGXl6emL16tUoUaJEluXHjh1DmzZtpI4wT09P1K9fv0C9b6nVapw8eVLve5+fnx8SEhKg1Wpx69YtPH36VK+8ffv2ePLkCW7dumWJkC1i8ODB2Lx5s/RcSUlJQVxcnJT249ixY2jYsKG06JxSqSyQfQyU/yZOnCj9bEqn6ZQpU5CcnGzSue1ixOnDhw/h4uKi16nj5uYGLy8vPHjwoEAvMFWsWDGDZQ8fPsxU7u/vjwcPHuR1WFZFqVTq5bhYvXo1SpUqhWrVqiEqKgrx8fGsJ6TWU1hYGJYuXYo7d+7AxcVFenN6+PAhihcvrrd/8eLFodFoEBERofflkbLBiqfB67HiGNNzc0VFRaFmzZoIDAy0cER57+HDh/Dz89ObbZE+Ov7BgwcFMo81kPoeJpdj8uHDh3jttdf0thUvXrxAXSQDAJVK/6PhqlWrUKdOHfj5+eHq1asA9D9bCIKAYsWKFcg28cqVK1i/fj1u3rwJf39/aRTugwcPsvzc8OjRI+h0ugI9E8pcCkGAwkZGc9lKnPYqq+83xYsXR0JCAp49ewYfHx8LRWZZbm5usuWG6u3+/ft5GZbVeXW17VWrVqFdu3ZQKpV4+PAh3N3dpQ5BAPDw8ICbmxsePHhQoHLJK5VKnDhxAjt37sSNGzcQGBiIfv36AUh9Lr1aF8WLFy9wnxNykyAIEATb+OxgyZHXhw8fRkREhMn7p+cyNoVddJwmJSVlepMDUr8wJyUlWSAi25CYmJip3tRqNTQajYUisrzff/8dS5YswZIlS6BUKqXnz6v15ODgUCDrSalUwt3dHa6urrhz5w4ePXqEokWLZvkaTP+dr0GylKSkJHz55ZfYsWMHEhMTIQgCKlWqhE8++cTgSHx7YOi9HeDrUY6heivIdfbrr7/ir7/+wurVqwFAyvfKekqlUqng4eEBJycn3Lp1CxEREfD09DTYJoqiiOTkZL2RqkSUu9gGmodtoD6dTodvv/0W9+7d02sDs+pzKKj1pFar4e7uDpVKhZs3byIqKgpFixblc4ks5vnz52jSpEmenNsuOk5dXFyQkJCQaXtCQoJdT8fMqazqLT4+vsDW2dy5c7Fu3TosXbpUmsqZXhev1lNBfW4VL15cyn+7aNEifPDBBzh48CBcXFwQHx+vt296nRXEeiLrsGDBApw5cwZfffUVSpUqhYSEBISGhmLs2LE4dOiQ3T43Db23AygwOSjNkVW9FdT3eq1WiylTpuDEiRNYvXq1tBp1xjYx40rL8fHxBfK5VbFiRSnVz9dff43PP/8c69atg4uLC6Kjo/X2jY+Ph0qlyvJLN1Fe0Gq12LNnD27fvp1pKuKQIUP0ZurZE0Pv5elllDVDnx0K4nt7YmIixo8fj8ePH2PlypXSTB32OeirXbs2ateuDQAYO3Ysvv32W8yePZvPJbKYAwcOZDsli6kXs+2i47RUqVJISkpCeHi4lJ/y2bNniIuLQ9myZS0cnfUqVaoULl++rLft7t27BbLOfvjhBxw4cABr167VW/DJ09MTXl5euH//PipWrChtv3v3rrQQWUEQHh6Op0+fonr16tK2119/HdOmTcPLly9RqlQphIWF6R1z9+5duLu7S/luyAycqp8jFy9exGeffSYtkAQADRo0wKVLl3D79m2957M9KVWqFB4/fgyNRiN10ty9exdKpRIlS5a0cHTWq3Tp0pmmJKYvEliQiKKIjz76CA8ePMDatWv1UjsEBARAqVTi/v37UloDjUaDR48eoUyZMpYKOd+FhYXh5cuXeqmgateujT/++ANA6mvwwoULesfcvXsXZcqUKTCLh+Q2ARZdqDdbrCXM6dOnY+3atahevbrehQ4Adj1rqnTp0li/fr3etjt37sDLywve3t4Wisr6ZdUG3r17F9WqVbNQRJah0WgwatQoqNVqrFy5Es7OzlJZ6dKlER8fj2fPnknfb8LDw5GQkFCg2sDbt29DEAS9z0evv/66tACpoedSQaqjXCdYdrX6bLFgnHnZ72AbiRKMKFGiBCpWrIgNGzZI29auXYuKFSuiVKlSFozMurVu3RqnTp2S3tji4+OxdetWtG3b1sKR5a+dO3dix44dWLFihV6nabpWrVph06ZN0tWLgwcPIiYmBo0bN87vUC3mzJkzGDRokF7OkJMnT6JYsWJwdXVFw4YNERcXh9DQUACpX7zXrVuHtm3b8ksiWUyRIkWQkpKSabtOp7PrL081a9aEh4cHtm7dKm1bu3YtGjduzKv9Mlq1aoX9+/cjMjISABAZGYm9e/dmufqwPVu6dClu3ryJxYsXZ8qH6+LiggYNGmDjxo3Sts2bN8PT0xO1atXK50gtZ+/evRg5ciRevnwpbTtx4gTKly8PAGjZsiWuXbsm5YRNTk7Gpk2bCtznK7KsY8eOYf78+VixYgUWL16sd5PL92zrGjVqhOjoaGkhGlEUsX79+gL3Xp5drVq1klIbAamdzSdPnixw71vTp0+HTqfD3Llz9TpNgdSLYuXKldNrA3/77TdUrVpVmplREKxbtw4ffvih3mfskydPoly5cgBSn0vHjh3Do0ePAKQuuLx9+3a+Bsmm2cWIUwCYPHkyhg8fjsuXL0MQBJw8eRK//vqrpcOyqIcPH+KXX34BkNr4xcXF4cmTJ/Dx8cF7772HGjVqoHv37ujbty8aN26MS5cuoVChQujdu7eFI89f06dPR+HChTFz5ky97SEhIahXrx7effdd9OnTB3369IG/vz9CQ0Px6aef2u0Up6y0a9cOW7ZsQbdu3dC0aVM8f/4cp0+fxo8//gggdWTuJ598gg8++AAtWrTAo0ePEB4ejt9++83CkVNBExsbK01J7NmzJ3766Seo1WqUL18eSUlJ+PPPP1G+fPksL5LYCwcHB0yePBmffPIJDh8+jNjYWFy9ehWrVq2ydGgWlbEOIiMj8fvvv+PcuXMoX748hgwZgjZt2mDLli148803Ua9ePZw6dQp16tQpUF8aExISMHfuXFSpUgVTp07VKxswYAAqV66MTz75BAMGDMD9+/fh5uaGI0eOYNq0aZkWlbJnvXv3xq5duxAcHIz69esjLCwM169flz53lipVCiNGjMDgwYPRvHlz3LhxA6IoYtiwYRaOnAoSZ2dnaSZeQVKoUCF89NFHePfdd9GiRQs8fPgQERER+OabbywdmkUdO3YMO3bsAJB6AXnp0qXYsWMH6tati27duqFPnz7YtWsXunfvjtdeew1HjhxBnz59Mi2aaM+ePHmC1atXo2HDhvjiiy/0yt5++20UK1YMkydPxqhRo3DhwgXodDr8/fffWLRokWUCtpDhw4cjNDQUISEhqFWrFm7cuIEnT55g5cqVAIDAwEB07NgRvXr1QuPGjXHu3Dn4+/vjjTfesHDkROYTxOwmAbBi4eHh0tXFpk2b2vXVVFM8f/4c27Zty7Tdy8sLXbt2lX7/+++/cePGDRQrVgxNmzYtUF9+gNTVErMaldawYUNUqlQJAKTRlLGxsahbt640qqSgOXv2LG7fvg1XV1dpleWMbt26hb///hvu7u5o0aIFR7eZKT4+HlevXsW4i6dxJz7O0uEYVcbFDdOrB6JKlSoWz/E0YMAAnDp1yuh+K1asQP369fMhIssJCwvD8ePHoVar0aJFiwJ1sScr9+/fx/79+zNtL168uDQKQhRFHDt2DHfv3kXp0qXRqFGjAjVqPikpyeAFr9atW0sjaqKiohAaGork5GQ0bNgQJUqUyM8wrYIoijhx4gTu378PLy8v1K9fP9Nr7OLFi7h06RKKFCmC5s2bM7+pGdLbw41hDnimsY2JckXUOrwZkGzxNvHPP//EiRMnMGHChExT9QuCGzdu4OzZs/Dw8EDz5s0L/GfSK1euZPn5qGLFimjUqBGA1NHxhw4dQnh4OCpXrizlrywoIiMj9WbrZBQcHCzNVgoPD8fhw4chCAKaNWsGHx+f/AzTKqSkpODo0aN4/PgxihQpgkaNGmV6vzt16hRu3boFf39/NGnSBEql0kLR2q70NrDCiW/hEhtm/AArEO8egJsNPrN4G5jb7KrjlIjIHkgdpxf+tp2O0xp1raKBfPToUZaJ+zMSRRHFixe3eKxERCQvvT3c9EBtUx2n3UtoLN4mfvjhhzh8+DBSUlIyDSZZsmQJihcvbqHIiIjIFFLH6cmpttVxWv9Ti7eBua1gDS0kIiK7tm7dOgwcOBCFCxc2af8VK1agQYMGeou/ERER2bqWLVuiQYMGWZZ5eHjkczRERET5586dO5g3bx7CwsKg1Wr1ylatWpXtWUDsOCUiIrtx9uxZtG7dGjqdzuT901NyEBER2YvOnTtbOgQiIiKL+PTTT6FUKtGyZctMaSLMSRvBjlMiIislABBsIJmKtWWA7NGjR7b279OnTx5FQkREuUIAbCbdsBXFuWvXLvzxxx8IDw+Ht7c3mjdvjr59+xa49QyIiGyZIAg2k3PfWuK8ffs2QkNDcy1dAFtNIiKyG3PmzEFycnK2jvH09MyjaIiIiCxj27Zt+PLLLxEUFIRatWohOjoaS5cuxf379zFhwgRLh0dERJRnihUrhvj4eHacEhERvaqgrxxPREQEAH/88QdmzJiBpk2bStuGDx+Ojh07SlMYiYiI7NHHH3+MKVOm4K233oKPj49eWdGiRbN9PnacEhERERER2ZGnT5+iatWqetu8vb1RrFgxPH/+HL6+vhaKjIiIKG+NHDkSGo0Gu3fvzlR24cIFODo6Zut87DglIrJWYtrN2tlCjEREZLMUggCFleRNM8Za4vT398fJkycRFBQkbQsPD8eTJ09QpEgRC0ZGRETZIwCCwtJBmMg62sCdO3cazLeqVquzfT52nBKliY+PR926dbFs2TLUq1fP0uEQERFZzM2bNxESEoK9e/fC39/f0uEQUTYNHDgQI0aMwK5du+Dv74+YmBjs378fvXr1gkJhK1/AiSzjyJEjGD58OC5evMi0FkQ2KCAgAAAgiiKio6Ph5eWVo4Wr2GoSpRFFEVqtFqLI4XNE9iY5ORlardbSYRDZDLaJRLatUaNGWLlyJTw8PHD9+nUkJibis88+w3vvvWfp0Iisnk6nYxtIZMOio6PxwQcfoEaNGmjQoAFq1qyJ8ePHIyYmxqzzccQpERHZrXXr1mHx4sUICwvD5s2bcfLkSdSoUQO1a9e2dGhERGQiQUi92QK5OK9fv441a9YgIiICFSpUwLBhw+Dh4ZHlvocOHcLy5cszbZ82bRq8vb1NiqVWrVqoVauWSfsSEZGVspdGMB9Nnz4dz549w4wZM+Dr64uIiAgsW7YMU6dOxdSpU7N9PnacEhGRXdq7dy/mz5+Pt99+G6tWrQIA+Pj44N1338WBAwegUrEJJCKi/PHvv/+id+/eePPNN9GpUyds3rwZgwcPxoYNG7KcCnz79m1ERERg9OjRettdXFwM3sfIkSMxePBgNGjQACNHjsTjx4+z3G/+/PkoVqxYzh4QERGRlTp+/Dg2b94MT09PaVuDBg3Qvn17s87Hb41ERGSXdu/ejUmTJqFly5b4448/AACdOnXCypUrcevWLVSuXNmyARIRUYGxbNkytGzZEp9//jkAoE2bNmjZsiUOHDiANm3aZNo/Li4OAQEB6NChg8n30bFjRykncceOHREbG5vlfm5ubmY8AiIiItug0Wjg6uqqty39d1EUs53vlB2nRERkl+Li4lC4cOFM252dnaHT6SwQERERFVSnT5/G0KFDpd8dHR1Rp04dnD592mDH6atf+owJCQmRfvbx8UGXLl0yLQS1adMmuLu7ZzN6IiIi21G2bFmsWrUKgwYNkjpJN23aBD8/P7MWiWLHKRGRlRLE1Ju1s9YYq1atiqVLl+K7776Ttl26dAlXrlxB6dKlLRcYERFliwAhR6vh5icBWcf55MkT+Pr66m3z9fXFkydPstw/Li4OCQkJmD59Ou7evYtixYqhX79+JrdfkydPxvbt2+Ho6ChtS0lJwYwZM9CiRYssLywSEZH1EQQbagOtJM4PP/wQw4YNw8KFC+Hr64tnz54hPj4eCxYsMOt87DglIiK7NHToUPTp0wcNGzaETqfD6NGj8fz5c0yYMEE2RxwREVFuS0lJyZRbW6VSITk5Ocv9k5KScO7cObz++uuoXr069u/fj65du2Lz5s0oW7aswfvp27cvbty4gZcvX6Jx48Z6ZRqNBk5OTtkeyUpERGRLatSogT179uDQoUMIDw+Hr68vmjVrZvLiiq9ixykREdklNzc3/P777zh48CDu3r0LT09PBAYGcrQpERHlO3d3d8TFxelti42NhYeHR5b7T58+Xe/39u3b4+7du1i/fj0++eQTg/ezZs0ahIWFoV+/fpg4caJeZ61CoUCVKlXg5OSUg0dCRERkfaKiouDu7g6VSoWoqCiIooimTZvq7RMZGWlW5yk7TomIrJUopN6snZXGePr0afj6+maZO46IiGyHAEBhdC/rYKhFLFeuHG7duqW3ou/NmzfRsWPHLPePiYmBSqXSmyFRtGjRTJ2vWQkICMDatWtRrFixTNMmDxw4gEKFCsHBwcH4gyEiIstTKFJvtsCCcTZr1gxr1qxB9erV0axZM2g0miz3u3Dhgl4aG1Ow45QojaurKy5fvgylUmnpUIgoFyxduhQNGjTAwIEDLR0Kkc2pWLEiLl++nGlqMRGZp2PHjli8eDH69u2LQoUK4dixY7h69SpmzpyZ5f7Dhw9HyZIl8f3330MQBISFheHYsWOYMGGCSfdXvHhxhIWF4fr160hJSQEAJCQkYM6cOfjtt9/g5+eXWw+NyO40bdqUbSCRjdm6dSuKFy8u/WxoMWC1Wp3tc/OdgCgDNo5E9mPs2LGYO3cuihcvjkqVKumNrvH29jar0SQqSNgmEuWenj174sSJE2jfvj1KlSqFW7duYfLkyfD39wcAXLt2DdOmTcOsWbPg5uaGL7/8EqNGjUK7du3g4+ODq1evomvXrggODjbp/vbv34+xY8fC2dkZ8fHxcHNzQ1JSEtq0aQMfH5+8fKhENk8QBLaBRDamTJky0s9btmzBe++9p1eu0+kwYcIEfPvtt9k+N98NiIjILn377bc4deoU9u3bl6lsxYoVqF+/vgWiIiKigkilUmH27Nm4ffs2IiIiULFiRb08a0WLFsWgQYOk6YOVKlXCnj17cOPGDcTHx6N06dIoUqSIyfe3cuVKTJ48GT179kT79u2xa9cu/Prrr/D29obCVqZ8EhERZcPly5eh0+mwfv16tG7dWq8sMjISW7duxTfffJMpjY0x7DglIrJWYtrN2llpjNOmTUNycnKWDSNH2xAR2Q5BQLa/5FiKsTDLlSuHcuXKZdru5eWFZs2a6W1TqVSoWrWqWXE8ffoUzZs3B5A6LTE5ORnDhw9Hq1atEBISwlkXRES2xEbaQEubP38+Lly4gOfPn+PNN9/UKxMEAe3atTPr8wQ7TomIKE9du3YNDx8+hJ+fH1577bVsl1+9ehUPHjxAQEAAKleubPL9Fi1aNEdxExER2aoiRYrg2rVr8PPzg4+PD+7cuYNKlSrBxcUFkZGRbCOJiMjuzJkzB0BqepyVK1fqlSmVSrNTcLDjlIiI8oRGo8E777yDixcvomrVqrhy5QrKli2LxYsXQ61WGy3X6XQYP348jh49imrVquHy5cto1aqVyXlpfvjhB1y/fj3TdlEUMX78eFSpUiW3HzIREZFV6NWrF8aMGYODBw+iXr16GDduHGrWrImnT5+icOHClg6PiIgoz6xfvz7L7dOmTcP48eOzfT52nBIRWTMrnQZvinXr1uH69evYtWsX3N3dER0djfbt22PHjh3o1q2b0fLdu3fjxIkT2Lp1K3x9ffH48WN07doVoaGh0vRDOWXKlNFbECo+Pl46NmNeOSIism4KIfVmC6wlzqCgIPj6+sLT0xODBw9GVFQU7ty5gxkzZui1jUREZOUERerNFlhJnMnJyVi/fj1u3boFrVYLAIiLi8OBAwcwbtw45jglIiLr0KJFC9SrVw/u7u4AUvO3lSlTBg8fPjSpfN++fWjXrh18fX0BAMWKFUOrVq2wd+9ekzpOe/TokWnbO++8g6FDh7LjlIiI7FpMTAxq1KgBBwcHODg44NNPP7V0SERERPli6tSp2LNnD+rUqYNDhw6hUaNGuHTpEr7//nuzcpxaR3cwERHZnYCAAFSqVEn6/cmTJ7h27Zq0mr2x8jt37qBUqVJ65yxVqhT+/fdfs2Nyd3eHm5sbrl69avY5iIiIrF2/fv1w9OhRS4dBRESU7/766y+sXbsWs2bNgo+PD37++We8++67uHHjhlnn44hTIiIrJYipN2tnSozR0dEYM2YMgoODERgYaFL5y5cv4eLiorefi4sLXr58aVJciYmJ0tQMIDW36e3bt3HlyhW4urqadA4iIrI8wYam6lvLwsdvvvkmduzYgfr162dqS4mIyIYIgvU0LsZYSZyJiYnw9/cHAGmEaZcuXdCyZUu8/fbb2T4fO06JiChPhYWFYfjw4QgMDMQXX3xhcrlarUZycrLevhqNBo6Ojibd71tvvYVTp05l2t6+fXuUK1cuW4+BiIjIlly8eBHnzp1DgwYN4OfnB6VSKZUtWbIExYsXt2B0REREeadUqVJYv349evXqBVdXV9y4cQMlSpRAQkICdDodFIrsTb5nxylJvvnmG1SuXBndu3e3dChmuXDhAg4ePIiYmBiUKVMGISEhcHNzy/Z5vvnmGyQlJUm/C4Ig5V7s0KEDnJyccjNsIrt2+/ZtDBw4EAMGDMDIkSOzVe7v749Hjx7pbXv06JF09dCYr7/+GvHx8dLvgiCgUKFC8PPzM+ORkKUtXrwYiYmJGDNmjKVDMcu9e/ewa9cuREREwM/PD8HBwdl6Lup0Onz99deoUqVKlvl7v/nmG5QsWRIDBgzIVPbDDz/A19cXgwcPzslDIAM2bdqER48eYezYsZYOhUjSrFkz1KtXL8tcbh4eHhaIiPLCkydP8MsvvyA4OBh169a1dDjZJooiDhw4gL///hs6nQ41atRAhw4dst2pkdGDBw8QGhqKBw8eQKfToXDhwqhXrx5q1qxpVm5DAo4fP44///wTEyZMgFqttnQ4REa98847eP/999GrVy906tQJvXv3hpeXF6pVq2bW+wtznJJk48aNOH78uKXDMMvcuXPRo0cPHDhwAI8ePcLMmTMREhKCp0+fZvtcGzduxOHDhxEdHY3o6GhERUXh9OnTmDBhAtq2bYtLly7lwSMgsj9xcXH43//+h+HDh2fZaWqsvEGDBjh8+LA03V6j0eDQoUNo0qSJSfdfqlQpFCtWDOXLl0eVKlUAAGfPntXrTCXbsW/fPvz555+WDsMsu3btQqdOnbBhwwaEh4dj5cqVCAoKwoULF0w+h0KhwNWrVzFv3rxMZdevX8eKFSswd+5c6HQ6vbKHDx9i8eLFiI6ONul+NBoNJk+enOk81sgSsWZ1n3fv3sXly5fzLQYiUwQHB6Nnz57o0aNHpps5AwvIOkVFRWHdunW4efOmpUPJtpSUFIwYMQJjxozB5cuXcfPmTYwfPx7Dhw9HSkpKts+XnJyMr7/+Gu3atcPMmTNx6dIlXLt2DatXr0avXr3Qt29fhIeH58EjyVtz5sxBWFiYRe/z6dOnOHv2rE18NiACgCZNmuDQoUMAgGHDhuHrr7/G4MGDMXfuXLPOxxGnZPNu3LiBOXPmoF+/fpg0aRKA1Km/3bt3x7Rp0zBt2rRsn7NOnTqYPn263rawsDD06NEDkyZNwubNm3MldiJZYtrN2hmI8ddff4VWq4WLiws2bNggbS9RogQaNmxotPzNN9/E6tWrMWbMGDRv3hx79uyBq6srunTpYlJYly9fxoABA7BlyxZER0ejb9++8PHxwYYNG7BkyZIcPWQiU8XHx2PixImoU6cOFi5cCLVajdjYWPTt2xeff/45tm3bZvK5mjVrhpkzZ+LOnTsoU6aMtP3o0aMICAjAgwcPcPnyZVSvXl2vLP1YU1y6dAlr167FxIkTczTiJz9YItas7vPDDz/Ml/suyARBsJmRYtYS54YNGxATE5Npu1arRa9eveDp6WmBqIj+s3HjRoSGhuKnn35Cp06dAAD79+/HqFGjsHHjRvTu3Ttb5/vqq6+wfv16fPDBBxgyZAgcHByksgMHDmDcuHF49913sXr1ar3UFdYsISEB8+fPR9OmTREQEGCx+wwJCUFISEi+3D9lQRAAwbo/k0mspA3cu3cv6tWrB2dnZwBAUFBQjs7HjlPKFo1GgwMHDuDKlStISEhA8eLFERQUBF9fX4SHh+Pnn39Gly5dMi3+smvXLpw6dQqffvqp1IhdunQJBw8eRHR0tHSeokWLAkidYvHrr79i+PDhOHPmDC5fvoyPPvoIKlXmp2z6lYQRI0ZI2wICAtC9e3esWbMGX331FZycnLBgwQKkpKSYPc0zICAAHTp0wG+//YaXL18iKipKNka5xwcAL168wI4dOxAWFgaFQoHKlSujXbt2evkbje1z//59LFq0CIMGDdLL2fjnn3/i3Llz+PTTT43WpbE4iczl7u6OOnXq4NixY3rba9asiYYNGxotd3Nzw7p167BmzRqcO3cO9evXR//+/U2eIrR69WqMHj0aAQEB+OWXXzBy5EiMHDkSHTt2xKNHj5jfzU4dO3YMZ86cwYsXL+Dr64uWLVuiQoUK0Gq1mDp1KmrWrJmp8/3cuXPYvHkzRo0ahWLFigFIvVi2a9cuhIeHo1ChQmjbti0qVqwIILXj4csvv0RwcDA0Gg1CQ0MxePBg6diM/vnnH8TExGDo0KHSc9fd3R1vvfUWxo8fj6tXr6JKlSrYsmULzpw5gy+++MJgJ2Dz5s0xc+ZMHD16NFPHaaNGjXDmzBkcPXo0U8epl5cXatasKT2uv/76C48fP4aTkxOqVq2Kdu3aQRAE7Nu3D8uXLwcATJkyBdWqVUOvXr0ApLb/O3fuxNWrV6FQKFCrVi20bdtWinXjxo2Ijo5GSEgIVq9ejcqVK6NDhw4G/07nz5/HkSNHEBUVBW9vb7Ro0QJVq1bV28ecWNetW4dnz55lausnTZqEli1bomXLlrKxmnOfWU3Vj4+Px86dO3Hjxg2p/c6Y7mfz5s2IjIzEoEGDsG3bNly7dg3e3t7o1KlTvn05Jvt2+PBhPHnyRPo9JiYGjx49QsOGDdGtWzcLRkaWEBUVhX379uHu3bvQ6XQoW7YsunTpAicnJ5w+fRrbtm3DmDFjMqWQ+fXXX6FQKPC///0PQOrU+tDQUPz999/QaDSoUKECOnXqJC1Advz4cezduxcfffQRVq9eDYVCgSFDhmQZU2hoKEqUKCF1mgJAq1atUKtWLWzZsgW9e/dGSkoKpkyZgsDAQNkL59evX8e6devQv39/DB8+PFN5y5YtsXz5cjg7O+t1msrVS7r79+9j9+7dePr0KVxdXREYGIhGjRoZvUhiynFy93/lyhXp++uiRYtQrlw5vP/++zh8+DD27t2LSZMm6X03XrBgAdzc3NCvXz/Zv4M595nVVH2tVov9+/fjn3/+gUajQalSpdCpUyd4e3vrPRcmTZqE0NBQnDhxAk5OTmjRooX0mYQor3zxxReYN28eatSokSvns5Fua7IGOp0OAwcOxKRJk/D06VMkJyfjt99+Q/v27XH9+nX4+vrixIkTWLhwYaZj586di4cPH0qdpvPmzUP37t3x999/QxAEbN26FR07dsTff/8NILXDcN26dVixYgW++uornD592uDUgNjYWAiCAB8fH73tFStWRGJiIv79918AwO7du3M8zTO9oRMEQTZGY4/v8ePH6NixI3777TckJycjNjYW3333Hfr06QONRmPyPk+fPsW6desyTTs5e/YsNm3aZLQujcVJlBNvvfUWZsyYkemWnmfRWDkAeHt74+2338b333+PkSNHZmt6YXh4OOrVqwcAOHHiBNq2bQulUonSpUvb5FQtMm7SpEkYPnw47ty5AyD1anNISAj+/PNPKJVKhIeHY/r06Znak5UrV+LQoUPSF8a9e/eiY8eO2LlzJwRBwOnTpxESEiKNjBYEAevWrcPOnTvx9ttv49SpU3jx4kWWMcXGxgIAfH199band8JeuXIFAHDq1CmsW7dOdhpclSpV4OPjgyNHjkjbkpKS8PfffyMwMBB16tSRRpgCqe32iRMn0LhxYyiVSpw8eRKdOnXCzp07odVqERYWhg8//BCjR48GALi4uEjtXOHCheHu7g4AiIyMRLdu3fDtt98iPj4eL168wCeffIKhQ4dK0ylPnTqFnTt34v3338euXbtw+/Ztg4/j119/Rd++fXHjxg0olUqcP38e3bt31xt5bm6sR48ezbKtX7dunZRqx1Cs5t7n8ePHsWPHDum+wsPD0aVLF8yYMQPx8fGIjY3F1KlT0bVrV0RFRQEA/v77b2zbtg1jx47Fzp07kZiYiHXr1iE4OJjvT5QrZs+ejfXr10u3Xbt2Yc6cOfDz88v0mZns25MnT9ClSxcsXLgQL1++RGxsLKZNm4aQkBDExcUhICAAGzduxB9//KF3XGRkJGbNmiW1S1qtFqNGjcKYMWPw+PFjJCcnY86cOQgJCUFERAQA4ObNm1i9ejV++uknLF++XDbFWVxcXKa2EQAqVKiAa9euSfe5bt06nD59WvYx7tmzBwCyzPOd7rXXXtMbaGKsXgDgyJEj6NChA44dOwaFQoGwsDCMGjUqy8VOMzLlOGP3r1arpe/Onp6eKFSoEADg6tWrWLdunZTKKt3u3bulAUWG/g7m3md6x3T6oq0ajQaDBw/Ghx9+iKdPn0Kn02HFihXo0KGD9Lnmzp07WL16NWbMmIE5c+bg5cuXOHjwIHr16pVp0ARRbps0aRKWL1+O06dP4+HDh3jy5Il0MwdHnJLJEhIS0L9/f1SsWFH6whcTE4OWLVtiyZIl+P777/Hmm29i5syZCA8Pl76A3rhxAzdv3sQ777wDIPWNd9asWRg4cCA+//xzAKk5bgYNGoSJEyfizz//lN6wT5w4gX379sHLy8tgXCVKlIAoirh//z5Kly4tbU//cvL8+XMAwMiRI83Kl5Pu+fPn2LdvH6pXrw4XFxeDMZry+Hbv3o3o6Gjs379fuqI5cuRITJw4EXfv3kXFihVN2scUOYmTLMzGp+pbmqenJ54+fYqrV68iOTkZFSpUAJB6UYILY9inxo0b6816EEURXbt2xbx589CxY0f07NkTe/bswbFjx6RcuYmJidi/fz8GDx4MhUKB+Ph4fPbZZ6hbty4WLVokjeb44osv8M0336Bt27bw8vKCUqnEn3/+iQ0bNuh9EXtV+mJmd+/elXLtApnbqJCQELz22muy0wcFQUDTpk2xe/duJCcnw8HBAWfOnEFiYiIaNGgApVKJzZs3Iz4+Hi4uLrhy5Qqio6OlafoeHh6YMmUKgoODpZGiy5Ytw9SpU3HlyhU0atQI586dw8mTJzFmzBjpsf/000+4d+8etm7dirJlywIAevbsid69e2PDhg3o06cPHBwccOfOHQwbNgxvv/227N9p7dq16NmzJyZPnixtW7p0Ka5fvy79bm6spjAUa27d548//ojIyEjs2rVL+iw0dOhQdOnSBQsWLMAnn3wCQRBw/fp1dOvWTbpYdP/+fbRt2xZ//vknF/J6hQKAwjpm/xllzaNSmjdvjm+//RYxMTFsBwsQrVaLcePGoVmzZtJowK5du6Jfv37YuXMnevbsiWbNmmHz5s16M/h2794ttaNAavqHAwcOYNasWdIo/bFjx6Jjx46YOXMmvvnmG+l7x/Pnz3Hw4EHZNCr+/v44cuRIphWuo6KiEB8fj/j4eDg6OuKLL76QPsMZ8u+//8LFxUXvu2Bu1Mv69etRrVo1LF26VDruwIED2LhxI5KSkvRmCmZkynGm3H9wcDC2bduGHj16oFatWiY/NkN/h9y6z7Vr1+LUqVNYtmwZGjZsCCC1I7xTp0749ttvsWrVKulC45MnT7BhwwYIggCNRoMWLVpg8+bNaNSokcmPp8ATBKuZAm+UlcQ5btw4aDQabN++PVPZhQsXDL52DWHHKZnM1dUVnTt31tvm4eGBChUq4N69ewCA7t27Y/bs2diyZYs0TWLHjh3w9vZGy5YtAQB//fUXRFHUm7ahUqnQs2dPfPTRR7h165a0vUOHDrKdpkDqh0BnZ2f8+OOP+PHHH6FWq3H9+nWpoUq/GteuXTuTH+vFixelfKlAagN+/PhxqNXqTFcYX43RlMfn7u4OrVaLVatWoXfv3nBzc0Px4sWxePFi6RhT9skOc+IsX768WfdFZA2CgoLw6aefQqFQYPDgwRAEATNnzkRSUpLU+UP2pX379nq/C4KA119/XRpF06RJE/j7+2Pz5s1Sx+nBgweRkJCAN954A0DqqMOYmBgMGDBAr2Osb9+++O2333D48GFpumBgYKBspymQOkq0dOnSmD9/PurXrw9vb29ERkZK+bfT26h69epJI6TlpH+5PXfuHAIDA3HkyBFUqFABPj4+aNiwIVJSUnD69Gk0b94cx44dkzpb02PJ2HkLALVr1wYA3Lt3L9NU+XR79+5FixYt9F43tWrVQtWqVbF371706dMHQOoFVlM6/Nzc3HDs2DGcP38eNWrUgCAImaZymhurqbKKNbfuMzQ0FC1atNCb8lquXDm8/vrreiOClUol+vXrJ/1esmRJuLi4cMQp5ZmoqChERUXlaCAB2R5/f3/pIl66WrVqQaFQSN/hevbsiVGjRuHMmTOoU6cOAGDnzp1o2rSpNCp07969KFasmF4aFm9vb7Rv3x67d+/GN998I23v2bOn0dzTHTp0wO+//45FixZJ3xt37tyJw4cPA0htH5VKpdTGyElMTJTSBWS0c+dOnDhxQm9bt27d8Prrr5tUL25ubrh9+zb27duH5s2bw8HBQUr7IseU40y5/5x69e+QW/cZGhqKkiVLSp2mQOpj7tChA1asWIGEhARpe//+/aVOVLVajXLlyrGdozy3fft2KBSKLFNqmJr2LSN2nFK2xMXFITQ0FLdv30ZsbCy0Wi0ePXok5cQsXLgwWrVqhc2bN0sN4J9//omQkBDpytfjx48BAPPnz9c7d/qom7t376JkyZIAUkeTGuPr64vPP/8cX375JZo2bYrixYvj0aNH6NGjBxYuXGjWyqHpU+PTFSpUCO+88w66desmTc1L92qMpjy+bt264d9//8Xs2bMxY8YMVK9eHS1atECvXr2kKRGm7JMd5sTJjlOyZW3atEGhQoUQGxuL5s2bAwD8/PywcOFCq1m8g3JXcnIyjhw5gqtXr+LFixdITk7GP//8I3VOCoKAHj16YN68eXjx4gU8PT2xc+dO1K9fX8or+ejRIwDAli1bEBoaKp07/RwZv1i8+uUjKyqVCl9//TVGjRqF1q1bo0yZMrh//z6GDBmCy5cvZ2pTjGncuDFUKhWOHDmCwMBAHD9+XPriUqhQIVSpUgVHjx6VOk6rVauGwoULS8ffuHEDx44dw9OnT6HRaKT3fEMpAhISEhAdHY179+7pXVAEUmedREdHS797eXmZ1ObOmjULH374IXr27IlChQqhUaNG6Ny5M1q1aqW3X3ZjzQ5Dseb0PtPrK6scykWLFtW7OOzt7a23eAmQ2pnKTi3KDe+8847elESdTof79++jZMmS0kgzKjiePHmC0NBQPHz4EPHx8dBqtRAEQXpva968Ofz8/LBp0ybUqVMH4eHh+PvvvzF79mzpHI8fP0ZKSkqmtuD69euIjo6WZlIApn2Ha9GiBXr27Ikff/wRa9euhbOzM5KTk9GpUyf88ccfWXaEGlK4cGGp3c/4vhoZGYn79+8DgHRhsVatWnj99ddNqpdPPvkE0dHRePvtt+Hs7IzAwEC0bdsWXbt2zfT+nZGpxxm7/5zK6u+QG/f5+PHjLNu5YsWKQafT4dmzZ9K2V/PmKpVKJCUlZeNREGVfqVKlcvV87Dglkz169Ai9evWCVqtFx44dUahQIajVar3k2UDqla1hw4bh3LlzUCqVuHfvHrp37y6VK5VKKBQKuLq66h3n6uqKoUOH6r0Jm3o1oEePHmjcuDFOnToFR0dHNGrUCPv37wcAvQU0TFW7dm1Mnz7dpH1fjdGUx6dQKDB+/HiMGjUKJ06cwPHjx7F06VKsWLECGzduNHkfQ17NeWNunES2rly5cnB1dYUgCLh27Rq8vLyY281OJSYmYsCAAbhx4wY6deqEYsWKQa1WZ+qY7N69O+bOnYudO3ciODgYoaGh+Oqrr6Ty9FGmrq6umd4fhw4ditdee0363dQ2KjAwEPv27cOxY8eQkpKCwMBAvHjxArNnz87WtEIgdaZHzZo1cfLkSURFReHq1atSKhwAaNiwIY4cOYKkpCScPXsWw4YNk8rS0+rUq1cPtWrVQuHChY2uLJxerlarM9VH27Zt9b7YmlofZcqUwebNm3H79m0cPXoUoaGhGDVqFHr16oUpU6aYHashWXVEZhVrbtxn+v5Z3WdycrLeyB9ewKG81LRpU8TExEi/KxQK+Pj4oHXr1haMiizh6NGjGDFiBEqXLo2mTZvC29sbKpVK7z1IqVSie/fuWLZsGSZNmoQ///wTXl5eaNGihd4+KpUqU1tQu3Zt1K5dW+/90tT24KuvvkLv3r1x7do1FClSBI0aNcKXX36JkiVLZuv9t0aNGtiwYQP+/vtvvVGQ/fv3R//+/QGkdqJmLDOlXjw8PPDLL78gPDwcR48exdGjR/Hll19i48aNWLVqlcHOU1OOM+X+s8OU73+5dZ+GLvKlr8OR8W/Hto4sYeTIkVJO3oy0Wi1+/fXXbI86ZccpmWzdunWIjIxEaGgoihQpIm1/NZF448aNERAQgO3bt8PBwQE1atTQy0tTpkwZ6HQ6DBkyJMuE4EDqiA9TJScn4+7duyhevLiUgwdITRJeuXJlvZE2+cGUxyeKIhITE+Hm5oY2bdqgTZs2GDFiBFq1aoVt27ZhxIgRJu2TnpsjvZFKZ8pUC1PiJMsSxNSbtbPWGC9fvowBAwZgy5YtiI6ORt++feHj44MNGzZgyZIllg6PctnBgwdx4cIFvXxbQOb2xNfXF82bN8eOHTukfNUZU7mkX2zr0KGDNFI5J0RRRFhYGFxcXBAUFCRt37BhA5ydnaUpkdnRvHlzzJkzB8ePH4dKpdKb4t+oUSMsWbIEhw8fRlJSkvQYRFHE3LlzERISgh9++EHa/8iRI1i2bJnB+1Kr1fD390dAQAA+/vjjbMealYSEBDg5OaFcuXIoV64cBg4ciFmzZmHevHn45JNP4OzsbFas6fGa0yaaWz9Z3X/x4sWlhSlfjSO7HeWUiundss/f3x/du3fPNF1606ZNegMayP7Nnz8fZcqUwe+//y5dHIyMjMRPP/2kt1+PHj0wf/58HDhwADt37tSbMQikto9XrlzJtbbg5cuXuHfvHqpVq4Zq1aoBSL3odODAgUypd4xp3749vv/+e8yaNQuBgYFZ5qB+tWPRlHpJSUmBTqeDn58f3njjDbzxxhsICgrC6NGjcf78edStWzfLeEw5ztS/y6vSO3w0Go30XTAlJUVvFqgh5t7nq0qXLo1//vknU37ae/fuwdnZOdMoU8opBSBYcwbtjKwjztdee02v4zQqKgqHDx9Gz549zboQbx2PimyCRqOBIAjSE00URcyfPx8xMTF6eUwEQcCbb76JXbt2YceOHZk+nLVv3x6Ojo6YOXOm3pWqAwcOYOXKldmemhAVFYVu3bphxowZEMXUHpx9+/YhNDQU//vf/6T9FixYgJ9//jnbjzu7THl87777LgYNGoT4+HipPCkpCTqdTspDaso+/v7+EAQBx48fl/Y5evQozp49mytxEtmy1atXY/To0QgICMCaNWswcuRI7N27Fw8ePJCmY5P9SJ/2lfHL0t69e3H27FmkpKTofXjq1asXzpw5g+XLl6NTp056Mydq164Nf39/LFiwQFpVF0hdxTa9zcsOnU6HQYMGYfz48VKM165dw6pVq9C3b184OzsDSE0NMGnSJJPee5s1a4bk5GSsXLkSNWvW1Bv9U7duXTg4OGDZsmXw8vJCjRo1pDiSk5P1PiyGh4dLnYLp7Xj6l+SMjzM4OBj79u3DuXPnpG0JCQlYsGABzp8/n636ePjwIerWrYuNGzfqbY+Pj4ezszMcHR1zFGuJEiXw4MEDhIWFAUj97PLLL78YXTwqJ/f5quDgYBw9ehT//POPtO2vv/7CtWvXpPy4RHlt8uTJmUbbpKSkYMaMGVIKCioYkpKSoFAopPe3+Ph4fP/993BxcdH7Dle8eHE0adIEixYtwvnz5zN9hwsODkZYWBjWr18vbdPpdFizZg327duX7biOHz+Obt26YceOHQBSv1vOmjULL168wMCBAwFASg2wbds22XN5enpi8uTJOHfuHN566y1cu3ZNKktMTMS+ffswbNgwuLi4SLnJjdWLVqtF69at8X/27ju+ifr/A/jr7pLuQmmBsjfKKkO2MgSLIgIyRPYQEHAiiOPrxr03gsgXFQcKCuLPr6KAKCIbRGQUBFmClkJZ3U3ufn8kDU3b+1yaNE0ufT19xKT3yX3unU8uOfK5z70/zz77rOs3ZsHzAOjOw+Hpep68LwXHnPPnz7vqKbj8vvDvv3nz5pU4uq4ob7dZ1MCBA5GWlobFixe7lh06dAgrV65Ev379vL5ChKis3HnnnZgxY4br9uSTT2LOnDnYu3evV/snR5ySm9WrV7tm3y1sxIgRGDp0KJYsWYIbbrgBTZo0wdGjR3HjjTdi/PjxeOWVVzBjxgw8/vjjiIuLw9ChQ/HWW2/BarUWm1CqRo0aeOGFF/Dwww9j/fr1aNCgAU6fPo1jx45h1qxZhonEi6pevToeeOABPPfcc/j+++8RGRmJY8eOYfLkyW4/UL7//nvk5eXhjjvu8K5xPOTJ65s+fTomTZqE7t27o2nTplBVFfv27UPXrl1do2Y9eU58fDxuvvlmfPDBB9iwYQMAx8FuzJgx+PTTT32Ok8jMUlNTMWLECADApk2b8O6770JRFDRo0ACpqalMR2FChw8fLvEY1aFDB8yePRt169bFlClT0KpVK6SlpaFRo0Z44oknMGXKFEybNg2zZs1C8+bN0b17d9SoUQN79uzB7Nmz3eqyWCx44403cMcdd6B379647LLLkJWVhZSUFAwZMqTUOUkVRcHTTz+NO++8Ez169EDVqlXx119/oVevXrjnnntcz9uyZQu++OILPPbYY4bfv82bN0f16tWxY8cOt8v0ASAiIgLt2rXD5s2b0b9/f1ddiqJgzJgxWLhwIfbv34+wsDCcOnUKc+bMwYEDBzB37lzk5ua68r4NHjwYrVu3xltvvYXbbrsNf/75J0aNGoVmzZohKirKdVnl9ddfX6r2qF27Nm6//XY8/vjjWLBgAapVq4bU1FSkpaVh9uzZrn/MehvrzTffjM8//xyDBg1C06ZNcfz4cUybNg0bNmxw+wFb0vvk7TaLuu2227B3716MHTsWLVu2RG5uLg4cOIAhQ4a4vpOI/GXUqFE4cOAAMjMzcdVVV7mV5eXlISIiotil1mR+r776KubOnVts+TPPPIMxY8bgwQcfRN++fVG9enUcPnwYjz32GM6fP49vvvkGkZGRrlGkw4cPxx133IE2bdoUm8k+OTkZU6ZMwRNPPIH3338f1apVw+HDh5GXl4fXX3+91DH37t0bAwcOxMyZM/Hmm28iMzMTmZmZePHFF12j8+12Oz7//HMAMDzxNHDgQMTGxuKZZ57BjTfeiEqVKiE8PBxnz56Fpmm4+uqr8eqrr7rmcPCkXR555BHcd999WLNmDerXr48LFy7g0KFDmDx5su5cEIqieLSeJ9u/9dZbERUVhfvuuw+JiYn46quv0LNnT7Ro0QIzZ85E8+bNkZ6ejk6dOqFjx47C45wv2yyqT58+uOWWW/D000/j888/R1RUFPbu3YtWrVqV2YhkorLWokULbNq0CZqmlTqFhKQZfbqowli6dKnuhATNmzdH27ZtkZ6eji1btiAnJwdJSUlo3Lgx7HY71qxZg7y8PPTp08d1yUD//v3RokULt0veCrtw4QK2bNmCs2fPokqVKrjiiitcyerPnz+Pb7/9Ft26dXNN2GHk5MmT2LlzJ/Lz89G2bdtiCYF/+OEH2Gw2t0sl9dqhTp06bpd6lsQoRtHrAxxnULdt24Z///0XVqsVjRs3RrNmzdzq8OQ5APDbb7/hyJEjiI+PR9euXfHXX3/hjz/+wLBhw3yOk8pfVlYW9u3bhwe37MDhixnGKwRYw9gYPN/pCjRv3rxUifz9bebMmejXrx9q166NW2+9Fb/88gskScKAAQPw+uuvG86GTsFl9erVSEtLK7GsRo0a6NWrF3JycrBhwwacO3cOjRs3Rps2bQA4RmWkpqaie/furvQts2bNwv79+3VHseTm5mLr1q34999/ERUVhdatW7tNsvD555+jefPmrhGdRs6fP48tW7bg4sWLuOyyy9xypQKOjtNDhw5hxIgRHv1jbt26dThx4gR69OhRbJKq3377DSkpKWjXrl2xY8auXbtw6NAhVKpUCV27dkVUVBT+/vtvbN26FY0bN0br1q2xa9cu/PXXX2jUqJHb69u/fz/27dsHAKhbty7atm3r6ujcsmUL/vnnH9x4440etUdqaip+//13XLx4EQkJCWjXrh0qV65cJrEWHNcyMjJc/1ZZsWIFGjRogDZt2ghj9WabGzduxNmzZ4v9+2L37t04cOAAFEVBUlISGjVq5CrTi+HLL79Ew4YNccUVV3jUjqGu4Hi46kwUztnMMYIpzmJHn4SsgB4Tjx8/jtGjR+PRRx91G20ty7LrxAuFhrNnz2LlypW65d27d0edOnVw5MgR7Nq1y5XepWrVqrhw4QLWrl2LuLg4V1qX8+fPo1OnTnjyyScxfPjwEus8efIkfvvtN+Tk5CAxMREdOnRwXblx8OBBbN26FYMHDy42D4aeffv2Yf/+/QgPD0eXLl3cJsG12+1YsmQJmjZtqntZfFGapmHPnj04duwYcnJykJCQgLZt2xY7xgDwqF0yMjKwbds2nDlzBpUqVULLli09OvnuyXqebP/o0aP47bffULVqVXTr1g2AI1Xdxo0bcfr0aTRs2BDt2rXDL7/8ApvNhl69egnfB2+2uX//fuzYsQPDhg1z+045duwYdu7cCZvN5vbvLkB/X1i7di3sdjuSk5M9ej8rsoJj4OV/zEFUljmulsuKqoX9SXcE/HdhVlaW24kEVVWxbt06PP7449i2bVup62PHKfnFgQMHMGDAAHz00UduudeIyBg7TsvG6tWr8Z///AeyLGPChAm47bbb8Prrr+Pbb7/F999/z2T1FVhWVhZ69OiBu+66C+PHjw90OESkgx2n3jt58iRq1qxZ7Fi3du1adOvWTTgjOFVcixcvxgsvvID169cjJiYm0OEQVWjsOPVeUlJSsZz3sixjxowZmDJlSqnr46X6VOZycnLw1FNPoU2bNuw0JaKASU5ORpUqVXDx4kXXSIrExES899577DStwDRNwwsvvOCaQZiIKBTVqlULx48fx/79+11XlGVnZ+Ott97C4sWLOXkLFfPPP/9gzpw5GDFiBDtNicjUli5d6jbiVJZl1KhRo8SR555gxymVqUcffRQ//fQT8vLy8PHHHwc6HCKq4ApmLM/Pz4csyxg5cmSAI6JAWrBgAZYuXYoTJ07gjTfe4A9DIgpZP/74I+666y5ERkYiKysLMTExyM3NRXJyMqpVqxbo8CiI2O12jB49GgcOHEC9evVw1113BTokIiKfrFy50m0uAcBxuf5DDz2EZ599ttT1seOUylTfvn3Rvn179OjRg3kyiXylOW/BLohj/Pzzz/Hf//4Xx48fx7Jly7B582a0bt2a+QMrqC5duiA+Ph7t27cvlgebiIKX7LyZQbDE+dFHH+Hxxx/HzTffjOuuuw4rV67E/PnzER8fzwlAyU3BieWIiAj06tULYWFhgQ6JiAqTJEAyyfd2gK/q27NnD1RVxZIlS3DNNde4laWnp+Prr7/GM888U+qrD9lxSmWq6OydRESBsmrVKsybNw933nmnawR8tWrVMH36dKxdu9YtuT1VDK1atSo2KRMRUSg6deqUK01NWFgY8vPzMWXKFPTu3Rs33ngjO8fIRZIkjyf2IyIKZvPmzcOuXbtw5swZ3HTTTW5lkiTh2muv9SplG381EhFRSPr+++/x2GOPoVevXvjqq68AADfccAM++ugjHDx4sNhs40RERKGiatWqSElJQWJiIqpVq4bDhw/j8ssvR1RUFNLT01GjRo1Ah0hERFSm3nrrLQDAzTffjI8++sitTFEUrwfOsOOUiChYaYAUxJfBuwRpjBkZGUhISCi2PDIyEqqqBiAiIiLyhiQF/Oo/jwVLnMOHD8cdd9yBn376CZ06dcKsWbPQpk0bnDp1qsRjIxERBSkeBEttyZIlJS5/6aWXcN9995W6PtN3nNpsNpw/fx7h4eHM10NEQUNVVeTm5qJy5cq8JDxAWrRogffffx/PP/+8a9nu3buxd+9eNGjQIHCB+RGPiUQUjHhMLH/9+vVD9erVUblyZUyYMAFnz57F4cOH8dprr8FqtQY6PL/gMZCIghGPgeUvPz8fS5YswcGDB2G32wE4BtWsXbsWs2bNqng5Ts+fP48jR44EOgwiohI1aNCAIzsCZOLEiRg5ciS6du0KVVVx++2348yZM3jkkUcQFRUV6PD8gsdEIgpmPCaWrw4dOgAArFYr7r33XuTm5iI2NjbAUfkPj4FEFMx4DCw/zz33HH744Qe0b98e69atw5VXXondu3fjhRdeqJg5TsPDwwEArx39AydysxAuyXimaUc8/OdW5GrOSzFF7SIbXGMqKJdF6xq8F7Lo+ltBmWRw3a6mebYTREgKnqjdFU+c2IgczQ5NFZ+VVe2CbYrWNbqE18N4S6ToX2orbCeDTSrOesMlBbPrdMXjf29EruZoAMUi2qa4XhFNEK6qiisWvucG7a8K3js1TxGseGmb4bKMZ5p2wMN/bkNuweXPou366fMoCfYHo0EHov1F1L6iaGtbozG9ZhvXdxSVv5iYGCxfvhw//fQTjhw5gsqVK6Njx44hO9oUuHRMrFWnAcIjIqCqdhz960/Ub9QUsiz4TFdwbCdjbCPPsJ1KlpuTg5N/H+ExsRwdPXoU06dPx5IlS3D8+HGMGjUK58+fxyOPPIIxY8YEOjy/KNi/GtRMQGS4FXZVw4FjqbisXiIUOTguHw1GbCdjbCPPsJ1Klp2bjyP/nOExsBytWbMGn332GerUqYNrr70Wc+bMwbJly3DgwAFce+21pa7P9B2nBZdhnMjNwl/ZFxHp/EfqkZwMZBf09gk6TSRfOk4V7zo/AXHHqSimsuo4jZQcb/3RvIvI1mxQ7UYdp4LOI8G6og5BxxO8/0IVdZQJ31ejjlOLY78p2kYAYBHkRRR2pBsQdY760nFq1P6qXf9HnT1X8IOv0P7g+sxlF/rM+anjVBJ85iRBp7bRe+OPjtNL2/bhUjHNw40EWpDGuHXrVlSvXh3JycmBDqXcFOxv4RERiIyKcl2aEhEZBUVhJ44etpMxtpFn2E5i3h4TJUmCHCR504x4M5LFH95880107twZFosFc+fOxaBBgzBw4EDceuutGDlyZEjunwX7V2S4FVGR4bDbHf82jIoIg6Lw0n09bCdjbCPPsJ3EvP5dyBynpZaTk4PatWsDuHRcHjBgAHr16oU777yz1PVxbyYiopD0/vvv4+effw50GEREROXuwIEDGD9+PCRJwvr16zFmzBi0bNkSCQkJOHPmTKDDIyIi8pv69eu7JoiKjo7GgQMHkJ+fj+zsbK8mCTb9iFMXTbp0K/w3IBx2ZzQiTzTAUxO0t2QwNF08IM+70W9GCr9WrdAyTfPginpfLqkXViwoM9qklyMtjUbtXqq3+L4kbofgG3ZneMLHy5HPmmAEsk+M3hrvBxIb1OvlqFIf0iSQ/9111114++23UatWLVx++eVuk2HEx8cjLCwsgNERERH5j9VqRX5+Pvbt24eoqCjUrVsXAJCZmcnJSYiIKKTdfffdmDFjBoYPH44bbrgBI0aMQFxcHFq2bOnVyF8eNYmIgpSkGWb9CArBGuOzzz6LLVu2YPXq1cXKFi1ahM6dOwcgKiIiKi0JQXP1n6FgCbNDhw54/PHHce7cOfTv3x8AsGTJEkiShPj4+ABHR0REnpMAySwXiwfHUbBbt25Yt24dAGDSpEmoWbMmTp8+jUGDBnlVHztOiYgoJL300kvIz88vMd9ctWrVAhARERFR+Zg+fTrmzJkDi8Xiyue2du1aPP/88wGOjIiIyP8yMjIQGRkJAKhSpQqio6MRGxvrVV3sOCUiopBy4cIFrFmzBufPn8cVV1yB1q1bBzokIiKichUdHY3777/fbdncuXMDFA0REVH5Wb58OV555RWsX78eixcvxosvvoioqCjcfPPNmD59eqnrC52OU9V5K+lvX/JzilIYCnPKGly7KpjIUpyfU1xtIAjzxPrS9kb5LgWzzRvmMRVQnTEXVK8WWiYJtmmUKkP03oniNZqd1ShPr4gs6+/EmiLI+1k4x2lBLlS50BTwohyowngN9hfBZ06Ud9Uo/bOoib1tX1/eF/eKyqieCiI9PR1Dhw7FxYsXERcXhxdffBH33nsvJk2aFOjQiIjIS7LkuJmBWeIkIiKTkKTg7AgqSZDEuWDBArz++usAgI8++ghvv/02mjZtiptuuqmCd5wSEVGF9+GHH6Ju3bqYP38+IiIisGXLFtx666246aabULly5UCHR0RERERERH6UlpaGDh06IDU1FampqejSpQsURUFubi5UVS31BFEByzB7/PhxTJgwAa1atcJVV12Fd999163822+/RZ8+fdCiRQtcd911JU7uQUREVNj+/fsxZMgQREREAAA6deqE+vXr4+DBgwGOTGzfvn0YNmwYWrVqhauvvhpLlixxK//kk0/Qs2dPtGjRAgMGDMC2bdsCFCkREVHZ2rx5MwYMGICWLVviuuuuw6pVq9zK3377bVx55ZVo2bIlhg8fjv379wcoUiIiMoOYmBikpaXhxx9/ROfOnaEoCjIyMgCg1J2mQIA6Tm02G6ZMmYKGDRti27ZtWLBgARYsWICff/4ZAJCSkoIHH3wQ99xzD7Zu3Yrbb78dM2fOxOHDhwMRLhERmURmZmaxkaWVK1dGZmZmgCIydvHiRUyaNAlXX301duzYgRdeeAFPPvkkUlJSAADr1q3Dyy+/jGeeeQZbt27FkCFDMG3aNKSnpwc4ciIiClYXLlxAXl5eoMMwdOLECUybNg3jxo3Dzp07MX36dMyYMQNpaWkAgKVLl2Lx4sWYO3cuNm3ahPbt22PatGmmeG1ERBQYN9xwA4YMGYIXXngBo0ePhqqqmD59Orp16+ZVfQHpOP3pp5+QmpqKBx98EBEREWjevDlWr16Nnj17AgC+/PJLXH311bjhhhsQHR2NG2+8EV26dMHy5csDES4RUWBoJrqR11asWIGoqCjccccdCAsLQ+fOnbF+/Xo0a9YMAPDFF19g2LBh6NatG6Kjo3HLLbegVq1a+O677wIcORFR+ZAkCbJJbka56cvL6NGj8euvvwY6DEOLFy9GixYtMGzYMFitVvTr1w/r169HtWrVADg6TidNmoQ2bdogNjYW9957L3JycvDLL78EOHIionIiyea6BYEZM2bgiSeewIcffoirrroKsiyjXbt2mD17tlf1BSTH6fbt29G5c2eEh4e7lhUeIbR371706NHDbZ2WLVti165d+pVqcExEVDAZkdtjQTAG/7YRzW0k2ifEE0eJt6uJ+rMFk/k4Vvb0H2sltJPo2aLJlgT/QNQMZ3jyz+RRml3QhpLRm1NQvSM2TZVcE1GpgvfGbjAhlaLob9fbiaOM1tUM2le0rqzob1crVCY5J4eSFM0Vq2jiLuFMTYYdcKIJq0QfSHHFmnD/Fq0oKBK1AfnVli1bcOHCBdffp0+fxqZNm3D27FnXsquuugpVq1YNRHjFbN++Hd27d3dbFhcX53q8Z88e9O7d2628ZcuW2LNnT3mER0REJnTTTTfhf//7Hzp37oyoqKhAh6Nr+/btuPrqq92WFRwDVVXFvn37MHPmTFeZoiho3rw59uzZg2uuuaYcIyUiIrOQZbnYMeLOO+/0ur6AdJympqYiOjoaM2bMwLp16xAREYHBgwdj5syZkGUZ6enpbj8aAccBVHRZYrisINJ5A+C6NyQb9NQIyiXRukadXYJ1ZdE2DXvwPeusiZAUt3ujPh5RB5ym6cdkPLO4nzqXhB3eBp2JzvaPcO5DEYX2JdF7oxiMMFBEHbaizk+DelVhx7X37asK2sleqE1K+sxpXuQN8YioWm8/q4D48+plx2nBZ4vKV1RUFJYuXVpsedGcofPmzQuajtPU1FQkJCRg8uTJ2L59OypVqoRx48Zh0qRJAID09HRUqVLFbZ24uDhh+hpVtcNud9wAuO6pZGwnY2wjz7CdSqaqbI/y9scff2Dnzp3o0qULEhMToSiX/l2ycOFC1KpVK4DRXZKamors7GyMHDkSKSkpiI+Px5133onBgwcjMzMTeXl5pf5daFc12O0q7HbHv70L7qlkbCdjbCPPsJ1KZld5eZ7ZBaTj1GazYePGjZgzZw5ee+01pKSkYOLEiahZsyZGjx4NSZKgqu4fNk3ThJe+PNOsvdvfC9p4l7ugopnXqLfxkyq49xr3CnQIpvDe5T0DHULIkTTDczBBIZhiLDrRoBnYbDb88MMPePfdd9G8eXNs27YNkyZNQv369ZGcnOzVMfHoX3+6/X0wZbdfYg81bCdjbCPPsJ3KliSJr5IJJsESZ48ePdCpU6cSjxWVKlUKQEQls9lsWL16Nd555x3UrVsXa9aswfTp09G4cWM0atQIgOOYV5iqqsJj4IFjqW5//3HoRNkHHoLYTsbYRp5hO5UxHgQDLiAdp3FxcbjiiivQqVMnAECzZs0wcOBA/PLLLxg9ejQSEhLcLqkEHCNuEhISdOt8OGU7jmRnIFJWsKBNN0z+fT2yPTm7Lbgc2ajcpxGngnrFI07L5tL3CEnBvEa9Me2vH5Gj2YVXTwOAZvfuEmnDEaf+upxZNOLUIn61hUecvte4F249tBY5zn1JOOJUcCm+YbloxKlBG6mCcqNL9b2t157nPuL0vct74tb9P7s+c1q+YGioLycgRSNOvf2sAn4ZcdogPBZPN+wk3i4RHMfEZs2aoXnz5gCADh06oFevXli/fj2Sk5N1j4miEbP1GzVFRGQU7HY7DqbsRpNmrdxGG5E7tpMxtpFn2E4ly8nOKnZCh/xr4MCBgQ7BI3Fxcejbty/q1asHAEhOTkZSUhI2bNiApKQkRERElHgMbNKkiW6dl9VLRFREGOx2FX8cOoGkxrWhKMGRdy8YsZ2MsY08w3YqWVZOXrETOmQuAek4TUpKKpavNC8vz5XztFWrVsXKf/vtN3Tu3Fm3zly7Hdk2u6tjJdtmv9RxKvrMGvXsicpFHTVG/VWCekWdo0ZX6kue5u8saCfN0U7GA8b0X5Aq6gkz6LgTduz5aRSbZFSxs5NNc16qnm1Tke0c7SXK+ylD3FGvCLYrG+WuFRDlXVUNc5zqx6SK3vNCncAFsecq+ciVCi5RFOyo+T78kBRd5iBqQqMTDqLPlZfDKXMMOuiJCiQlJRXLV1r4mFhwzBwyZIir/LfffsOUKVN065Rlxa3TRlEUduJ4gO1kjG3kGbaTO9nTFFpUpnbt2oXFixfj2LFjmDt3LrZv346OHTsiJiYm0KG5JCUlITXVvUOh4BgoSZJrnosrr7zSVbZ3717ceuutunUqsuTWaaMoMjtxPMB2MsY28gzbyZ1ikCqQ/CMtLc010eDGjRuRl5eHHj16eDWJY0D25uuvvx5paWlYuHAhMjIysHXrVnz99de49tprAQDDhg3Dxo0bsXTpUqSnp2PRokXYvXs3hg0bFohwiYiI/Gbo0KHYsmULVqxYgczMTKxZswbr1q1Dnz59AAAjR47EV199hdWrVyM9PR2vvfYasrOz0a9fvwBHTkREwWrjxo0YN24cJEnCwYMHXWlhHnrooUCH5mbkyJH4v//7P/z888/IysrCF198gT///BM9ezpSQI0aNQoLFy7E5s2bkZaWhtmzZ6NGjRqujlQiIqKili9fjsGDBwMAFi9ejNtvvx0PPfQQ3nzzTa/qC8iI05iYGPz3v//F7Nmz8dZbb6F69eq4++67ccMNNwAAGjVqhDfffBMvv/wynnnmGTRs2BDvvvsuEhMTAxEuERGR39SqVQvz5s3Dc889hyeeeAJ16tTBc889hw4dOgAAOnbsiCeeeAIvvvgiTp06hebNm2PBggWIjo4OcOREROVDlhw3MwiWOD/88EO89tpr6NWrl+tE3OzZs9GzZ09kZWUhKioqwBE6tGrVCi+88AJeeOEF/PPPP2jYsCHmzJnjym/av39/pKWl4cEHH8S5c+dwxRVXYO7cuRzRTUQVhyQZX3ocLIIkx+mCBQvw+uuvAwA++ugjvP3222jatCluuukmTJ8+vdT1BaTjFHDkNV28eLFuec+ePV1nGomIiEJZp06dsHz5ct3yQYMGYdCgQeUXEBERmdrJkyfRunVrt2VhYWGoVq0aLl68GDQdpwDQp08fV+duSW655Rbccsst5RgRERGZWVpaGjp06IDU1FSkpqaiS5cuUBQFubm5UFUVsly6juiAdZyWOU2C5LwBcHusifIiGp0W9mB+qRLDMXofRBP+CCeyMcibKDj56lPfv5d5Vw0nhxJOvONDxILtiia6AgqH5HykSq73SxPlBDU4CyTKPyvKsyGakMqxrve5dkU5ZkXrFt5mwWNJ0lyPZUF+Ty1L/2tH1L6OigVBiT7nhg0hKBOt6q/9l4iIiMgHderUwa+//uo2SVTBD8jq1asHMDIiIiL/iomJQVpaGn788Ud07twZiqIgIyMDAErdaQqEUscpEVGo0eC3SdLKlBliJCIiU5N8GwJQ4UycOBETJkzAqlWrcPbsWbz44otYv349xowZ49XEGEREFCCSFDSXwBsKkjhvuOEGDBkyBBcvXsScOXOgqiqmT5+Obt26eVUfO06JiIiIiIhCSIcOHfD555/js88+Q+vWrWGz2fDQQw9xYkEiIgp5M2bMQNu2bVG1alW0adMGANCuXTtMmDDBq/rYcUpERERERBRCNmzYgC5duuCpp55yW/7ll19i6NChAYqKiIjI/2RZxjXXXOO27M4778RLL72E++67r9T1seOUiIiIiIgohDz++OP45ptvEB4e7lpms9nw2muv4eqrr0ZCQkIAoyMiIvKf/Px8LFmyBAcPHoTd7pi4KCMjA2vXrsWsWbNKnbImdDpOVeet4PVrcM2jJGoUzSg5n2gyGuHEUQb1CqoVTqgkmlTKiHKpYq3QvSYOx+35JRHtcz7Nj+NLUD6sVzB5VMHESZpdglbQ7j7k7FChn4RYNMGTEdG6RnmP7YKJsgwn9hI5E65fJpjsyugLTPh5FU70ZvSmC8qEs2QJ1vPls1pQvSacly1omCFGIiIyLxnG87kGi9JPOVG2Ro0ahQMHDiAzMxNXXXWVW1leXh4iIiIQHR0doOiIiKj0ZPGM3EElOOJ87rnn8MMPP6B9+/ZYt24drrzySuzevRsvvPCCV3m+Q6fjlIiIiIiIqAL79NNPcfz4cYwePRqPPvooLJZLP/dkWUbz5s0RERERwAiJiIj8a82aNfjss89Qp04dXHvttZgzZw6WLVuGAwcO4Nprry11few4JSIiIiIiChF169bFZ599hpo1axYbWbN27VpUqVIFVqs1QNERERH5V05ODmrXrg3g0lWtAwYMQK9evXDnnXeWuj52nBIRBTNeBk9ERBWcLJnoUv0gibNWrVo4fvw49u/fD5vNBgDIzs7GW2+9hcWLFyMxMTHAERIRkUck+JQysFwFSZj169fHkiVLMHz4cERHR+PAgQOoU6cOsrOzoaoqZKOchkWETsepJjlyChbsUKp0KcegKKeiQRJOTfUyp6IqrFYYkzAxqFGqRtF2C1WrOf/QVEf+TkkUjxFBgkPDVByiZjBqQ28TqBqup7nduRLBApdynZbEoFpV8Hoku6ihxA1Rys+85wTtpBUqc+WC1STXY8kmqFfwudEUcUjCJhbtS0Zt5G16VFFyT6P9l4iIiMhPfvzxR9x1112IjIxEVlYWYmJikJubi+TkZFSrVi3Q4REREfnN3XffjRkzZmD48OG44YYbMGLECMTFxaFly5al7jQFQqnjlIiIiIiIiPDRRx/h8ccfx80334zrrrsOK1euxPz58xEfH+/Vj0YiIiKz6NatG9atWwcAmDRpEmrWrInTp09j0KBBXtXHjlMiIiIiIqIQcurUKfTs2RMAEBYWhvz8fEyZMgW9e/fGjTfeiLCwsABHSERE5D+RkZGux/369fOpLnacEhEFq0JpIoKaGWIkIiLzkqRikxwFrSCJs2rVqkhJSUFiYiKqVauGw4cP4/LLL0dUVBTS09NRo0aNQIdIRESekGQPciAGiQDHmZeXh+3bt6Nr164AgIkTJyI/Px8AcMUVV2DGjBle1RsyHaeSdulW+G8A0EQ/6g3yEEqifIyiikW5MAGDnIr69QpzbMJgPy1cb5H8nUb9Hl6ngDRoBm/TlDoqF1UsWs8oUaxU/L7gsYc5ZEsbkirMl2uQh1fQiJLBaxXuwqr+zlQ4/6xWaFnBctkmiFmUx9QoXi/fdMngzdFE2xVu0vt8xERERET+Mnz4cNxxxx346aef0KlTJ8yaNQtt2rTBqVOnkJCQEOjwiIiIytSFCxcwZswYZGVlYfXq1QCArVu3YuLEiYiJicG7776LDh06oHv37qWuO2Q6TomIiIiIiCqq/fv3o0mTJlAUBf369UP16tVRuXJlTJgwAWfPnsXhw4fx2muvwWq1BjpUIiKiMvXOO+9AkiQsXbrUbfmwYcNQp04dSJKETz75hB2nREShpPDI+WBmhhiJiMi8ZMlxM4NAxnnnnXfiwoULuOqqq9C9e3d0794dVqsVVqsV//nPfwIXGBEReU+SgiYNjKEAxrl27Vo89NBDqFKlSonlvXv3xvz5872q2ySJEoiIiIiIiEjP+++/j5kzZ8Jms+H5559Ht27dMGTIELz22mvYvn077HZ7oEMkIiLyi9OnT6Np06Zuy1599VVXeprq1avj/PnzUFWDfJ0l4IhTIiIiIiIik6tTpw6GDx+O4cOHQ1VV7NmzBxs3bsSGDRvw/vvvIzw8HFdeeSVmz56NuLi4QIdLRERUZqKionD27FnUqlXLtaxPnz6ux//++y/i4uIgy6UfP1oxOk6FMxAZTRTkZZnRCGUfJhkSEU+Edanigkl2NFVyTDhltE3ZP9fiSqJ6DSdFElUsWlFYrZggJsNqRS9VNPhbMZgQzIfrpEUTS6l2/bKwwxGux1aLAlwGWI9EwmZzjGTQFP2YhOd3fBgD7+3EUQDEM/WK5n8StT0vXyciIqIAkWUZSUlJSEpKwo033oiNGzdi8eLFWLlyJWbOnMmOUyIiCint27fHsmXL0LJlyxLLP/zwQ3Tp0sWruitGxykRkRlpMEcHrBliJCIi02J6t9LJyMjAli1bsGHDBmzYsAHHjh1DUlISrrrqKtx7772oU6dOoEMkIiJPSRIgmSTLZgAPglOmTMHw4cORm5uLW265BfXr14emafjrr78wb948/Pjjj1iyZIlXdbPjlIiIiIiIyOTef/99/PDDD9izZw+aNGmCzp0748EHH0SHDh0QFRUV6PCIiIj8pkWLFpg7dy4effRRLF26FBaLBZqmwW63o0GDBvjvf/+Lyy+/3Ku62XFKRERERERkcl999RUOHz6M66+/Ht27d0fnzp1RrVq1QIdFRERULrp164ZVq1bhjz/+wPHjx2GxWFCvXj20atXKp3pDpuNUky7dCv/t0YrCcv1rUCVhvkuDa1dlwXZFSSAFuSMdGxYlZCzhec6G0gwmFhOOuBbleTTIvykJEkhqRnlVhflnAzBE3CAnqzA/qmA/U1WjfcmHHKeCPKYQlMm2Qo8LLStYrgreV0mwD2sGbSiJ3nTRrm/03gj3YUGR6INhtE1PaIYfoeBghhiJiMi0JEiQg+EaeA+I/m3rb8uWLcPu3buxfv16LF68GA8++CAaNGiALl26oGvXrujcuTNiYmICFh8REXnDRPlqAngMLGCxWNCuXTu0a9euzOo0SaIEIiIiIiIi0qMoCtq0aYM77rgDn3zyCTZv3oxZs2ZBlmXMmTMHV155JUaMGIEzZ84EOlQiIiLTCJkRp0REREREROQQHR2NDh06QFVVWK1WAMBvv/2GrKwsJCQkBDg6IiIic2DHKRFRMONl8EREVMHJknkukxNl4yoPdrsdf/zxB3799Vf8+uuv+P333xEWFoauXbti2LBheOutt1C7du3ABklERJ6TZMfNDMwSZymFTseppF26Ff4bEOa7NMofKMyNKMgtKRnk2BTltBTn7hRWa7DN4o81zXEzbgf9MknxPiYRyYccp5pPOU4L7UMF9x7sSz51cAnyiRrlnxUn9zRoB1GOU9EW7YUeS5eWFSwXpWCRRPuSwWvVhAlHBZ9Hg+9vYb3CHc271YiIiIjK2mOPPYbvvvsOFy5cQNOmTdG9e3fcddddaN++PcLCwgIdHhERkSmFTscpERERERFRBRUWFob77rsPPXr0QI0aNQIdDhERUUhgxykREREREZHJPfLII4EOgYiIKOSw45SIKFhpMMcl/2aIkYiITEuShImJgoooXREREVGpSZJ5Di6CONPS0lzpZJKSktCzZ0+PqszMzMS7776La6+9Fq1atSqrSEslNDO3EhERERERERERUUAdOXIE/fv3x8aNG5GZmYlHH30Uzz77rEfrvv7665g/fz4OHDjg5yj1VYwRp4JJY8STwviRcFIZQUxGEwWJJlQqXG/BY01y3sTViuayEsVkOMGTgOFJFWHdgqCMJu4qmBCs4LRC4dMLqoftW2K5uNjrekUTmBlsUxKsG3VMMOtX4dXUS/cFkzsVnjyq2Ko2wWRtPkwIJp4ky6Be0SkkwY4ofGt8mqCMiIiIiIiIyPzefvttdOvWDa+88goAYOjQobjxxhsxZswY1KtXT3e9Xbt24eeff0aLFi3KK9QSBaTj9NChQ1i4cGGx5VOmTEH9+vUBABcuXMDSpUvx999/o169ehg2bBhiYmLKO1QiooCRNOF5n6BhhhiD2Y4dO/Dll18WWz5r1ixUqVIFAHDq1Cl8+eWXOHXqFJo0aYJhw4ZxhmQiqjAkSJBNcrG+ZJI4g8VPP/2EVatWFVv+1FNPQZYdZ7aPHTuGr776CufOnUNSUhIGDhwIRRGc4CciCiWSBEgmuVhcZ9DR+vXr8fTTT7v+btKkCRo3boz169dj1KhRJa5js9nw6KOP4sEHH8S8efP8Eq6nAtL6hw8fxpo1a9C0aVO3W0REBAAgIyMDw4YNw/r161GnTh2sXbsWI0eORG5ubiDCJSIi8pu9e/diy5YtxY6JVqsVAJCamorBgwdj7969qFOnDpYtW4bJkydDMxpJTUREFOS2bduGffv2FTsGSs4f33/++SduvPFG/PPPP6hZsybmz5+PBx54IMBRExGRpzIzM3H27FnUqlXLbXmtWrVw/Phx3fUWLlyI2rVro3fv3v4O0VBARpxmZGSgevXqmDBhQonlX375JcLDw/Hee+/BYrFg7NixGDBgAFasWIGbb765fIMlIiLyo4yMDDRs2FD3mPjBBx+gWbNmeOuttwAAN998M5KTk7Fu3TqPk6oTEREFo4yMDLRq1Ur3GDh37lz07dsXzz33HACgf//+6NOnD2699VZcfvnl5RgpERF5o2AAZHh4uNvy8PBw3cGRx44dw8KFC7Fs2TK/x+eJgIw4zcjIEF52v3HjRvTo0QMWi6NfNywsDN27d8eGDRvEFUu4lOZQKvK37jqa+CaiCW6q+CZpku5NXK8kvmmiW5G6Cr0GTZWEN/E2RW1hEJMvJP2bpGi6N8gGt6Lvf+G/S9O+pWoLo3UFN1ETqZLwJt5HBR8Lwb4EzZHrVPdmF9xsBjcv6zX63IjqFbW9L18fRAWMjombNm1Cr169XH/HxsaiY8eO2LhxY3mER0RE5DcZGRmIjo7WLd+4cSOuueYa1981a9ZEs2bNeAwkIjKJyMhIAEB2drbb8uzsbFdZUY899hgmT55cbJRqoARsxGlYWBi+/PJL7Nu3DwkJCRg4cCBq164NADh58iS6d+/utk6NGjWwa9cu3TojFAWRioJI2ZHvpuAegPeT8gDizj1Rz4hhh61gk6IJcnyZMKlQWbF2MqhXNGmPJIsm+xHX6wtvr1I1mhBMc77nJe5Lsg8vyNt9yYd6JaPOaUFxhEU/d1Thague5/Z8QTOpgm8dVRG3gyZIZ6UJtilaD4DwcyOqV9R+4b7sK66Nw7fvr/JihhiDWEZGBiRJwieffILDhw+jRo0aGDJkCOLj4wE4jok1atRwW6dGjRo4efKkbp2qaofd7rgBcN1TydhOxthGnmE7lUxVfWsPSTL+J3CwMJzclNxkZGSgSpUqWLhwIU6ePIl69ephyJAhiImJQU5ODtLT04sdA2vWrIkTJ07o1mlXNdjtKux2x6ylBfdUMraTMbaRZ9hOJbOLJpj2hCSZ5+BSQpyRkZGoXr06jh07hlatWrmWHz16FNdee22x52/cuBHbtm1D8+bN8fLLLwMA/vnnH6xcuRJ2ux3Dhg3zX/w6AtZxunXrVtStWxdNmzbFtm3b8O6772Lx4sVo3rw5cnNzXbndCoSFhQlznD7d8gq3v99rf5VfYg81C1r0CHQIQW9Bswp6KWzH0j39rf5d/BMHUYjLyMjA6tWrER8fj7p162LdunV477338NVXX6FmzZpeHROP/vWn298HU3b7JfZQw3YyxjbyDNuJyDOZmZn46quvMGLECNSpUwf/+9//8OGHH+Krr76Cqjo6X0p7DDxwLNXt7z8O6Xey0iVsJ2NsI8+wnaioXr16YcWKFbj++ushSRK2bduGEydOoEeP4v1RtWvXxqxZs9yWWa1WxMbGonLlyuUVspuAdJzefvvtmDZtmuvSxHHjxmHatGl477338OqrryI6OhpZWVlu62RmZgov43hkzw4cycpApKzgvfZX4dbtvyK74Ox2RRpxKhqxV2TE6YIWPTB57zpHO/ky4lTRP6MUlCNOVYMRp/ZLI04XNOuJySk/X9qXbKKhlEYbDsCIU7vBGyvYbNTfno84fat/F9z1zSbk2JztJGomq6DMEjojTutHRePp1lfoP4HI6YknnsCTTz6JqKgoAI5j4tChQ7Fo0SI88MADXh0T6zdqiojIKNjtdhxM2Y0mzVpxBmIBtpMxtpFn2E4ly8nOKnZChwgA3nnnHVitVtckwSNHjsS1116LZcuWYeTIkZAkqcRjYPXq1XXrvKxeIqIiwmC3q/jj0AkkNa4NRTHJjNQBwHYyxjbyDNupZFk5ecVO6FQ0d9xxB0aMGIGxY8eifv36WLVqFWbOnOn6Lt+3bx++//573HPPPahXr16xvNfffvstrrrqqhJHqJaHgHScRkVFuc4gFmjSpInrUvyGDRviyJEjbuWHDx9G48aNdevMsduRXeiyqGzV/W9dRv1V3ubhFHV+AuKOU1GPoOH3j2i7xcuyVfulTkFhtYKOU1GHoVE7CEh+ShJp2HFa0DnqTEGQbVORXbC/2gQx+Zqz1Q8kg9cafVT/R10O9PeLkl5qjs3u6jgVdmKKmtCg81nUsSpKAWC4Jwkun/C24zS3LC5R4aX6FUJMTIzbMVGSJDRq1AhnzpwB4DgmHj161G2dw4cPo1OnTrp1yrLi1mmjKAo7cTzAdjLGNvIM28mdLPvWFrIUoIkZvCDIYEUliI2NdfvtEx4ejnr16uHMmTMICwtDnTp1cPToUbRr1871nMOHDyM5OVm3TkWW3DptFEVmJ44H2E7G2EaeYTu5U3w+MMj+HZFWpkqOMzExEV9//TXWrFmDc+fOYeTIkW6X7VssFsTGxurWOmrUKDRv3rzMo/VUQFp/5syZuPvuu11/5+bm4ueff0br1q0BAMnJyVi1apXrR+PJkyfx448/om/fvoEIl4iIyG9GjRqFZ555xvX3+fPnsWnTJrdj4tdff+0acZOSkoIdO3bguuuuC0i8REREZUHTNCQnJ+O9995zLTtx4gR2797tdgxcunQpbDYbAGD9+vX4999/3SZNJCKi4BcbG4tBgwZhwoQJbp2mANC0aVNMmjRJd91Bgwbh8ssv93eIugIy4vSOO+7A+PHjMWTIEDRs2BC//fYbqlevjmnTpgEArrvuOnz33XcYPHgw2rZti+3bt+P666/HVVcxbykREYWWmTNnYtq0adi3bx9q1KiBLVu2oFWrVhg+fDgAx2WLq1evxo033ohmzZph8+bNmDZtGpo2bRrgyImIiLwnSRIeeOAB3H///di0aRMqVaqEDRs24Prrr3eNKJ02bRrGjh2LwYMHo27duti8eTMeeughJCQkBDh6IiKqKALScdq4cWOsWbMGmzdvxvnz5zF27Fi0adMGknMGLlmW8eabb2L79u04fvw4Jk+e7DrrSEREFEo6dOiAn376CVu3bkVmZiYmT56MFi1auMrDw8Px4YcfYsuWLUhNTcWdd94Z0DOuREREZaVPnz5Ys2YNduzYgby8PNx1111u6dni4uLw5ZdfYuPGjTh37hweeOAB1K9fP4ARExFRRROQjlPA8UOwpBm0Cmvfvj3at29fThEREQUXCb7NG1ZemM7NdzExMcLLDhVFQdeuXcsxIiKi4CFBgiyZ42gj8ahYavHx8cKcpWFhYejZs2c5RkREFERkmCeBtllSsZZSwDpOg4bh/uflZEBGnR3CCdb1C4UTRwGAaDIgqYQ/VMlxM+qdEdQrnBPJYHIi0XY1gzfH238/a0Yzzduc5QVfTjbp0usQvB6jiZg0URuLVjV6nb7MQeRtp1zhL0Sp0H0Av89FmzZ8mYKdWBJ85oT1mqDDk4iIiIiIiIj0hWh/MBEREREREREREZH3OOKUiChYaTDHyFUzxEhERKYlSeZJC2OWOImIyCQkyfvLbcubWeIsJY44JSIiIiIiIiIiIiqCI06NCHNPBmCYlQ+5U1E4t2dBTke7M3+nURe6tycODNvI+zMSXudWNchxKjnLJdnRKJJNhqQ6X4e9NBEWqVd09sWXEzOCJo45rIjXFaXpFe0TWgmPC4+O9DJnq/A99YFsE5drgmYSphUOzRNqRERERERERASOOCUiIiIiIiIiIiIqhiNOiYiClKQFZmB7aYlivHjxIpYvX44TJ04gMTERgwYNQnx8vNtzUlNT8d5772HgwIFo3bq1a/mhQ4fw4YcfFqtz8uTJqFevXpnFT0REwU2WANkkl3lwVAoREZUpSXbczMAscZZSaL4qIiIKuLS0NPTv3x/r169H1apVsWHDBtxwww1IS0tzPWfHjh2YMGECvvjiCxw+fNht/cOHD+P7779H/fr13W7h4eHl/VKIiIiIiIioAuKIUyIi8oulS5eiatWqePfddyFJEiZOnIg+ffpg5cqVGDt2LDIzM/Hoo49izpw5mDBhQrH1MzIykJiYiEmTJpV/8ERERERERFThhU7HqVTohiKPjdYT0GTBNaiCIkk0OZEPJIPZczRVFFThJxa612A4YZL3V0cZ1ethvCURXcIsaCfJZjA5lLNcUiTX3wUTRkEVrGdwSbXwrfNhd4k5Ihg4brh/iwq9CseY6C03akNBuZKj/2JVq7hi8Xsj2JcEAZXJN0DhCbeCmU6Mt99+O6ZOneqaGE1RFMTHxyMrKwsAYLVa8fHHH6NKlSolrp+RkYHo6Gi/hExEROYhSeaZj9EscRIRkVlIwt+kwcUscZZO6HScEhFR0FEUxfV427ZtOHDgAF5++WUAQFhYGMLCwnTXzcjIQEREBL766iukpKQgPj4eAwYMQM2aNf0eNxERERERERFznBIRkd/t2rULd999Nx5//HE0aNDAo3UyMjKwefNmbN68GYmJifj999/Rr18/pKSk+DdYIiIiP/j000/Ru3dvJCUlYciQIdi5c6dH6x05cgStW7fGO++8498AiYiIqBiOOCUiClYmv1S/wOrVq/HQQw/h0UcfxYABAzyudtq0abj11ltRuXJlAMAtt9yCKVOm4L333sMrr7ziS8RERGQiEswz2kPvIsWff/4ZL730Et544w20bdsWixcvxtSpU7Fq1SpUqlRJWOejjz5q+BwiIgpRkmSe2epNk1KgdEKn41TRoFk0V05STdGgGSVM9CNhblQY5yrVr1hcLKxXKvzQmb9TlTzLxyrarmB1w/fATx8sSZCz1TDHqd15X+hv1zJRLlhB/tPC9ZW2MPpvH9rIlzyxHtYrqZfuJYM2MIpJM1jfkifKYypY0ei98TLXqyjnqik6PMvBqlWr8Mgjj+Ctt95C586dS7VuTEwMtCKNfNlll2HXrl1lGSIREZHfLVmyBCNGjECPHj0AAFOnTsVXX32FlStX4uabb9Zdb+nSpVAUBVdccUV5hUpERESFmKTbmoiIzOb48eN44IEHMGfOnFJ3mgLArFmzMH36dNffeXl5WLduHZKSksoyTCIiIr/bs2cPWrZs6basVatW2L17t+46Z86cwRtvvIHHH3/c3+ERERGRjtAZcUpEREHlnXfeQUREBD777DN89tlnruWtW7fGuHHjsHPnTnz88ccAgAsXLmDx4sX45Zdf0KJFC0ycOBFTp07F+PHjcfPNN6Nhw4bYsWMHKleujGnTpgXqJREREXnl7NmzrtQzBeLi4vDPP//orvP0009j2LBhaNiwob/DIyIiIh3sOCUiClISjLMtBAO9GPv27YvWrVsXW16vXj0AQHx8PNq3bw8ArnsAqFWrFgCgadOmWLNmDTZt2oTz589j2LBhaN++PaQQzZ1DREQlkyTJlWYq2OnFWTT1TMEyvWPazz//jN27d+P5558v0/iIiMhkJNlEOU5NEmcphU7HqQxA1hw3OB8XJBkU5iH0U/5Ig2pL+sfTpUJRtUYVC8oK53ksqEaDYf5HQ6J4jTo4RMW+5IgUvSajfJfOPKauPLB26dIyu2g9g5gEryci3bPctKXerlGKWcH3mqiZCqeuLahCtjluAKAqopX1iyxZgvUgzmMqyq9qmMdX1W8oLTS/+8tFz549heX16tVzdaLqiYyMRK9evcoyLCIionKXkJCAs2fPui07e/Ys4uPjiz03KysLs2fPxtNPP43w8PDyCpGIiIhKwC4BIiIiIiIiP0pKSnKb3FDTNOzcuRNt2rQp9tzDhw8jLS0NU6dORVJSEpKSkrBq1SrMmTMHAwcOLM+wiYiIKrzQGXFKRBSKfBl5TUREFALMkroG0I9zxIgRuOOOO9C9e3e0bdsWCxcuRG5uLq677rpiz23ZsiX++OMPt2V33303mjVrhttvv90PURMRUdDipfoBF5qvioiIiIiIKEhceeWVeOihhzB79mxcddVV2LRpExYsWIDo6GgAwObNm3H55ZcjPT09wJESERFRYRxxSkRERERE5GfDhg3DsGHDSizr3Lkz9u/fr7vum2++6a+wiIiISCB0Ok4luF/HU/SxHtEkTUBgJpYSbtL7eN0myCnaTv66HNiXeo3W9fS1FisTV1tQXjCvlaQWWmbTX0+2i99US4Z+mWhXMtxVhO3g/f4iC7bs1oRaofsibVdivbn6ZcJJpeA+KVVRmmhyKB8+5kRERERERERUMYVOxykRUajRjDt9g4IZYiQiItOSJUl4QjeYmCVOIiIyCQnikUnBxCRhlhZznBIREREREREREREVwY5TIiIiIiIiIiIioiJC51J9Sbt0K/y38Yo+bNSH61N9Smrp5SblS/EW5ErVJA2arEEyytcqzPVaBsGVNS/znwKAZHfeF/rbtUyUO9UuDkm0Owpzdxq8NZLo9IcveWLt+oVSoUsFXPlfC+WCFe3DWhCerhG2sT/KPKUhOD9fRZkhRiIiMrUQvfqPiIjIgGzwoz+YmCXO0gnNV0VERERERERERETkA3acEhERERERERERERXBjlMiIiIiIiIiIiKiIkIoxykc3cAFXcGFu4RF+feM8qB6m7vPMGeoKKmlD/V6mgBKKnQvAZrBC5UCkVnKaJPCPKbelQEAiubpVC8tk236qyk5BvUKCFPeGu1KPqTLNWwLvW2GFXosX7ov/Fh3XdHpGoNTOd7mIlUt4v1bHK/+usL1mOOUiIioTEgw/vdQsPBoigUiIiJPSSbKcWqWOEspKF7VoUOHcPz48WLLz549i7179+LcuXPlHxQREVEApKSk4N9//y22PC0tDXv37kVGRkYAoiIiIvK/Xbt2IT09vdjyf/75B/v27UNOjg8jFYiIiLwQ8I7TQ4cOYdCgQZg3b57b8ldffRU9e/bEzJkz0aNHD8yZMydAERIREZWPrVu3YtCgQVi6dKlrmaqq+M9//oPk5GTMnDkT3bp1w5IlSwIYJRERUdn75ptvMGzYMKxZs8a1LDc3F9OmTUO/fv1wzz33oFu3bli9enUAoyQiooomoJfqa5qGxx57DA0aNHBbvn79enz66adYvnw5GjdujP3792PEiBHo3LkzOnToEJhgiYjKmaSZ45I/M8RoBnl5eXj88cfRpEkTt+XLli3Dhg0b8MMPPyAxMRGbN2/GpEmT0KVLF9SrVy9A0RIRlR9JkiCZ5Fp9yZEDi0rp3LlzePXVV1G/fn235e+99x5OnDiBdevWITY2Fv/3f/+H+++/H2vXrkXlypUDFC0RUTkyVb6aQAfgHwEdcbpkyRKEh4ejW7dubsv/97//4dprr0Xjxo0BAJdffjl69eqFb7/9NhBhEhER+d3cuXPRpk0bNG3a1G35t99+i6FDhyIxMREA0LlzZ7Ru3RorV64MRJhERERl7oUXXsCNN96IhIQEt+Xffvstxo4di9jYWADAgAEDUKVKFfz0008BiJKIiCqigI04TUtLw5tvvolPPvkEn3/+uVvZoUOH0LdvX7dljRs3xqZNm7zbmC+93t6uK5r8yXBdL8uASxMblUgq/lCCR69REw0p89NZdcmX2XVEMRlMiCSpju1KsuSKw7VMMDmUUTsKJ3Hy4T2X7Ppl0aniF6ta9IPKqu79eRXhpEmKd+sBELaxaF01TL/Msa5gAigv4zV8LUSFHDx4EEuXLsXXX3+Np556qljZTTfd5LasSZMm+PPPP8szRCIiIr/YtGkTtm3bhm+++cbt957NZsORI0dcg2kKNG7cGAcPHizvMImIqIIKWMfpU089hREjRhS7TB8ALly4gOjoaLdlMTExuHDhgm594bKMSFlBpOzo5Si4DxhfOhOFnWi+dCZeWrdYO/krXh/40nEqK/rrisoAQHZuN0JR3O4BQBF8YgxHzwv6MEWz2xtNTCcqjjDoMBR1nKpW/ZrVQh+vCIvidu+oV3+boo5IwzHwgjYWbdOmiHdSf3ScRsjsOSXPaJqGRx55BNOnT0d8fHyx8osXLxY7JkZHRyM1NVW3TlW1w2533AC47qlkbCdjbCPPsJ1KpqpsDypZbm4uHn/8cTz66KMIDw93K8vKyoLdbi/xd+H58+d167SrGux2FXa74x/YBfdUMraTMbaRZ9hOJbOrzN9idgHpOF2zZg1SUlLw8ssvl1husVhgs7kP7bPb7bBY9MN9pnFHt78XNO/he6AVwII23YyfVMG9c02XQIdgCq8PZTv5BY+zIe/TTz8FgGKjSgsoilLqY+LRv9xHox5M2e1jlBUD28kY28gzbKeyJSMIZrT1kFniDBZz5sxBs2bN0KNH8d9uinPwQtETETabDVarVbfOA8fcTyz+cehEGUQa+thOxthGnmE7lTFJNh5NFSzMEmcplXvHaV5eHp588kmMHj0ae/fuBQCcOnUKWVlZ2LVrF1q3bo3ExEScOnXKbb3U1FTUrFlTt96HD23FkZwMRMoKFjTvgcn71iE7kGe3AzXiVHRyp8iI0wVtumHy7+sd7WTGEaeC1yrnCUacCsoAQLZdGnH6zjVdcPuaTchx/oNNydFfT3TJPADvR5wanLCT8/XLYlLFQQkv1a/m+YjT14d2wT1fbkKOze6sV3+bARlxGl3+I04bREXj6aQrhNslSk9Px2uvvYYHH3wQv//+OwDHBBn//vsv9u3bh+bNm6NGjRqlPibWb9QUEZFRsNvtOJiyG02atXL9AKXi2E7G2EaeYTuVLCc7q9gJHaK//voLixYtwksvvYSdO3cCADIzM3H8+HEcPHgQTZo0QWxsLE6dOoWWLVu61jt16hSSkpJ0672sXiKiIsJgt6v449AJJDWuDUUJzR/zZYHtZIxt5Bm2U8mycvKKndAhcyn3jtPc3FzUq1cPv/zyC3755RcAwLFjx2C32/H6669j4cKF6NChA37++WfXOpqmYcOGDbj55pv161VVt47SbNUe2I7TgPH08nZHR1E2bMiGj+3kSw5ZHy7HL8g7WhJZVGYXb9OS7SiXnZ+OvBw7cp0dgqLOUdmgGUXrinKnGnWcVv1DvzfXHiY+YJ29TP9afs2mH7BbN6OzOXPsdlcHs13U2Sjs3BeUQdxRmRsr6n02qlewYdG6grIcw550Isdl+M2bN8eKFStcyw4ePIijR48iJycHr7zyCtq3b49NmzZh5MiRABwnILdu3YrZs2fr1ivLilunjaIo7MTxQKi2U3qm/hm2dzYeEa77SLL7ZGWh2kZlje3kTg50Ci0KSufPn0dSUhIWLVrkWnbixAl8//33yM7OxsMPP4wOHTpg06ZN6NWrFwDHCcc9e/bgoYce0q1XkSW3ThtFkdmJ4wG2kzG2kWfYTu4UOUSnmq9Ayr3jNDY2Fh999JHbshdeeAEXLlzAM888AwAYPnw4Fi1ahCeeeAK9e/fGt99+i8zMTAwePLi8wyUiChhJM5g4LEiYIcZgVb9+/WLHxBkzZqBRo0a46667AADjx4/H0KFD8dprr+GKK67A4sWLkZiYiN69ewciZCKi8idJkAyTyQcLiWl2PNSuXbtix8CRI0diyJAhGDZsGABg8uTJmDhxIqpXr45GjRrhvffeQ9euXdG6detAhExEVP54qX7ABcWrqlWrFurVq+f6Oz4+Hp9++ilyc3Mxf/58WK1WfPzxx4iJiQlglERERP5Xv3591KhRw/V3o0aN8NFHH+H48eN47733ULduXSxcuJCj2YiIKOQ0bdrUbaLEDh06YP78+di5cycWLlyIjh074vXXXw9cgEREVOEEZHKoosaOHVtsWcOGDfHcc88FIBoiIqLAueeee4ota9WqFV599dXyD4aIiKgcPfnkk8WWdenSBV26cBJSIiIKjKDoOA1qokttfLliyOtrWw02Kqq38KoFOR1lDYBm/FqEeR59uB5JlOPUIBepcA4tg3WF9SrF77WCjYk2apTjVJDHVDTBU40N54T1ahbBJE7hRh9xQY5TwXj0wpNDFTxWFUDVjNcVlRnth7lV9fOYCvOUGgzM00T7sLfj8kXxEBGVQBV8bWTkCA4iBm54/RfdsmV3XClc90K2Y7uqM4f1xWwbZMURqEXR/9J+6Nv9wnrv6FJPt6xpDV5hRERERETBgR2nRAE04er26NeusbDv2ZJ5qUd27Q/78MWiDeUQGQUF53mNoGeGGIko6FWLsaJyBBAGwdnEQuySBXYfJpkk85Dg23iF8mSWOImLMZLjAAEAAElEQVQouGiWCGilGLEhQYVk058kmEKJZKLcoaF5FGTHKVEAJVaJRfN6tXF+wyew51zUfZ4SEYvKV47GHztPlGN0RERE5cdqkREVGebxMVGza7DzxA0REYUASVIgWSM9PgYiP7scoyOq2NhxShRAH/y4DcO7tkLuiT04/Y1+Tt+q/f8DW14+ln74azlGR0REVH7SLuYhPlrx6JioqXbkq2YZfUFERGTAlg1NCfP4GCjZ2HFKVF74L06iAEo9n4EvNu1B3PX3QY6ILfE5ckQs4vreh/8t24EzafpnHyn0SJp5bkREvsq3a0jPtCPhhvuFx8SEG+6HXZgwm0KNJAGSJJnkFujWIiJT0lRI9jyPjoGSPQ/Q9Od+oBDjOAia5xaCOOLUSADed9G+phl1UAgncSr0WHa/lxTxF68kmOhGmG7DoEdFU/UD1mziH0Wa4EeTZtHfrj1CWK1rUiibc/ILW6QGm/NaQNEkTpANJrPSKZ6/fiuGdW6J+OQ7Szy7GJ98J+TwGCyZsxpSZvE8NpKs3w6pnap7FRMA8WkVuYTHcqHHXtabGy/eD72dAEpTDD44onq9neRJsA8SUcW0/58MYflr6w/rlq343y7dsmq1EoT11qsXp1v20LcpwnVPnskEAIQrwPPdIjFu0TbkOlNvHzmcrrteTpY479q6HX+XuDyxUjjWPtBdfEyMiEWOwYSMREREpmPLhhxR2fAYiNzzAQiOqOLi6XqiAEu9kIFvP92IuL6zip1ddIw2nYX/fb4FZ05dCFCERERE5SP1Qi7smlziiBuONiUiopAmGHXK0aZEgcN/eRIFgaVzVkMOj0V88p1uyx2jTWOxZP5PgQmMAksz0Y2IqIzkq44fiCUeEyNikc/fixWObLIbEZHXbNnCYyCY27TikWRz3UJQaL4qIpM5/e/5YqNOOdqUiIgqqqKjTjnalIiIKoQSRp1ytClRYIVOjtOis5QE+YwlXufMNXpNgnoL5ymVnPk4JUWFJKmQDHJAyl7meZSM4hVsV5D+1FEuiEmUC1OzGuQizXf8KLMrjufZo1XY7Y4DlJqvv649XFgtLn+75HxuAACLgqXvrkW/UV1dOW0KRpsunbsGkk2UzM2HRG+ifLqC36aFywoea3Khx6J6vdwmAHEeU4vgHxFGOU4F5aL8vsJcrlb+o4aoIlr82zHdsjue+la8siBXdp3L6uuWRURYhdWeOKF/8m375kPCdVXn8S8qTAa6NccfO44gK8+xLD8nV3e92PjKwnp3PHmtsDxfBSKcI25cx0TmNiUiooqgSK5T5jYlCiyeticKEqf/PY9vP9uMuL6zYKlSC3F9Z+HbxZtwJpWjTYmIqOIpGHVqqVKLo02JiKjiKDTqtOAYyNGmRIETOiNOiULA0rlr0G9EZ9R/cK0jt+ncHwMdEgWSWfKHmiFGIjKdglGn9R9cy9GmFZ0kQfL6cq3yZpY4iSioOUedFhwDOdq0ApMk8+QONc2xunRM0vpEFUPBqNPwGpc5R5vyAElERBWXXZMRXuMyjjYlIqKKxTnqNLzGZRxtShRgHHHqA2Fnug+5SIWrGSX+FK3rluNUc91L0AxzmEqyd1/URvVqgoSXsiQeWqII9l4pQn+7NpsgUSYA1eb8cVbwmmPzAdURi5ov+OFmN3hvZFHS0EvxfjZnNWIqReKzd9Y4djLVh4OkQUiaKGeoqKxwvdKle00qsqy0LAb7iyhXqWBdSZT/FIAsyEcqC/Z90XeAxcqhUUQVUcNKMbpldZo1EK57c+/GumWVIvS/lA+fyRHWe02TKrpl4x76QriuFOV4PTbnSAdbvg0257T2lrAw3fVs+TZhvZ5ybEpz3hMREVUg+VnQJAlSflagIyGq0NhxShRkzqZdxEszPw10GBQEJAT1HHcuoXlBBhEFC3aakgTzHGvMEicRmYEGKS8j0EFQoEmyeBBWMDFLSoFSCs1XRUREREREREREROQDdpwSERERERERERERFcGOUyIiIiIiIiIiIqIimOPUgHACKMHER5KfEhP6NKmszoQ+kABZEScQE70eURsZtYMkiSblEa8rC+ouXFY1PAYzWvTFa3tX4nRuBvINJgqy2x2NHO6sIzzCBlVzTHJhD9N/sapdPOmUFmYVlutXLI73n+tq6K9q8AlXBSGposmh5OKPNbnQYw/XLVYmmvwJAETlgv1FNph0yiqYyEkRfDZE+7eoTo9pzluwM0OMROWkaky4btmXd3cTrhsTof+lfT4rX7dstZQmrHfpzn8BAHGRFozpUBsfbzuBc9mO41pEQoJw3YJJnhSL7LxXoHgwUWV4pH47EJWWBIN/kwcRM+QmJ6qYJGhh0ZDyMsF/vJKpSJKJDoImibOUOOKUQt6tTXuif502mNy0Z6BDISIiCpghrWugR+MEDG6tf8KNiIgoJFmjIFkiAGtUoCMhIpNhxymFtOoRlTC4XgccSMvAkHodUD0iNtAhERERlbv4KCt6X1YVB9IycM1lVVElysurIIiIiMxGkmFXwnEgLQN2JTxkZ/4mIv/gNwaFtElNuiMj145e72xERq4dE5v0CHRIRJ7TNEgmuEHj5U5EwW5QUiIycm3O46ENg5ISAx0SkcdkSKa6EVGQsUTiovM34cVcO2CJDHRERJ6TJEdnvyluoXkMDJ0cp5J76geP00AY5eAU5dkU5vb0YNs6RH0Qkg/5WAq/loLHkqxBgmaYi1SU51HElzylRhRZP6Ywix3VwythSL0OeOL7P3HyQg5e+ekvPHFdByw8uA6nci7qrlvQForzTVQsdlg0u7NM/1yDzSBeLTLM4Bl6FYvbXpQz1DDHqSgXqac5TpVL965919v93yD/rCjHqSQokwX7CiDev0X7mYgv+zYRmVeTxGjdspx88fdJn1fX6Zapqv53yr4ffxXWWzsyHx+NegyzVzuOhy//dAiPJzfFnTPeRk6mcFUgqjIAwBbu+LK3nTsDW64zh7M1Qnc1JaGyQcVERETlwDna9MVV+3HyQg5eXHsQT/W9HIotG9C8+3c+EVUsHHFKIWtco6txMdeOt9cfAQC8vf4ILnLUKRERVTCzJiQjI8/9eJiRZ8esCcmBDYyIiMjfnKNNi/4m5KhTIvIUO04pJFUPr4RBdTrilZ/+wsVcx1jQi7k2vPLTX8x1SkREFUbtarG4ZfCVeGVdkePhur8wcciVqFWVx0MiIgpRBaNN1x50Owa+uPYgc50Skcf4TUEhqeho0wJvrz/CXKdkHpqJbkQUlGaNuspttGkB16jTMd0DExhRaRRKyRXsN6Y4JQoiRUabFuCoUzKVgOctLeUtBIVQjlPt0q3w3xDnPhTmMIX43z5GeUFFNE2UIFWwnur9v8YK51yUC93LkmaYw9QoV6m3VEE7CNsIgF0t+UOZGFEJN9bpiNnf/+k6s1igYNTp49d1wPwD60vMdWq3O+pVnR961S7D7kzqabPpfxHYbYKkoADyEvRnMJbz9Nv/VHuDA7qomXz53vK0XredyfFQE+wvuXXz9Ddp9Hn0Muew0b6kCj5XSmh+9xNRAERYxV8o17WvrVv26rs/6a8YE1/i4toJ0bil/xWYvepAycfDdX/h8YEd8fLXKTh5RifZabgzj2mYM/boyoDVccySZf3XY7GK/4kpzOfOjiciIvJVodymJR0DmeuUiDzFLgEKOZMv64aMEs4sFigYdTq5KUfZEBFR6Jo17Apk5NoMjoc2zLrpivINjIiIyN90RpsW4KhTIvKUxyNOR40ahbS0tBLL4uLi0LlzZ0ybNg0xMTFlFhxRaSVGVMJN9a8ocbRpgUujTttjwZ+/lDjqlCgYFB5EH8zMEGNZ69OnT4nLJUlCfHw8evbsiUmTJiEsLKycIyNyqJ0QjVuua1niaNMCrlGnfVvi5S926I86JQowyfmfGZglTm+lpaVh1KhRJZZJkoTq1aujb9++GDVqlHBUOpFfCUabFuCoUzINVy4YEzBLnKXkccfp33//jRtuuAFVq1YtVpaZmYnvvvsOR44cwdtvv12mARKVxsSmVyJcsaBljVjMHZqk+7zYCAvCFQsmNrkKz+9eWY4RElEoOHbsGKZPn47w8PBiZefPn8fnn3+OM2fO4JFHHglAdETAjKHtEG5VPDseWhXMGNIO9723vhwjJCIzstvt+OeffzBjxowSy0+fPo23334b2dnZuPXWW8s5OiInSyQUWfboGKjIsmPUaT5PHhJRyUqV43Tw4MG47LLLSiwbPXo0evTogZycHERERJRJcESl9W/2BexKP4nWDWQA4pFef6SfRGo2R5sSkXfGjBmDSpUqlVjWt29fTJ48mR2nFDB/n87Etj9P4fLYMCBakINbVrDtz1M4wdGmROQhq9WKSZMm6ZZ37NgRb7/9NjtOKWA0zY78/DwMS6pu+Ny8/DxYNXuIjxUnIl943HH6wgsvoFatWrrlOTk50ESZ/v1MkjRIsuaaPEaSNUjOqZ5Fo4WNviAlWX/Ivqheo6YQbddoIhthvYJV5UITQMmS6loma6rh5E+iibBUnUmaAON2EL1W0YQ9euvO37cZ8/dtFsZbfD33j0HB5FCK7PihmZevoGDuJlUwOZSWL74cKSdB/+NmzbTr1yuecwqi3UUzukLKy3ULb7PgsSYVeiyKWbh/++c7xKhW0b5ms+s3hOhzI5r4jMxv3rx5iIqK0i0P9DGRzOnNDzbqF+bod2xaKhefHOrt7//E29//CQCw5Zd8mSIA9+NAePET34qiOO9l19+K4lgpIlr/RHlcHHPGEYWiKlWq4I033hA+h8dACjTJloMw5AQ6DCIKER53nHbt2lW3bNGiRVi4cCGuueYaj0ebbt26FR9//DH+/fdfJCYmYuTIkW7bSElJwbvvvou///4b9erVw2233YYmTZp4Gi4RUWjg746g1KtXL92yN998E59++in69+/vcX0//vgjvvjiC6Snp6NOnToYP348kpIuXVq2bds2vP/++zh16hSaNGmCu+++GzVr1vTpNRARmQXTuwWP8PBw9OjRo8SyvLw8vPrqq/j8889xxx13eFznV199hW+//RYXLlxA48aNMWnSJDRq1MhVvnbtWixevBjnzp1DUlIS7rzzTlSpUsXn10JEZA4SIJklZ3RoHgTLpPUPHjyIQYMG4aWXXvLo+Vu3bsXkyZPRrl073HfffWjWrBkmT56MP/74AwCQmpqKMWPGoEaNGpg1axbi4+MxZswYpKenl0W4REREfnPw4EFMnjwZDz74oEfP//bbb3HfffehV69emDVrFqpUqYJx48bhxIkTABwnEidOnIikpCTMmjULNpsN48aNQ15enj9fBhERUalYrVakpKTgwQcfFF7KX9j777+Pl19+GQMGDMDMmTORl5eHMWPGICMjAwDw66+/Yvr06ejZsydmzJiB48ePY9q0aRzRSkRE5aZUOU71PPnkk6V6/p49ezB58mRMmDABANChQwd899132Lp1K5KSkvD555+jZcuWeOCBBwAAnTt3xq5du/DFF19gypQpZREyERGRX7z55pulen5KSgpmzpyJYcOGAXAcE1esWIHff/8dtWvXxqJFi3D99ddj2rRpABy545KTk7Fy5UoMHDiwzOMnIiLyhiRJ+OCDD0q1zqFDh/Dwww/j+uuvBwAkJSWhbdu22L9/P9q3b4+FCxfilltuwejRowEArVu3Rrdu3bB161Z06tSprF8CERFRMWXScVpaBR2mBQ4cOIC///4brVu3BgDs2LEDHTp0cHtOhw4dsGPHDt06JfnSzfW3qzD4zkh6m8dUMshFKgtysiqFcpwqzhyniqJC0VRhTlBfGL1OuyB/pCooM6pbeBLa4KUW5DG1O3Oc2nMV2AuaTpTH1C5+rRfq669rDxPk0dRPfwrAIMepwSdclMdUtQjyxBaqV1Ucz1OtGlTn/il82/V3UUA2ymurXyaJ3liD/dBoX9OPR5DjVPH9MgVJC8qvr2LMEGMwmzlzptvfW7duRVZWFpo3bw7AcUwsPHJHlmW0a9cOO3bsYMdpiDmycLRu2Q/7/9Utm/vTEWG9URFW3bLjJ84L161aNRoAEO7MXd22U2PkOo9Lya0Sddeb2qW+sN5Qv5yZypYMCbJJLv8zS5zB4umnn3b7+8cff0RMTIzrUv0dO3Zg/PjxrvLo6Gi0bNkSO3bsYMcpEVUMkowyuljc/0yTUqB0AtJxWmDBggVYunQpsrKy8NJLL7k6S0+fPo2EhAS351atWhWbNm3SrStcVhApK4iQHP+yj5CUS/uW4Fe94eRQgjdeODmUQb2aoIPIlz4IWRCvtVDAEZLF7b5gsihvqIJtaga/jOyCdVXDTjQ/dZzKjpginR2nBfcAANnDGZNKEGHRnzFJFXwSDX9c+qvjVBF0nBZ6KRHOiUMK7gFxU9hEE64ZnBiAoFzU+W/0/S363Ig+G7Jgm+GB/XolE3rhhRfwww8/QJIkvP/++2jYsCEA/WPi33//rVuXqtphtztuAFz3VLJgaSdVsH1Z0/8uCjeYRFBUHmERH2QK1i16DwAWwQFV9FoAwG5wstGsgmVfCjaqyvYgsfvvvx9btmxBpUqV8Mknn6BKlSrIzMxEVlYWqlat6vbchIQEpKWl6dZlVzXY7SrszpEPdrv3v3MqAraTMbaRZ9hOJbOrHGVidgH9Zd+nTx80btwYv/76K2bPno2GDRuicePGsNlsrllcC8iyLPxH6FN1O7v9Pb+x/sQddMlbdfoEOoSgt6BZz7Kp6IqyqSZYzeuhP4EcERkbMmQI2rdvjx9++AGPPPIIPvroI1SvXh12u73Ux8Sjf/3p9vfBlN1+iTnUBHM7iabHfL6bLzPYx5fq2U90KTwJ6AXd5504tMfLeEJDMO9LRMFo/PjxSE5OxrJly/DQQw/ho48+ch3n5CIDFxRFgc1m063rwLFUt7//OHSi7AMOQWwnY2wjz7CdKNQEtOO0fv36qF+/Pnr16oVTp05h7ty5ePnll1G5cmVcuOD+j/GLFy+icuXKunU9enwzjuZeRISkYH7jXphyaC1yNOePSp9GnIpGuOmvZ5SvXDha0iAmEdEIOKv10o/sCMmCt+r0wV1/r0KOZoOs+DDiVBWMOFV9uFTfYF1/X6ofKStY0KwnJqf8jOyCkRI+XKoffUT/46aG6a8nGQ3SCMSI00L1RigK5vXoimnrNiLH+Q9c4YjThHzdMkmQHgCAH0ec6q8r+mzIgnjqWSvhsZpXijdsRINvXwjlxQwxmkDTpk3RtGlTJCcnY/jw4Vi0aBFmzZqFSpUqlfqYWL9RU0RERsFut+Ngym40adaqWOcrXRIs7ZSdp/+F/+OfqbplC9YfE9YrulT/75P6nZ8AkJAQBcAx0vSJLhF4YlOO61L9Xs2r6653S8e6wnpjIkJzVH6w7EvBJic7q9gJnVKRTJTewSxxBpmWLVuiZcuWuOaaa9CnTx8sX74cI0eOhMViwcWLF92ee+HCBdStq/8dc1m9RERFhMFuV/HHoRNIalwbihKal4+WBbaTMbaRZ9hOJcvKySt2QqdUJAnmuVQ/NA+CAflX69KlS1GnTh107XpphFq1atVcMwhffvnlOHDggNs6e/fudeV7K0muZke2ZnN1kubAhmxnx6ko/55Rbk9huajI8Fp9Uaeffpkv8dq04j/IcjQbsjUbLIJLAI0IO8k08Y8Gm6DTVdQhC1zq4Cy5UNC+RpcH2pxt6OwUy87RkF0wvF4VXGZu0NFrq6R/ZlzJFr3nwmqFnZ+awf6ievubroTf9jl2O7I96Ti1CfY1gw+OJOjMFZ4gMUgBIKpXFiRlFWW4yBKkJCAq7IMPPkCHDh3QqlUr17KqVau6fijqHRNF+U1lWXHrtFEUhZ04Hgh0O8VE6m97YGv9ToL+SeJOShGbweV0FucPH7vdjj/3/o5PJnZ2tZFBRp0KLdD7UrCRZbYFFWe32zF//nz069cP9es78iIrioIqVarg4sWLUBQFjRs3xoEDB9CxY0cAjvzyKSkpGDp0qG69iiy5ddooisxOHA+wnYyxjTzDdnKn8B9MpheQvXn//v145plnkJ6eDgD4559/8MMPP7hynA4YMACrV6/G3r17AQDbt2/HL7/8gkGDBgUiXCIiIr/Zvn07XnjhBWRmZgIA/vzzT2zcuNH1I3HgwIH48ssvXTlNf/jhBxw6dMg1AzEREZEZKYqCH3/8Ea+99hry8vIAOCZITElJcTsGLlq0CGfPngUAfPTRR1BVFT17llEqLSIiIgMBGXE6Y8YM3HfffejVqxeqVauG1NRU9O/f3zVjYseOHXHbbbdhxIgRqFq1KtLT0/Gf//wHzZo1C0S4REREfvPYY49h5syZ6NatGxISEnDq1CmMHj0a/fv3BwD0798fv//+O66//npUrVoVGRkZePHFF5GYqD+jORERkRm8+OKLuPfee3HllVeiUqVKOHv2LGbMmOEaUDN+/Hjs27cPPXv2RFxcHFRVxeuvv46oqKgAR05ERBVFQDpOo6Oj8c477+DChQs4ffo0EhMTER0d7facKVOmYNSoUUhNTUWNGjWKlRMRhTpJE6caCRZmiDGYVatWDR999BHOnj2Lc+fOoVatWggPD3d7zsMPP4w77rgDZ86cQZ06dYqVExGFMslEOU7NEmewaNiwIZYtW4a0tDRkZmaiVq1aCAu7lPzfarXilVdewZkzZ3Du3DnUq1cPVqt+3mYiopAjSTBNAu0QPQgGNDN/pUqVUKlSJd3ymJgYxMTElGNEREREgVGlShVUqVJFtzwuLg5xcXHlFxAREVE5qVatGqpVq6ZbnpCQgISEhHKMiIiIyCFkpjSVCt1Q9LEPw6FEc9WIOtMNJ/TxcsIqo0luFA9nAC/YhiRrkDVNOKu4L1SjSXl8OSHh7eRcnk7cVfi+4LFoBnvRxEUAJP3JkqFaBe+5QbyiiZg0o094WZwQKulDJyJ6PQYTbAmbwoeMzZJogi3RJGWiCaA4CpOIyokvcw6EWTz78iw41sgSJ4UiIiIiooojZDpOiYhCjgZzdMCaIUYiIjItx7lZc/TYmyNKIiIyDUlGgOZ1Lz3RiCQTC81XRUREREREREREROQDdpwSERERERERERERFREyl+pLkua4FfyNQrlCfcjf6W0OTmGOTR8Y5WuVBTlFLYXyMVok1bXMoqk+5YEVMarXX9sV5so0yKMpJIrX4DSEpogKBUVG4YpynBqsWha0Qveu7XnbxIb5ZwVlgvfVsFpVkGNW9L4RERERERERUcgKmY5TIqJQI8Gn8z7lhvnciIjIn2R4cCI5SPByPiIiKluSiXKHmuRgXUpmaX0iIiIiIiIiIiKicsOOUyIiIiIiIiIiIqIiQuhSfc1xTatbXlPHY2/zlDrW1b9OVvPXNUOCbRq9FqVQHtOi5EJlsjPHqSyrkDX9dQqoqn/62H15b4TXbIkubza69LlobtzC+5WoGQT5ZQGDHKfCFb1cDyifkfJyoXt/X1Yu/Mx5nyhW9FnWBPVKwnrLoPE1zX8Jk8uSGWIkIiITkyCZ5vI/s8RJRESmIEkwz7HFLHGWDkecEhERERERERERERXBjlMiIiIiIiIiIiKiIthxSkRERERERERERFRECOU4JSIKMZow5XHwMEOMRERkWpLkY178cmSWOImIyCQkGeYZ82iWOEsnZDpOJVmDLGuQnb0MsqxBDsEJS2TB5E8AXK+/5LLij2XJsWvbVfG/8uyCSXBsNv1Zj2SDCZNU0XaNJt8S9iiV/8RdRpNDqRbRJEPeBuRHHsakOV+3JmvQnJ85eyW7/gq+vDXeNpS/2lcYTzC+qURERERERETkqdDsDiYiIiIiIiIiIiLyQciMOCUiCjkazDFw1QwxEhGRaUnO/8zALHESEZFJSBLMM+YxNI+BZml9IiIiIiIiIiIionLDEacGNEGeTWEKVaP8nAKivKBGCeclQc5FtVCRWmiZqgF2VdyHbrfrl4vK8nL1858C4pythilqRW0saidP3xqp0H3BY0EzSQY5TjVBUwTjgD1P03cWvC5NKbTYoC30N+rdar7WK/rcCHelstjPiIiIiIiIiCgoccQpERH5zbZt2zBt2jQMGDAAkydPxi+//FLsOT///DP69euH1atXFyvbtWsXpk6dihtuuAHTpk3Dnj17yiNsIiIiIiIiInacEhEFK0kDJNUEN50Bu7t378bEiRPRqVMnPP/88+jcuTOmTZuGvXv3up4zf/58vPPOOzh9+jQyMzPd1j9x4gTGjx+PVq1a4fnnn8fll1+OCRMmIC0tzZ/NTkREQUaWzHUjIiIqM5JsrlsICs1XRUREAbd//36MGjUKEydORMuWLXHrrbfi8ssvx/r16wEAFy5cwN69e/Hhhx8iIiKi2PpLlizBFVdcgbvuugtJSUmYMWMGmjZtii+++KK8XwoRERERERFVQBUix6koT6nhuqp36xptU5hTUbCqIsgJ6ij3LMdp8TJxvN7mOFXthskl9csM0mSKc8yK1xUqaEO3e+djUeJPo13For+uJkwoasCXkQ3e5ul1y3Hq+EOzatBcbSao15fX6iWj/LOiE2Oyov+ZE+UjFpVVFEOHDi22LDMzE9HR0QCA2NhYvPbaa5B0vgd27tyJjh07ui1r3749du7cWeaxEhERERERERVVITpOiYhMqVCffVDzMMZFixYhMzMTAwcOBADdDtMCp0+fRkJCgtuyhIQEXqpPRFThSJBMM+uiWeIkIiJTkCTjWcKDhQ+DFoMZO06JiMjvFi1ahLlz5+K///0vYmNjPVrHbreX2LmqquKR90RERERERERlgR2nRETkN5qm4bnnnsOaNWvwySefoFGjRh6vGxcXhwsXLrgtu3DhAqpUqVLWYRIREREREREVw45TIiLymxdffBGbNm3C559/jqpVq5Zq3WbNmmH//v1uy/bs2YMWLVqUZYhEREREREREJQqZjlNNlaCqElTnZZ2qKhlOeORYUfwc0SRPvqQeFG1VNHGU0eRQonU19dIMOJozAk2TXDcRVTQBlE2/TDOYHMqep78LSoLJlBwbFk1eJCgz2i2kEu6dj0WpRURtDwCqIppYyodJp/w0OZQmmn2rUPsWTAilKZprkivJor+fGrWTiLcTvcmitgcgK3bdMiWAk0NJWkDm0io1vRg3bdqE5cuX4+uvvy51pykADBo0COPHj8f27dvRvn17rF+/Hhs2bMB9993nY8RERGQmUqF/iwU7s6ShIyIik5Bk8WzGQUU2xxwdpRQyHadERBRcPv74Y2RmZmLw4MFuy3v16oWnn34aq1evxuOPPw4ASE9Px1NPPYUXX3wRV155JV566SW0bdsWs2bNwuTJkxEWFga73Y6nnnoKTZo0CcTLISIiIiIiogqGHadEROQXjz32GLKzs4stj4qKAgB07doVn376abHyyMhI1+OxY8di+PDhOHPmDBISEhAWFua/gImIiIiIiIgKYccpEVGw0jTHLdjpxFi9enXhatHR0YiOjjasPiwsDDVr1vQqNCIiMj8zXf1upliJiMgEJMlEl+pLvFQ/uEnOvIvOf65okisPo7d5EQGD91yYR9P7vUWUA9KX/JAiRn0zquplHlNB/lNHxYKYjL4bRDH768Mqem+M4hXlvBTtoka5MkXrGrWDl+0kR9guPXbm3ZVj8iGrducyP+2ngoCFTSjIUwoAFot3OU4Vwf5gEeRNJSIiIiIiIqLgF7COU03TsGnTJpw4cQKJiYno2rUrLJZL4aiqik2bNuHvv/9G3bp10aVLF0jMtk5ERCHIZrNhw4YNOHXqFOrWrYtOnTq5HfPy8/Px66+/4tSpU2jSpAmuuOKKAEZLRERUdnJycvDrr7/i3LlzaNy4Mdq2betWnp2djfXr1+PcuXNo1aoVmjdvHphAiYioQgpIx2l2djZuueUWnD17Fm3atMHOnTsRFRWFxYsXIzIyEjabDVOnTsWRI0fQrl07zJs3D82aNcPbb78NWTbLEGUiIt9Imk+D18uNGWIMZunp6RgzZgwsFguaNWuGt956C40aNcKCBQugKAoyMzMxduxY5OTkoEWLFnjjjTfQp08fPPHEE4EOnYioXMiSBM0kAyg40KN0jh49irFjx6JmzZqoW7cuXnzxRfTs2RMvvvgiACAtLQ2jRo1CbGwsGjZsiJdeegm33HILbrvttgBHTkRUTiTZRJfqmyXO0glIx+mnn36Ks2fP4uuvv0Z4eDiysrLQp08ffPPNNxg2bBi++eYbHDp0CCtWrEDlypWRnp6OAQMGYPXq1bj22msDETIREZFfzJ07F5UqVcKnn34KWZZx+vRp9OrVC7/++it69OiBjz/+GKqqYvny5QgPD8fx48fRv39/DBkyBK1btw50+ERERF575ZVX0LZtW7z55psAgAMHDmDAgAGYPHkyLrvsMrzzzjuoV68e3nvvPciyjF27dmHUqFG48cYbUatWrQBHT0REFUFAOk4HDRqEvn37Ijw8HIBjhuW6devi1KlTAICffvoJycnJqFy5MgAgPj4evXv3xtq1a3U7TlVNctycJ3lV5zKfeZnH1GjLkiAHpCg/pGxQsezl0C9RDlMAUL3NYypaDxDmOJUM8qNqYeK8lX4hejkGbW+tlKtblp8pmCncl3yhRjuiYP8W7aOF834qzrNfiqxBkZz5TgV5Qf01EEOU/1eUwxQALLIgj6mgHUR5TMMVm24ZUWGTJ08GANcVFVWrVkXVqlWRlpYGAFi7di369+/vOmYWXMr/448/suOUiIhM7f7770dkZKTr76ZNm8JisSAtLQ2XXXYZfvrpJ9x7772uY2Tr1q3RsGFDrFu3DiNGjAhU2EREVIEEZBxtQkICateu7fr7yJEj2LNnD7p37w7AcclG3bp13dapW7cuDh8+XK5xEhER+VtiYiISExNdf+/YsQNpaWno0qULAP1j4pEjR8ozTCIiojJXp04dJCQkuP7+7rvvEBUVhTZt2iAvLw8nT57kMZCIiAIqYJNDFUhNTcXtt9+OCRMmuEbOZGVluZ15BICIiAhkZ2fr1hMuyYiUFURIiuP5klI23cJ+GnEqC3JUWAUrRxhUXDDir+RtKoXqsbjdQxLvCrKs6JZpgjJh+xmQDNbVRMNvfcm56BxhGOl8XZGFXp8k2KZsMBxY9J5bRG3opxnqAXg94tRSaDW3z5yTLNgPAzHiVDHYqEUQryIaySqoN0wSvKelwfyhFcqhQ4dwzz334D//+Y/rBGN2djYiIiLcnmd0TFRVO+x2xw2A655KxnYyxjbyDNupZKrqe3swc2jo++233zB79mw888wziImJwfnz5wGg2DEwMjISWVlZuvXYVQ12uwq73fHvu4J7KhnbyRjbyDNsp5LZVR9/0EmS/35ElzmzxFk6Ae04TUlJwbRp03DjjTdixowZruURERHIzXW/rDk3N7fYQbOwp+p2cft7fqPeZRtsiHol8fpAhxD0FrToEegQTGEeP3NEPtmyZQvuvvtu3HXXXRg9erRreXh4OPLy8tyem5eX57p0vyRH//rT7e+DKbvLNtgQxXYyxjbyDNuJqHR++OEHPPLII3jyySddqdkKjnMl/S4sPEq1qAPHUt3+/uPQiTKONjSxnYyxjTzDdqJQE7CO0927d2PSpEmYOXMmhg8f7lZWp04dnDx50m3Z8ePHi12mUdijxzfhaN5FREgK5jfqjSl//YgcrQzO9vtrxKlgNJ81TD83YoRVnDdREeRqzLO5jzh9JfF63Jv6HXI0G3JyBTk2AeTm6O8qWq5gZJ0vOU6NRpyKcpz6clJHuTTidEGLHpi8dx2ynSMlJKtgRK9FfGZNlH82P8sqWDEIR5yGXfpsRUgK5jXqjWmFPnPBluNUCUCO09pKZdwbx85k8swvv/yCGTNm4Pnnn0dycrJbWZ06dXDihPs/QI8fP46mTZvq1le/UVNEREbBbrfjYMpuNGnWCopSRqOgQxDbyRjbyDNsp5LlZGcVO6FDVOCrr77Cc889h3feeQcdOnRwLY+IiEC1atVw8uRJt5zex48fd6WzKcll9RIRFREGu13FH4dOIKlxbShKaM70XBbYTsbYRp5hO5UsKyev2AkdMpeAdJyeO3cOU6ZMwX/+8x8MGjSoWHm3bt3wwQcfYMaMGQgLC0NmZiZ+/vlnzJo1S7fOXE11dG45P585mt3V2eUL4VxAosviDTq7FEHHkiboTZQg7jiVNf3t5haqVnP2XmWrduRodmQZXFKWL5ioSROEZDTBk6jj1FC+fpEWIeqRNai3oA2dd9maHdkFHYKCznhFE78YUcdeniAoyZ/XagveHlHHaYT10ug3uzPNg92aC5tzZxDt/6J2ME5xIVhX1PnpU8epfploPYusPxrQU5JmOOdYUDBDjMHs2LFjuOeee/Dmm2/iqquuKlberVs3rFq1CuPGjYMsy0hLS8OWLVtck0qVRJYVt04bRVHYieMBtpMxtpFn2E7uRCmfPBYiV/8dPXoUaWlpaNSoEeLj4w2ff+TIEaSnp6NOnTqoXr16OURYvnbu3Imnn34aixYtQosWLYqVd+vWDd9//z369u0LADh48CAOHjzomhujJIosuXXaKIrMThwPsJ2MsY08w3ZypxjN8O2REDkImlRAOk7feecdWCwWnDp1CvPnz3ctb9SoEZKTkzF48GB89tlnmDBhArp164Yff/wRtWvXRr9+/QIRLhERkd+8/PLLSExMxJ49e7Bnzx7X8qSkJHTt2hXjx4/H119/jalTp6Jt27b43//+h+7du6Njx44BjJqIiEojPz8fM2fOxMaNG1GvXj389ddfuPfeezF27NgSn3/u3DncfvvtOHr0KGrVqoU///wTAwcOxOzZsyGZJtedseeeew6NGzfG+vXrsX79etfyLl26oHXr1rj99tsxbNgwzJgxA40aNcKyZcswevRo1K9fP4BRExFRRRKQjtOGDRsiOTkZ//77r9vyKlWqAHBclrF48WIsX74cx48fx7BhwzB48GCeuSciopDTqlUrVK1atdgxseBHYXx8PJYtW4bly5fj1KlTmDp1Kvr37x+IUImIyEuff/45UlJSsGrVKlSpUgXbt2/H+PHj0aNHjxI7AV977TVYLBasXbsWYWFhOHToEPr374/+/fujU6dOAXgF/tGxY0dkZWUVOwZmZmYCAOrVq4cVK1ZgxYoVOHfuHB5++OFiKW2IiIj8KSAdpyNHjjR8TlRUlNvkGERERKFoypQphs+pUqUKJk6cWA7REBGRP3z//fcYOnSoa6BI+/bt0bp1a3z//fclHgcGDRqEuLg4hIU55iFo0KABIiIikJGRUa5x+5soFVuBGjVqYOrUqeUQDRERUXEBmxyqPInzlIqT84nyPIrylBrlOJW9zKnoL0btIExiKGpgo9yH/sqNKIrJ4L0peM8L3xfkGZVEqVqM9iVRfk7Fu5ygjieItileVRSzKJ+opdC+rzhz/SqKCoszz6to0iTRZEuibTrW1f9shAm2GSaLc5x6m8dUFuzAEZI4H7FHNO1Szt1gZoYYiYjItKRC/zergwcPFrssv1GjRjh06FCJz2/Xrh0AICUlBceOHcPy5cvRtm1bYW5PIiIKRbJBR0QQ0UwSZylViI5TIiIiIiKiQMnIyEBUVJTbsujoaBw/fly43ooVK7BmzRpIkoSZM2fCarX6M0wiIiIqgh2nREREREREfmSxWGCzuV+Nkp+fb9gR+sADD+CBBx7A7t27MW7cOISFhaFXr17+DJWIiIgKYccpEVGQkjTDDBBBwQwxEhGReUkSzHOlvk6cNWvWxKlTp9yW/fPPP2jQoIFH1bZq1QqdO3fGTz/9xI5TIqKKRJI8yMEXJMwSZymFTMepLKmQZRWyM/eDLGmuPKKi984oF6k4j6kgT6lBT4I4x6n3OSA93abszEspyypkTTXOyWrRL7fbBevmly7GUtH031gpS9FfrZJBUAVt4Xbve8+QaD8U5dL1JU+p0aqyl3l6C+cEdeU4lVUozhynVkG9opyhojJHvfq5SsMV/ZyiEYr4PRfGJAnaSFAWgxzhNomIiKji6NixI9avX4+bb74ZgGPW+O3bt2PEiBElPv/GG2/E4MGDMWHCBNeyf//9F82bNy+PcImIiMgpZDpOiYiIiIiIgtHEiRMxePBgPP/882jTpg0+++wzNG7cGD169AAAnD9/Hnv27EHHjh1htVoxdOhQvPHGG1BVFXXq1MHatWtx9OhRDBkyJMCvhIiIqGIJzSmviIiIiIiIgkT9+vXx2Wef4eLFi1i+fDnatWuH9957D5Lz0qJ//vkH8+fPR25uLgBg3LhxeOmll3DgwAEsW7YMsbGxWLFiBerUqRPIl0FERFThcMQpEVGwKpssEf5nhhiJiMi0QiVj2mWXXYZnnnmmxLJmzZrhgw8+cFvWu3dv9O7duxwiIyKi4CXBPGMeQ+WI7c4srU9ERERERERERERUbkJmxKliUWFRVddELhaLCotWMAGSYLIlg8loRJM8eTvBk6NcNOGM95P9iLaqFppMqeB1KbIGRdNgsehPugMAdrt+H7smaEI1V9w3LwkmeIL4rTGoV1B23ipeOcFxiZTkrESSNNdj4URMBm+OaH+JjtGfSCgnO0xcsXAfNZqkzLt18+2XJt9SJMW1LF9zrBMZpj8Zk2giJtHkTwBglfXLoyx5umVGk0OFC+oVbdMi6ZfFarnCbRIRERERERFRcAuZjlMiolAkOglARERUIUgwz9V/ZomTiIjMQZKMR2kFC7PEWUq8VJ+IiIiIiIiIiIioCHacEhERERERERERERURMpfqWxQVVosdFufQYIvFDqvmyD8oyhlqUQxynArzmOqXGQ1QFsXkC1WQM7RwvIozF6wiq1A0VZh3EgDsghyomiifaCVhtbAL8o0K858CXs/kbVSt7DydIBW6L1hF9L75kk9UmEvXYB8V1SsZ7GeidY32CdfzCvIKy5fyCouaWPS5EeUTBcR5TKNFZYp+GQCEy/o5UMNlm26ZKN5YNQfQX5WIiIiIiIiIglzIdJwSEYUcFYBqgiSnPkzkRkREZERiklMiIqqomOM04HipPhEREREREREREVER7DglIiIiIiIiIiIiKiJkLtW3WuwIhw3hzpHB4VYbNM2RYFCUU9Eo16go96Qv/JXjVKTwFgu2L0saZGiGuV41TT+Xo2g0dr5+6kgAgF3Wz3FqmMNUkFxVE10mZdD2anqY415RHPdnw6DaHa9fqZ6tX61BvUbleqIjc4XlufmCPLEG2xTlMZU9zHEqO3OcyrIKWbuU71R3m5J+WYQi3mGEOU4FeUwrWfTfNwCIEqwryn9qlfQ/FzFqtu85TjV4ncu3XJkhRiIiMi2JV+oTEVGFJcM8Yx7NEmfphOarIiIiIiIiIiIiIvIBO06JiIiIiIiIiIiIigiZS/WJiEKNpBlmlggKZoiRiIjMzSxXwPOQSEREZUqCeWarN0mYpcURp0RERERERERERERFhMyIU6tsR5hiR5izJz5MsUN1Tmjky0RMgZjEyV+kEh4X5NoXTaAFQLiniCYgMpqcKE8RTPBk0PSSKjqd4cP7VnA6oXAjOZfJsn69orLC1ZVYJmgn2eCsTVS4/sRGufnij7i3E1YVjqngsSxdajqroj9pkqgsTFAGADGiyaEs+pNoVTaYHCpGydEti5BEk0Ppz/4UgSzhNomIiIiIiIgouHHEKREREREREREREVERITPilIgo9GjGQ6+DghliJCIi0yq4RMoMzBInERGZhGSeHKchehDkiFMiIiIiIiIiIiKiIkJmxKksOfKRSs6RTxI0V37SUMpTqmr+6cFXDNpIFuSe9Kl9BTlOjQaxiQbiCfOfGjSh5oxJc+Ys1RQNmvM15lwM110vNj5TWK+onbzNNWpUb6D2/fM5Ebpl9Sqd1S2LFeQpdZTr5yKNt+i3fxVBGQBUkvVzoEbI3uU4tUKcV5WIiIiIiIiIglvIdJwSEYUaSXPcgp0ZYiQiIvOSnP+Zg8QENkREVIZkmOdicbPEWTqh+aqIiIiIiIiIiIiIfMCOUyIiIiIiIiIiIqIiQuZSfVly5DSVcSmvqRzEF8r4K1epSOF8l0VzwforF6Yii/vmJYuqW6b50K+viRKgGlVbEFPB88LtgOrI8Spb9eM1zBMr66/rL1HhecLy7DyrX7Yr2p8qW/XzlMZZs4T1Jlov6Ner6K8bJygDgFgvc5yGQT/3rySok4iIiIiIiIiCX8A7TvPy8mCxWCDrdLCpqqpbRkQU0jQYTpIWFMwQo0nwmEhEVJwEQDJLilPwsOit3NxchIWFQdJ5s3kMJKIKSZLMcxA0S5ylFNAjz/vvv4+2bdvi//7v/4qVffPNN+jduzdatGiBa665Bj/88EMAIiQiIvI/VVXx0ksvISkpCVu3bi1WvmjRInTr1g0tW7ZEv379sHnz5gBESUREVPby8vLw4IMPonXr1jh27Fix8jfeeAOdO3dGq1atMHToUKSkpAQgSiIi8sWXX36Jm266Cddeey3uvfdepKamCp+/ePFijBgxAtdddx3uvPPOgH73B6Tj1G6347777sPu3bsRFxdXrHzv3r146KGHcP/99+P333/HzJkzMWvWLBw6dKj8gyUiIvKjrKwsTJkyBXl5JafW+Pnnn/H666/jpZdewm+//YYRI0bgtttuQ3p6ejlHSkREVLbS09MxduxYxMTElFj++eefY+nSpfjvf/+Lbdu24aqrrsLUqVORm5tbzpESEZG3Vq5ciWeeeQaTJk3Cm2++CVVVMXXqVKhqyekMP/zwQ7z11lu49dZb8dZbb6Fy5cqYNGkSMjMzyzlyh4B0nGZlZaF+/fp45ZVXYLEUzxawbNky9OrVC3379kV4eDhuuOEGdO3aFcuXLw9AtEREgSFpmmlu5L1///0XycnJePjhh0ss/+KLLzBs2DB07doVERERGDduHOrWrYtvv/22nCMlIgoMyWQ38txff/2FyZMnY/LkySWWf/HFF5g0aRJatWqFqKgoTJ8+Hfn5+fjll1/KOVIiogCRJECSTXIr+Si4aNEiTJ06Fddffz2aNWuG5557DseOHdO9ii4tLQ0PPvggrrnmGlx22WV49NFHcebMGezbt8+fLa0rIDlOY2Njceedd+qW7927F1dffbXbspYtW2Lnzp3+DawMBWLyJ0A8KY9UqEyBo2dfkVUoUP02OZTdYEKkKlUydMvOni35zLOLLGhjUTso4teqKI6Yrc7qreF22DSbY5OK/uvJsynCeiPCvJscSvLTewMAlSL0z9Zn5oXplimF3ldFKrQvaY7Hbaqe0N+mRX9yqJph53XLACDRql8umuDJaHKoaEl/Ei2rJJgQTJDFTJXyYRNulcihUaNGaNSokW75nj17kJyc7LasZcuW2LNnj79DIyIi8qsOHToAcJxELEpVVaSkpGDWrFmuZYqioHnz5iUeG4mIKPioqoo//vgDd999t2tZREQEmjdvjl27dqFr167F1in8vQ8AFy9ehKZpqFSpkt/jLUnAJ4cqydmzZ1G5cmW3ZZUrV8bZs2d117FCQTgsCHe+pPAAvzQ1QOebZUFHjlSorGg7idYzYhcNXJbE74Mi6a8bKYs7IoXn9EUdp7JBx6kzpghJcbsHAFnUiSYoA4BwLztAFYP1RJ3eRjFZoF9uF7x3hesNdz4vvNDzrdDvdLVogjbUwnXLAEDWInTLhKMeVYO2l/T3NU3Qhproc2PwWog8dfbs2WJpbSpXrozDhw/rrqOqdtjtjhsA1z2VjO1kjG3kGbZTyVSV7UGll5mZiby8vFL/LrSrGux2FXa7499wBfdUMraTMbaRZ9hOJbMb/RYNcRcvXkReXh7i4+PdlsfHx+P06dOG62uahieeeAJXX301LrvsMn+FKRSUHaeSJBXLdaBpmu4MiwBwdyX3M46zqwzyR2gh54m4wYEOQV9ioANwmNeod6BDMIXnE/p79kTRcVR/MKpn5X4gCpf/JKDy4M0x8ehff7r9fTBlt19iCzVsJ2NsI8+wnYh8V3Cc04qcHFdVVXgMPHDMfcKRPw7pXwlFl7CdjLGNPMN2osIKfscU/d6WJKnY93tReXl5uO+++5CamoqFCxf6LUYjQdlxmpCQUOws4pkzZ1C1alXddd68sBon7ecQDgtmVxmEx89+hdwAXigb7Jfqh8OCJ+IG44lzy5ELm0+X6ttV/VGjuXbxLpZv11/33Llo8YZVP12q7xyRGiEpmNeoN6b99SNyNMdICdGl+oqgDADCw/KF5Ubx6BGOODVIlWARjKbMyvfsUv1wyYLnE/rjwTPfINeZ0qBVwj+668Yq+r2fNcIuiMJFdat+eYzgUv3KgjJAfKm+qI1El+prtgawnX9euF1DGszRO1uxT6L6nd4xsVq1arrr1G/UFBGRUbDb7TiYshtNmrWCohiN4q+42E7G2EaeYTuVLCc7q9gJnVIxU/JQs8RpAtHR0YiIiCjxGNi0aVPd9S6rl4ioiDDY7Sr+OHQCSY1rQ1ECMrWHKbCdjLGNPMN2KllWTl6xEzqlY+6DYGxsLCwWC86fd0+7d+7cOeEI0gsXLmDq1KmIjY3FokWLEBUVVebReiooO06TkpLw+++/uy3bsWMHrrzySt118mF36yjNha1MOk4D0QHqaednadctXGZ39sbY5TzYYRNfcmzAJut/Kdp0ZklzxSFYN0/W77gDIJzaTBJ0GFosBj1Rzo7KgkvV7WG5sDs7BDVBvUb9W5rm3WVqisFZGNHl+FajqGT9mPIFu75aaJsF27dJeSjI6hktyEUaZ9GfCa+KVf+yKwCoYrmoWybKcRor63eMAkCEpN8OVsFnwypoo3wtG+eEWyXyTOvWrbFz504MGTLEtey3337DbbfdpruOLCtunTaKorATxwNsJ2NsI8+wndzJhumXiIqTJAmtWrXCzp07Xb8Dc3NzsXfvXkybNk13PUWW3DptFEVmJ44H2E7G2EaeYTu5U0Rzs1QAFosFTZo0wb59+1x5rW02G1JSUnDLLbeUuE5OTg6mTJmCBg0a4Jlnngn4v6kCsjfn5eUhPT0d6enpUFUVWVlZSE9PR0aGY6KgYcOGYdOmTfjss8+QlpaG999/H/v27cNNN90UiHCJiIj8Jicnx3VMBBx5gNLT05GV5ZjUbOTIkVixYgVWrlyJU6dO4aWXXkJubi6uv/76QIZNRETks4LfgQUjkc6fP4/09HTk5DiuUBo9ejTef/99bNiwAf/++y+eeOIJ1KpVC126dAlk2EREVApDhgzB+++/jxMnTsBut+Ptt99GTEwMrrrqqhKf/+6778JqteLZZ58NeKcpEKARp2vWrMH999/v+vvZZ5/Fs88+i549e+Ltt99Gw4YN8fbbb+OVV17BSy+9hIYNG2L+/PlITAySpJdEROVA0jTxpFdBwgwxBrPPPvsMr7zyCgAgLCwMM2bMAADcfPPNePTRR9GhQwc8/fTTeOONN3Dq1Cm0aNEC//3vfwN6uQoRUXmSnP+Zg1niDA5vvfUWPv74YwCOY+Do0aMBAHfddRemTJmCfv364fTp03jsscdw7tw5tG/fHvPmzQuKH9JEROVCkhw3M9CJc+zYsTh69Cj69u0LRVFQq1YtzJkzB2FhjquMV61ahRdffBGrVq0CACxevBhZWVno1KmTWz0zZ87EqFGj/PsaShCQjtPrr7/ecKRM9+7d0b1793KKiIiIKDAmTJiACRMmCJ8zYMAADBgwoHwCIiIiKicPPPAAHnjgAeFzxo0bh3HjxpVTREREVNZkWcZjjz2GBx54AJmZmYiPj3cr79atG1q3bu36e8WKFSVOHBUbG+v3WEsSlDlOy5O/cpj6MtmSKI+pUb3CHKeFcjXKhe5lH2d2EU2eoyrivJ6iLSfEZQjXvZgdrlsmCyZUMmrDgomPwpy7RpjFBlUzzpdrlH9WRJT2xJf33JfJoapG6ecizci71PYWZx5Vi6TC6qwvXpTHVFAWp2TplgFAlJyrWxYh6b9HRrleowRtGCvIy6YIRpXkyApznBIREREREREBCA8PR3h48X6cyMhIREZGuv4OtqvNmbGXiIiIiIiIiIiIqIgKP+KUiChoaRAPyw4WZoiRiIjMy0Tp3fx0MRsREVVUkgRIJhnzaJaDdSmZpPWJiIiIiIiIiIiIyg87TomIiIiIiIiIiIiKCJlL9VVNctyck7UUfuwLbyd5MpooSDiJk4cTPPkSk+SsR5I012N/CJPFk0OJ5BvVbdGvWxZMiiSaiKkwxdleiqxBKWFGt6J8mxzK+wmeFMG6FoN1rYLJu8Jk/cmW6sRkX3oerACA2jHnked81xKt53XXjVf0J/0ymhwqWsrTLbMKJroStZGjXv1zSFYIJocSXIpgK4vzUprmuAU7M8RIRESmJTlvZmCWOImIyBwkSJBMcgm8FKJHQY44JSIiIiIiIiIiIiqCHadERERERERERERERbDjlIiIiIiIiIiIiKiIkMlx6i2jHKaivJXe5ikFvM9V6kseTV94m+sVBuuFeVcrACA2Ile3LF/1/ZyAxZlHxKLYYdGMc7Vqmjifh7f7klF+TlGeUqtBjtkIRT+TbISin+M0OX6v67GsRQAZwNVV9kOVcgAA1SwXdNdNkDN1y6ySOF5F8LmJlvTjjTJItSLaar6gVBW85zaI88t6RDP8CAUHM8RIRETmxSSnRERUYckwzw+u0BybGZqvioiIiIiIiIiIiMgH7DglIiIiIiIiIiIiKqLCX6pPRBTUNLNclkFEROQfkvM/MzBLnEREZBKS5LiZgVniLKWQ6TiVJc1xc+Z+KPzY29ySRuXe5ik1islfCr+Wou3kdQ5T+JD/1IBR/tM8v2z1EoszR6VFUmGVjPNVivJdAgZ5TGX9+i2CMkCcx1SUwxQAoiz6rVjJop9DVi6Uv7PgsePe8dgqyAsqymMaYZDjVFQe7sN3tKiF8zX9UlXw48gmWI+IiIiIiIiIgh8v1SciIiIiIiIiIiIqImRGnBIRhRpJddyCnRliJCIi8+JVikREVGFJMM/BxSRhlhZHnBIREREREREREREVwY5TIiIiIiIiIiIioiJC5lJ9WVKhyCoU5zQvhR+H6gRPvqxbppND+dCGou2qBjGJJk0S1mswiVOB0u5LRoSTQwmudTaaHCpMtumWRSj6ZQAQI5wcKlu3LF+79NUha4pzmQLV+ZWSpYXrrpuATN2yKIPJoax+GvpvF81cL7wsQrCfeR8OEREREREREQWBkOk4JSIKOZrmuAU7M8RIRESmJcE8adPMEicREZmFmS4UN1OsngvNV0VERERERERERETkA3acEhERERERERERERURMpfqK5IGi6TC4sw5aJE02D3IMhiIPKWA97kyjdbztN5iOU59yFMqovpwwZLxa9XPhynKY6p5mOM0zHleIUxWocGxLdH+4st7YxHmOBXn/RTlQDVaV/S+Ryn6+U8L52QtqEORNEjO5XZN/5xMjqb/taNCf5u+ELcCoPhlq2VAgyiNavAwQ4xERGRuvAaeiIgqIkkymHcjiJglzlLiiFMiIiIiIiIiIiKiIthxSkRERERERERERFQEO06JiIiIiIiIiIiIigiZHKeSpDluzmR7hR/7i7d5So3W9aleD1+zq508zG8qylUqyidqWK8P6/qrDQtYnecVrIodqjNLpigXqSwoM4rJl9fiS37afEEuUlGe0sJlBTlj7ZoE1dlm+Zp+1tCLaqRuWawsznFqlWy6ZaF4FkjSNEha8CcQNUOMRERkbhKTnBIRUUXEHKcBF4p9DUREREREREREREQ+YccpERERERERERERUREhc6k+EVHo0QBTXAZvhhiJiMiseJUiERFVXBLMM+YxNA+C7DglIiK/UlUVZ8+eRVxcHBSleP7b/Px8nD17FlWqVIHVanUtz83NRVpaWrHnV69eHWFhYX6NmYiIiIiIiKjCd5z6a4InX9b1ZbIfb4kmfwK8n8TJaL1AtKGnkzgVdMuEK/mQkG+8zTKYkCqYZKn6HVPn7VGux4oWDgC4YI+CXXJ0iokmccrRrLpl59QIYUwRUqZuWZQP7W8XFYpGfAqGlYj3sopj3rx5mD9/PiwWC3JycjBu3DjMmjXLVf7hhx/ijTfegCzL0DQN9913H0aMGAEA2LRpE6ZNm4ZKlSq51Tl//ny0adOmXF8HERERERERVTxB23F67tw5LFmyBH///Tfq1q2L4cOHF/vxTEREwWvlypVYsGABPvnkEzRv3hx79uzByJEj0b59e/Tq1Qvbt2/Ha6+9hgULFqBDhw7YvHkzbr31ViQlJaFly5bIzMxEgwYN8N133wX6pQRcamoqli5dilOnTqFJkyYYPnw4wsPDAx0WERGR3x0+fBhfffUVzp49i6SkJAwZMqTEK1iIiIj8ISgTJWRkZGDYsGHYvHkzGjVqhA0bNmDkyJHIyckJdGhEROVHNdGtBHFxcXjyySfRvHlzAEDLli3RokUL7N27FwCwYsUK9OnTBx06dAAAdO7cGT179sTXX38NwHEsiI6O9rb1QkZqaioGDRqEP//8E40aNcI333yDSZMmQTNF/lsiIt9JJrtR2dm/fz+GDBmCM2fOoGHDhvjggw9w3333BTosIqJyJF1K9h3stxA9CgbliNMvvvgCUVFRmD9/PhRFwejRozFgwACsWLECw4cPD3R4RETkgS5durj9nZ2djWPHjqFRo0YAgAMHDuCaa65xe87ll1+OrVu3AnB0nMbExAAA0tPTUalSJVgsQXnY8qv3338fLVu2xBtvvAEAuOmmm3DNNddg3bp16NmzZ4CjIyIi8p+5c+eiX79+ePrppwEA/fr1wzXXXIOUlBQ0a9YswNEREVFFEJS/QDdt2oQePXq4LsGwWq3o1q0bNmzY4FXHqd/yaPopT6lRDk4RVdMfRFw4j6nmfKxBggrJ6xymRgKXQ1a/DS2yZzlOLc5hdBZZhep8LM6d6p/RX77kiVV8iClX1f96KJz/1KI5HmerYbA5t5ej6edHjdDydctyVP38pwCQJenHZJX167X66cSXaMh+UA7nDyBVVfHII4+gQYMGuPbaawE4UrIUTcESGxuL8+fPAwAuXryIY8eO4eqrr0Zubi4uXryIIUOG4NFHH3WbRCrUbdq0CcOGDXP9HRMTg06dOmHDhg3sOCUiopC2adMmV6cpACQmJqJ58+bYuHEjO06JiKhcBGXH6cmTJ9G9e3e3ZTVr1sTvv/+uu04YLAiHFeHOlxRe6KX51Ikp6igTlEk+bdOHjlNBd41WqOM0zNk+BfdGk0MFgi/vmyJoB4tB+xZsNwxWt3ujmPw1qZfReyPartWg+05UbhV02Fq0S7kVFWcnqVKos1TW9HMvypr+VEySJn5vNFV/XU3wdab6sHuLVlVFk0OpvueflDQNkgkuxzaKMTs7G7NmzcKZM2dcVxIAgKIosNvd31NVVSHLjv2yZcuWUFUVY8aMQbVq1XDo0CGMHz8etWrVwrRp0/zzYoLQP//8g8TERLdlNWrUwMmTJ3XXUVU77Ha7q32LtjO5YzsZYxt5hu1UMlVw/PaIma7+M0ucJpCTk4OzZ8+iRo0abstr1qyJEydO6K5nVzXY7Srsdse/KwvuqWRsJ2NsI8+wnUpmV338PSfJgFkmohb8PjazoOw4zc3NLTaayGq1Ii8vT3edqdHXuf39n9ib/BJbqJkVxdQHRu4IGxPoEAJD9N1cwkcxOf9eYbm/nS//TZIHLl68iFtuuQWJiYn44IMPEBER4SqrVq0azpw54/b8U6dOuToJk5OTkZyc7Cpr3LgxBgwYgI0bN1aojlNvjolH//rT7e+DKbv9EluoYTsZYxt5hu1E5Lvc3FwAKPUx8MCxVLe//zik38lKl7CdjLGNPMN2olATlB2n0dHRyMrKcluWmZnpynVXmKo6zmZ8mb0Bp9WLsELBxOhkLMxcjXw4zm5LfrpcXDSq1Jd+dn9dql84WissGBd5LRZl/4B82NxGowYLX0btKoI2VAwv1XfcW2DBcGs/fJ7/LWywGcYUjCNOLbJ4hIcobUG4ZNMtq2zJdj1WYEVn21hstnwEOxyXy8dZMnXXjVX0J3mLkcQTwFWRs3XLYmT9eH2Zd1U0ZtciOKOWb6uFcxenu76jKiJVVXHbbbehcePGeO6551wjSQu0bdsW27dvd1u2detWXHed40TYihUrEBcX53Y5enp6OuLi4vweezDx5phYo3ZdhIdHwK6q+PvIIdRp0BiKzAQSethOxthGnmE7lSw3Nwf/njheoY+JVHpRUVGQJKnEY2DRKzGAS8fAejXiERFmhaqqOPh3GprUqVbs3yB0CdvJGNvIM2ynkuXk5ePYv+k8BppYUHacNmrUCIcPH3Zb9tdff6Fx48bFnltwJnJo5JVuyydGJxd7LhU3LvLaQIcQ9IZb+wU6hOBTQh9lZ9vY/2fvvuObqvo/gH9ukm7KKKNQKJS9N7LK3lsEZDxsBGQryqM/HnGLE0UUFFCQIXuIspG9yhYpe7bQAi2r0J0mub8/0oSm7T03dKVpP29eedHk5J77zclNTnJy7vcIy7NbTM7v0i6JiYnpDnDlB6tXr8bt27fxv//9D9evX7fe7u3tjVKlSmHAgAFYtmwZ5s6di/bt22Pz5s0IDw9Hv37mMwYeP36ML774Ap9++ikqVKiAY8eOYevWrVi4cKGjHpJDVKhQASEhITa33bx5M83iW8DzPvF++B2b28NCbmRbfHkJ20kd28g+bKf05ec+kV6ci4sL/P39ERISgvr161tvv3nzJjp27Jjm/pY+8Pb9xza3Xw97kL2B5hFsJ3VsI/uwndLHPtB55cqB044dO+Ljjz/GpEmTULx4cYSFhWHv3r2YO3dumvsWKlQIAQEBcHNz468aRJRrmEwmJCYmolChQhmvRAbgBDlOlSY9nz9/HjqdDpMmTbK5vVWrVvjoo49QqlQpLFmyBD/88AM2b96M8uXLY+nSpShSpAgAYOTIkXB1dcXixYvx9OlT+Pv7Y8GCBWjevHl6u8uzOnTogFWrVmHo0KEoUKAALl68iH/++QcffvhhmvuyTySi3CizfaKU/M8ZOEuczqJDhw5Yu3YtevToARcXFxw4cAARERFo27ZtmvuyDySi3ChLvheyb3EoSZZz37dyWZbx9ttv48SJE6hTpw7++ecfdOrUCR9//LGjQyMiynZxcXG4dOkS5n52EHfv5P7srX7+hTBpRitUr14dnp6ejg4nz9Hr9Rg9ejTCwsJQtWpVnDp1CiNHjsSECRMcHRoRUbay9IdeJcpD6+rh6HDsYtTHIzbyFvvELPL06VMMGzYMer0eZcuWxcmTJzF9+nS8+uqrjg6NiChbWfrAakWS4Omifv/cIC4JuPzEJc/1gblyxqkkSfjuu+/w77//4s6dO5g4cSJq1qzp6LCIiIhynKurK5YsWYJTp04hMjIS06ZNSzd1DRERUV5TqFAhrF+/HidOnEBUVBRmzJgBf39/R4dFRET5SK4cOLWoW7cu6tat6+gwiIgcRHaOU/WzaWE0ek6j0aBx48aODoOIyCEkyXxxBs4SpzNxcXFBYGCgo8MgInIMSXKeM/WdJc4XlKsHTl/UpUuXsG/fPphMJrRp0wa1atVydEi5Qnx8PBYtWoR69eqhRYsWNmVGoxHbt2/HtWvXUKJECfTo0SOTuTec071797Bz505ER0ejcuXK6NSpk01upHv37mH79u149uwZ6tevb7PKd34hyzL27t2LixcvwsPDAy1btkTVqlVt7nPkyBGcOnUKnp6e6Nq1K8qUKeOgaIno9OnTOHr0KFxcXNCxY0fOUk0WFRWF3377De3bt0edOnVsyhITE7Flyxbcvn0bZcqUQY8ePeDh4RynB2elGzduYP/+/YiPj0ft2rXT9Hk3b97E7t27kZCQgObNm6NRo0YOitRxDAYDdu3ahWvXrqFQoUJo3769zSw4WZaxe/dunD9/HkWKFEGPHj1QrFgxB0ZMlL8cOnQIp0+fRoECBdC1a1eULl3a0SHlCuHh4VixYgX69++PgIAAm7Jnz55h8+bNiIyMRMWKFdGtWzfodHlquMAu58+fx9GjR2E0GtGwYcM0P1xfvHgR+/btgyzLaNu2bb48MzYhIQHbt29HaGgoihYtis6dO6NEiRLWcoPBgG3btuHGjRsoUaIEevXqBW9vbwdGTJQ5eSZr9o4dOzBw4EBERkYiKioKQ4cOxZYtWxwdlsOFh4djyJAhWLZsGf7555805ePHj8e8efMAAHv27MErr7yCJ0+e5HSYDnXq1Cl069YNFy5cgMFgwNdff43x48dby69evYoePXrg3LlzMBgMeP/99/HVV185MOKcZzKZMHHiRHz99dcwGo0ICQlBv379sHnzZut9vvvuO7zzzjtITEzElStX0LNnT1y+fNmBURPlX8uWLcPrr7+O6OhohIeHo0+fPjh27Jijw3K4K1euWPvEK1eu2JTp9XoMHjwYK1euBACsX78e//nPf6yrNOcX27dvR9++fREaGoqEhARMnz4dH3zwgbX8+PHjeOWVV3Dr1i3Ex8djwoQJWLp0qQMjznmJiYkYMmQIfv31VwBAcHAwunbtavMae/fdd/Hll19ClmUcP34cvXr1wt27dx0VMlG+8vXXX2P69OnQ6/W4dOkSevbsiatXrzo6LIc7fvw4Ro8ejcWLF6d5P3ry5AleeeUV7Nq1CwCwcOFCm+9D+cWyZcswfPhw65jC+PHj8dNPP1nLt27div/85z949OgRoqKiMGTIEGzbts2BEee8p0+fonfv3ti4cSM0Gg2CgoLQuXNn62tMlmWMHj0av/zyCwBg586d6Nu3L549e+bIsIkyJc/8hGTpIAcOHAgAqF69Or766it069YtX6+qOGnSJEybNg3z589PU3bkyBH8888/2LVrF4oUKQKTyYQhQ4Zg6dKlePPNN3M+WAeZPXs2+vfvj+nTpwMAevbsie7du+PmzZuoUKECfvzxR3Tt2hWfffYZAKBHjx7o27cvhg4dCj8/P0eGnmPOnj2LQ4cO4cCBA/Dx8QEAeHl5Yc2aNejZsyciIiKwaNEirFmzxjrT++OPP8b333+f7rFHdjIlX3I7Z4gxH4mPj8fs2bMxe/ZstGnTBgBQsmRJfPPNN9iwYYNjg3OgpKQkTJkyBd9++y2mTp2apnzz5s2IiorC5s2b4eHhgcTERPTq1Qt//PGH9bNFfjBr1iy88cYbGDlyJACgadOmGD16NN599114eXlh1qxZGDNmDCZNmmQtf/PNN/Hqq6/mqUUARPbs2YOwsDDs3r0b7u7uAMyza9atW4emTZvi4sWL2L59O3bu3Gn9nDB+/HgsWLCAC51mQh49+4+y2L1797B06VKsX78e1atXBwB88MEHmDNnjnWySH509+5dfPbZZ1i4cCE6d+6cpnz58uUoUaIEfvvtN2g0GowYMQKdO3fGoUOH0LJlSwdEnPP0ej1mz56NmTNnolu3bgCAypUr46uvvsKECRMgyzK++uorvPfee9bFyapUqYJvvvkGXbt2hZRP8nRs3LgRLi4uWLp0qXWcZeDAgfjzzz/x3//+F/v27cOVK1ewc+dOFCxYEEajEQMHDsSKFSvy5WB8lpA0ztMJOkucLyhPjCjevHkT4eHhNp1A586dERkZme9/XZw7d65iTqDDhw+jcePGKFKkCABzDr1OnTrh8OHDORmiw/322294++23rddLlSoFAIiJiQFgHmBOeWxVq1YNZcqUwdGjR3M2UAdq0KABTp8+bR00BYDo6GjrsXP8+HH4+vrapMfo3Lmz9TQXIso5Z8+eBQC0atXKeluXLl1w/vz5fHdGQUoajQbLli1TTONz5MgRtG7d2npqvpubG9q2bZvv+sTt27djyJAh1uulSpWCLMuIiYnBs2fPcO7cOZs+sWXLlpAkCWfOnHFEuA7RrVs37N271zpoCpg/M1j6xMOHD6N27do2P6527tw53x1LRI5w7NgxlC5d2jpoCjz/TCo7Rd747OHt7Y2VK1cqLqx15MgRdOzY0ToQVqRIETRp0iRfvW+5urri6NGjNn2cn58f4uLiYDQacf36dURERKBLly7W8s6dO+Pu3bu4ceOGI0J2iJEjR2LDhg3WY8VoNCIuLs7aBx45cgTNmjVDwYIFAQBarRYdOnTIV8cS5T15YsZpWFgYPD09rS9WAChQoAAKFSqEsLAwVKtWzYHROZYon094eHiaGZN+fn4ICwvL7rByFVdXV5vrK1asgL+/P2rUqIHHjx8jNjY2TTuVLl06X7ZTWFgYli5dilu3bkGn01lP3wwLC0tzrJUuXRqJiYl4+PAhfH19HREyUb4UFhYGX19fm7MtLO9h4eHhNn1lfqLVaoXvRWFhYWnylPn5+eW7FAep+8SVK1eifv368PX1xaVLlwDApk/UaDQoWbJkvuwTL126hA0bNuDq1asoXry4dRZuWFhYup8b7t69C1mW882sJCJHSO/1Zxn8evToUb7NNayWX1Kp3fLbe3vqvOYrV65Ex44dodVqERYWBm9vb5u2LFSoELy8vBAWFoZKlSrldLgO4+rqihMnTmDHjh24dOkS6tWrZ/3RNSwsDJUrV7a5f348lihvyRMDp4mJiWk+6APmF3R+y032IhISEtK0m5ubW75usz///BOLFi3Cr7/+Cp1OZ22L1O2UX48tjUYDNzc36HQ6hIeH48GDB/Dz80v3NWi5npCQ4IhQifItpfd2SxmlT6nd8nObLV68GDt37rTmfbW0BdvJTKPRwNXVFVqtFnfu3MGjR49QuHDhdPtENzc3mEwm6PV66+uRiLIe+8CMYR9oy3Ja/rVr17Bq1SoA6bcRkH/bSavVWtvjzp07ePr0Kdzd3XksUZ6UJwZOPTw8EB8fn+b2+Pj4fJNvKyM8PDzSvIHFxcXl2zb7+eefsWLFCvz666+oXbs2gOe/OqZup/x6bPn5+WHatGkAzEnj33zzTezbty/d16Dlen5sp6wiyTIkJzitzBlizE+U3tsBc25iSp+np2e672P58T3MaDTi888/x8GDB/H777+jbNmyAGz7RBcXF+v94+Li8uWxVbVqVbzzzjsAgE8//RTvvfceVq9eDQ8PjzSLYMTFxUGn06X7pZvsIMF58qY5S5x5FD+TZozSZ4f8+N6emJiId999F7dv38bvv/9uTVWW3ucEIP9+VmjYsCEaNmwIAJg4cSK++OILfP/99+m+BvPrsZRlJMl5+hZnifMF5Ykcp+XKlUNiYiIiIiKstz169AgxMTEoX768AyPL3QICAhAaGmpzW2hoaL5ss1mzZmHTpk1YtWqVddAUAAoXLozChQvn+3aKjIzExYsXbW5r2LAh7t69i9jYWJQrVw63b9+2KQ8NDYW3t3e+PSWKyFECAgJw79496PV6622hoaHQarWKuc0I6b6PhYSE5Kv3esA8y2b69On4999/sWbNGgQEBFjL/P39odVqbfpEvV6Pu3fv5qt2CgsLw7Vr12xua9iwIa5fvw5A+fNVQEAAT9MnymYBAQHpvpcXLlzYJlc/2eL3QjO9Xo8JEyYgJiYGv//+O4oXL24tK1euHOLi4vDw4UPrbREREYiPj89X7XTr1q00x0rqPjC974X5qY0o78kTA6f+/v6oUqWKzWrBa9asQeXKlW0+8JOttm3b4uTJk7hz5w4A8wySv/76Cx06dHBwZDlr+/bt+Ouvv7B8+fJ0BxXatm2LP/74w3r94MGDiIqKUlx0Ky86efIkhg4dikePHtncVrJkSXh5eaFZs2aIjo7GwYMHreVr165Fhw4d+CWRKIfVrVsXBQoUwJYtW6y3rVmzBs2aNUOBAgUcGFnu1rZtW+zbt8+6gNbjx4/x999/o2PHjg6OLGctXboUly5dwpIlS9IMMnh5eaFx48bYuHGj9bY///wThQoVQt26dXM6VIfZuXMnxo4da53JDQAnTpxAxYoVAQCtW7fG5cuXcfnyZQCAwWDAxo0b893nKyJHaN68OZ48eWJdxFWWZaxbt46vPxVt27bFtm3brKnIQkNDcfz4cbRv397BkeWs7777Dnq9Hj/99FOaWaQBAQGoUKGCTR+4du1aVKtWLV/9ML1y5UpMnTrVZgHgEydOoEKFCgDMx9LRo0dx7949AEBsbCy2bt3K1yA5NUnOI8sLnjhxAq+//jpatGgBSZJw+PBhLFiwAC+99JKjQ3OYu3fvYv78+QCAvXv3okSJEqhVqxaKFy+OyZMnAwA++OAD7Nu3Dy1btkRwcDC8vLywdOnSfJV/q127dihcuHCalZZ79eqFRo0a4d69exg4cCD8/f1RunRp7NmzB2+//TYGDRrkoIhznsFgwLhx43DlyhW0bt0ajx49wrFjx/DNN99YO8EVK1Zg9uzZaNeuHe7du4c7d+5g9erVKFmypIOjdz5xcXG4dOkS5n20D3dvP3V0OKr8yhbCxI/aonr16vnyVKXcaNu2bXjvvffQrl0760roK1asyFcLF6R2+fJla67OrVu3okaNGihfvjwqVqyI4cOHw2QyYcKECbhx4wYaN26MEydOoHLlypg3b16++QEoISEBgYGB1rZJafDgwahatSouX76MYcOGoW7duvD29sb+/fvx9ddf56svRLGxsRg2bBiio6PRtGlT3LlzB+fPn8f8+fOtpy3Onj0ba9asQdu2bXH16lXo9XqsXLlSdYEWsmXpD71LVoDO1UN9g1zAoI9H9P2b7BMdaNmyZfjhhx/Qvn17hIWF4d69e1i1alW+Xqw0KCgI27dvB2Ae7GvTpg1KlCiBhg0b4uWXX0Z8fDyGDh2KxMRE1K5dGwcPHkSXLl0wY8YMB0eecyIiItCuXTsEBgam+f4yYcIElCxZEkFBQZgwYQJatmwJk8mEoKAgLFy40Prenx88ePAA//nPf+Dh4YF69erh6tWrCAsLw7Jly6yDp++++y6OHTuGFi1a4OzZs/Dx8cGiRYuYruYFWfrAasUATxf1++cGcUnA5YfIc31gnhk4BYD79+9bZ7y1atUq3w/YPHz4EH/++Wea2wsXLoy+fftarx8/fhxXr15FqVKl0Lp1a5u8ZfnBkiVLbH4xswgMDES1atUAANHR0di7dy9iYmLQqFEjVK1aNafDzBVOnjyJ69evW2cdpX6NXb58GadPn0aBAgXQvn17zm7LIA6cUlYICQnB0aNH4ebmhrZt2+b7UxRDQ0Oxe/fuNLf7+fmha9euAACTyYRDhw4hNDQU5cqVQ6tWrfLNoClgHjhdsWJFumWdOnWyzqh5/Pgx9u3bB71ej8DAQGsO1PzEZDLh6NGjCA0NRZEiRdC0adM0r7GzZ8/i/PnzKFq0KNq1a5evfpTOKhw4pYyyfCb19vZGu3bt8v1n0gsXLuDYsWNpbq9atSpatGgBwHya+v79+xEREYFq1arluwlIjx8/tjnLMKVXXnnF+h5/7949HDx4EJIkoXXr1vlyQF6v1+Pw4cO4d+8eihUrhsDAwDSvsaCgIFy/fh1+fn5o3bo1dLo8sbxOjuLAae6RpwZOiYjyAg6cEhERceCUiIjyLw6c5h55IscpERERERERERERUVbifGkiolxLBpzipABniJGIiJyVBMBZsmY4SZhEROQ0JOfpBCEjL3435IxTIiIiIiIiIiIiolQ4cEpERERERERERESUCk/VJyLKrWQ4x6n6ThAiERE5LwnOcwq8s8RJRETOwpl6QSAvfjnkjFMiIiIiIiIiIiKiVDhwSkRERERERERERJQKB06JksXFxaF+/fo4deqUo0MhIiJyqBs3bqB+/fq4e/euo0MhIiLKUUePHkX9+vVhNBodHQoR5QLMcUqUTJZlxMXFsYOk3MOUfMntnCFGInohRqMRcXFxMJn4AqdcwJnSuzlLnESkyGAwIC4uDrIzrDVAeZ8kmS/OwEnCfFGccUpERERERERERESUCgdOiYiIiIiIiIiIiFLhqfpERLmVLENyhlOEnCFGIiJyWlLyP2fgLHESEZGzcKZ8NXkTZ5wSERERERERERERpcIZp0RERERERNksKioKW7duRWRkJKpUqYIuXbpAq9UK779t2zY8ePAAZcuWRbdu3eDm5paDERMRERFnnBIREREREWWjhw8fonfv3tizZw8AYO7cuZgyZYri/UNCQtClSxccOnQIALB06VK8+uqr0Ov1ORIvERERmXHGKRFRriU7Sf5QZ4iRiIiclgRIzpLeTSHO3377DeXKlcOiRYsgSRKGDRuGDh064MSJE2jcuHGa+y9YsAD16tXDzz//DAAYNWoUAgMDcfToUbRp0yYbHwAREeUqEpynE5Ty5vdCDpwSERERERFloyNHjuDVV1+FlPzlt2jRonjppZdw+PDhdAdOP/30UxgMBut1b29veHt7Izo6OsdiJiIiIg6cEll5eXnhzJkzcHd3d3QoREREDlWpUiWcOXMGnp6ejg6FKE8ICwuDn5+fzW1+fn4ICwtL9/46nQ463fOvajt37kRCQgICAwOzNU4iAgIDA3HmzBmb1yAR5V98JyBKwcvLy9EhED1nks2X3M4ZYiSiF6LRaNgnUq4hQfEM+FxHKc6EhAS4urra3Obm5oaEhATVOo8dO4YPPvgAX331FXx8fLIgSiIS0Wq17AOJyIqLQxEREREREWUjT09PxMfH29wWHx+vOqt706ZNmDJlCr788kt06NAhO0MkIiKidHDGKRERERERUTYqV64cbt++bXNbSEgImjRporjNunXrMHv2bPz666+oU6dOdodIRERE6eCMUyIiIiIiomzUtm1bbNmyBXq9HgBw48YNnDlzBu3bt0/3/sHBwfjyyy+xePFiDpoSERE5EGecEhHlVrJsvuR2zhAjERE5LUkyX5yBUpzDhg3Drl270L9/f9SoUQMHDhzAsGHDUK1aNQDA7du3sWrVKkyZMgUeHh747rvvUKxYMaxcudKmnsDAQHTu3Dm7HwYREeUWeaETdHIcOCUiIiIiIspGBQoUwNq1a7F37148ePAA/fr1Q4MGDazlLi4uKFasGKTkL509e/bEkydP0tRTpEiRHIuZiIiIOHBKRERERESU7VxdXdGlS5d0y0qVKoXXXnvNer1Pnz45FRYREREJcOCUiCi3kuEcp8E7QYhEROTMpOSLM3CWOImIyDmwD3Q0Lg5FRERERERERERElAoHTomIiIiIiIiIiIhS4an6+dSxY8ewZ88ejB8/Hj4+Po4OJ0NiY2OxYsUKREdH4+233xbe98aNG1i9ejXatWuHZs2avdB+LG2VkouLC/z8/NC4cWNUqVLlhWMnso/sHKfq81x9ymK3bt3CypUr0bdvX+uK087GaDRi/fr1uH79OiZOnIjChQsr3jc6Ohpz585F9erV0bt3b7v3cejQIRw8eBDjxo1D0aJFbcosfdfIkSPh5+dnU3bmzBls374dQ4cORdmyZV/kYZEdHj9+jJ9//hmvvPIKatSo4ehw8gQuKExktm7dOty9exdvvPGGo0PJsLt372LVqlXw9/dH//79hfc9cOAADh8+jGHDhsHf3z9D+zOZTDhx4gTOnTuHqKgoeHt7o1y5cmjVqhUKFCiQoToJ+Pbbb1GvXj20b9/e0aHkfewEHY4zTvOpCxcuYNmyZYiOjnZ0KC9MlmX88ccf6Ny5M77//nusWrVKeH+j0Yh3330Xy5YtQ3Bw8Avvz9JWV65cwe3bt3H79m1cvHgRCxYswMsvv4yZM2dm9KEQEVE67t27h2XLliEkJMTRoWTI0aNH0bt3b3z88cdYtmwZYmJihPf//PPPsWTJEhw4cOCF9iNJEpYtW4ZDhw6lKVu5ciWWLVuW5oc/AFi/fj1WrVqVZrBVyZ49e144NkdxRKyp95mQkID9+/cjMjIyR+Mgorxv3759WLt2raPDyJD4+HjMmTMHXbt2xaJFi9Ltn1KKiIjAtGnTsGzZMkRERGRonxcvXkT37t0xfPhwrF+/HufOncO2bdswbdo0tGzZEhs3bsxQvY508eJFrF692uH7PHz4MG7cuJGjcRA5CgdOyelcuHABn3/+OcaOHYtBgwap3v+XX35BTEwMPD09M7XfTz/9FAsWLMCCBQuwZMkS7N+/H4MHD8ayZcuc5gslERFlL6PRiLFjx6JFixb43//+p3r/AwcOYMeOHahQocIL76tx48bw8PDA4cOHbW43mUw4fvw4ypQpgyNHjqTZ7ujRo2jUqBG8vLzs2s/KlStx5syZF47PERwRa+p9+vn54e+//0abNm1yNA4iotxs7dq12Lp1K3744Qe7ziZ5//33M3VWxO3btzFs2DAAwKZNm7Br1y78/vvv2Lx5Mw4ePIgmTZrgf//7X7r9ZG62c+dO/P333w7f5x9//IGxY8fmaBxEjsJT9UkoLCwM+/fvR0REBLy8vFCnTh00b94cgHnGyt27dzFlyhSbbRITE/Htt98iMDAQrVu3BgDo9Xrs2LED165dg4uLCxo1amStBwDWrFkDAGjTpg3WrVuHevXqoUWLFunGVLp0aezcuRM+Pj6qsz2vXbuGefPmYeHChZg8ebJN2fXr17FmzZoMnwqq1Woxbtw4LF++HMePH0fr1q2Fj0OtDQDg33//xYkTJxAVFYVixYqhXbt2KFeu3AvdZ+XKlTAYDNYPCoB59su3336Ljh07onHjxpmOk4goN3jy5An27t2LO3fuQKPRoHLlyujUqRO0Wi0OHTqEQ4cO4Y033kgzQPjTTz+hWLFi1lMEZVnGwYMH8c8//8BkMqFatWro1KkTdDrzx6T9+/fj3LlzeO2117BmzRoUKlQIffv2TTcmjUaDv/76CxUqVFCdyfLs2TO8//77mDJlCvbu3WtTFh0djR9++AEtWrSw9qWpubq6okmTJggKCoIsy5CST48KDg5GVFQUxo8fjx9++AFJSUlwcXEBYE5dc+/ePWsfYTQaERQUhODgYMTGxqJkyZLo1KkTSpQoAYPBgK+++gr//PMPnj17hri4OIwdOxbFixcHYE6psHfvXjx69Ai+vr7o0qULfH19ATw/XX3QoEEICQnBP//8g/Hjxyv+iBkfH4+dO3ciNDQUAFCpUiV06NABbm5u1vtkJFYAWLhwYZq+/uDBgzh06BDee+89YawZ2adWq033VP2rV69i//79iIqKQtGiRdGmTRtUrFgRgPlY/umnnzBo0CDodDps374dcXFxqF27Njp06CA8joiIUgsODsbx48fx5MkTFC1aFC1atLCmF5s9ezbKlSuHPn362GxjSW02cOBA63vTo0ePsGPHDoSHh6NQoUJo06YNqlatat1m1qxZaN68Oby9vbF792707t0b5cuXTzemdu3aYdCgQXB1dcWcOXOE8W/YsAH//vsvZs+ejZEjR9qU7du3D0ePHsUbb7whPNX+u+++g16vxy+//IIyZcrYlBUrVgxz5szBwYMHUbt2bbvbDjD/OLl//35cvnwZCQkJKFOmDDp27IgiRYoIH5O924n2v3DhQmzfvh0GgwEzZ85Ez549UadOHXz11Vdo3Lgx2rZta63H8l131KhRKFWqlPC5ysg+0ztVPyIiArt27cK9e/dQoEABNG7cGI0aNbJ5Tpo2bYrq1atj8+bNePDgAcqVK4devXrB1dVV2H5EjsQZp6To4MGD6Nq1K/78809ERUXh7NmzGDVqFGbMmAHA/OVw3rx5uHDhgs12Bw4cwNKlS6353CIjI9GrVy/MnDkTDx48wM2bNzF27Fi89dZbkJPzN+7btw/btm3D+PHj8ccff+Dy5cuKcRUpUsSuvKwGgwHvvvsu+vTpk25e0zt37mT6VFCtVgsAqo/Dnjb48ccfMXjwYPz777+IjY3F7t270a1bN5tf9+y5z65du7B9+3abOPV6PZYtW4aLFy9mOk7KQSbZeS5EOezWrVvo2rUrFi5ciMjISISEhODdd9/F4MGDkZSUhGLFimHp0qVp3g/v3LmDOXPmwGQyATC/P44aNQoTJ05ESEgIIiMj8dFHH2HAgAHWU+z//fdfrFixAp9//jkWL14snNEoSZLds0dnzpwJPz8/DB8+PE1ZbGwsli1bpjp7slWrVnj48KFNv3n48GGULVsWXbt2RWxsLP79919r2dGjRwHAOhg7adIkTJ48GVeuXMGTJ0+wfPlydOrUCefOnQMAuLm5IS4uDlqtFm5ubtbB2bVr16J79+7YsWMHYmJirCl0goKCbOJft24dpk6dij179iAhISHdx/DkyRP07NkTP/74Ix4+fIj79+/j888/R//+/REbG2u9X0ZiffLkSbp9/b///otly5apxpqRfUZHR2PZsmW4deuWdX+LFy9Gr169sH//fsTExGDnzp3o0aOH9YfMhIQELFu2DBs3bsSoUaNw9epVnD9/HhMnTsQ333wjPAbyA8nJLkSONG/ePLz66qs4efIknj17hu3bt+Pll1/Ghg0bAAAPHz7El19+Cb1eb7Pd2rVrsXHjRmte7FOnTqFTp05YunQpYmJiEBQUhJdfftn63mnZZufOnRg1ahR27tyJ+/fvK8bl7+9v1+DY/fv38cUXX+C9995DsWLF0pSfPn0ay5YtQ3x8vGIder0ee/fuRfv27dMMmlq4ubmhY8eOKFiwoPU2tbYzmUwYPXo0pk+fjpCQEERFRWHx4sXo1q2bzXt+avZup7Z/V1dXxMfHQ5IkuLm5QaMxD+f8/vvvOH36tM0+Ld91Hz58CED5ucroPletWoVTp05Z93fo0CF06tQJq1evRkxMDM6ePYvBgwfjgw8+sN5n/fr12LZtGwYNGoRTp04hNDQUH330EcaMGaPYdgQ4vldjL8gZp6SoQIEC+OCDD/Dqq69ab5s9ezbmz5+PcePGoVu3bvj888+xceNG1KxZ03qfrVu3olKlSqhbty4A4Msvv0RERAT++usva1Lvffv2Ydy4cejUqRO6dOkCFxcXnDhxAhMmTMDo0aOzJP6FCxciKioK//3vf9Mtr1SpEqZPn47q1atneB+WDuWll14CAMXHYU8bLFu2DCNHjrRZ6Oq7777DP//8g44dOwKAXfexR2biJCLKDUwmE9588010794d3t7eAIDt27fjzTffxIEDB9ChQwfUrl0bGzduRL9+/azbbd26Fe7u7ujevTsAYMmSJTh69Ch+/fVXtGzZEgAQGhqKHj164JdffsHUqVOh0+msA3iHDh2yDh5mxr59+7Bjxw5s2rTJ+iUkJW9vb0yfPt3alypp1aoVAPNgqaU/O3r0KJo1awZfX18EBATgyJEj1hkfR48eRenSpVGxYkXo9Xp07NgR48aNs+4nLi4O7du3x/z58/HTTz9h2rRpWLx4MZo0aYKpU6cCMOeg/eSTT9C1a1fMmjULkiRZUxT873//w549e6yzdffs2YOdO3eiZMmSio9h//79uHPnDo4cOWL9kvzgwQO89dZbuHbtGurVq5fhWB8/fqz6XCjFmtF9phzsBcxfXmfNmoVBgwbhww8/BGD+wXXq1KmYOXMmOnfubD2mtm7dio0bN1pnIL311ltYt26d4mcZIqLUKleujJ9//tlm9uHQoUMxZ84c9O3bFwMGDMD69eutEzAA83vSjh070L17d3h4eMBoNOKdd95BmTJlsHr1anh4eAAwzzD9+uuv0aFDB/j5+UGn02Hr1q1YsGABGjZsmCXxz5gxAy+99BJ69eqFq1evpilv27YtihUrJpxteufOHSQmJqJWrVovtG+1trtx4waOHDmCBQsWWNOx6PV6TJs2Df/++6/ibFt7t1Pb/4gRI7Bt2zZ4e3tj2rRpL/TYlJ6rrNinwWDA//73P9SsWRNLliyxDpD/8ssvmDVrFnr16oVGjRpBkiT8+eefWLt2rfUzy+LFi/HVV1/hzp07GV4AjCi7ccYpKWrQoIHNoCkA6xev27dvw93dHb169cKWLVusv1jGxcXhwIED1lMYDQYD9uzZg86dO9u8EbZt2xb+/v7YuXMnAFi/dA0dOjRLYr98+TJ+/vlnzJw5U7FT9ff3x4gRI9KcCq9k/vz5mDlzJmbOnImPPvoIgwcPxjfffIOePXtaO5r0Hoe9bWDJU5cyyfZbb72Fd955x3rdnvvYIzNxEhHlBhUrVsTAgQOtg6bA8x+xLKd7DxgwAKdPn7aZbbh161Z06tTJut2uXbtQrVo166ApAJQrVw5t2rSx6aOSkpIwdOjQLBk0ffr0Kd5//328+eabil+yvLy8MGLECNSvX19Yl7+/P8qXL2/N0WaZYdq0aVMAQPPmza2zTA0GA06cOGF9rK6urujTp4/N4Kynpydq1qyJ27dvK+5z7969SEpKwujRo63todVqMXjwYNy9exfnzp2z3t6mTRvhoCkA6xfydevWIS4uDgBQvHhxLF++HPXq1ctUrPZQijWr9nngwAEYjUYMGTLEZp/9+/dHYmIiTp48ab29d+/eNqdt1qhRA0+fPrW2CxGRmk6dOtkMggFAw4YNERERgYSEBNSpUwfVqlWzTgABzLNL79+/b/0Od+HCBYSHh2PIkCHW92gAGDZsGJKSkqzpZSRJQpUqVbJs0HTdunUIDg7GJ598onifhg0bYsSIETZxpWY5ayB1epi4uDjr9znLZe7cudZytbZzd3eHJEnYsmULHj16BMDcV/zwww/o3bu3Yjz2bqe2/8xQeq6yYp/nz59HZGQk+vfvbzOreMCAAQBgs4il5VR9C0tKm3v37r3YAyLKQZxxSkLXrl3DiRMn8ODBA+j1eusbmsFgAAD0798fK1assP5iuWfPHhgMBrz88ssAzHlxEhIScPPmzTT5SI1Go82X2RIlStjkMssog8GA6dOnK56in1H379+3dtA6nQ61atXCuHHjbL5sA2kfh71tMG/ePLz99tvo1q0bSpcujebNm6N37942eWHsuY+9Mhon5SDZZL7kds4QI+VJ9+/fx+HDh3Hv3j3Ex8cjMTERgPk9CwC6deuGL774Ahs3bsRbb72FGzdu4OrVq9aUMwAQHh4OLy+vNO974eHhCA0NtUlTklUzIT777DP4+/une4p+RrRu3RorV65EQkICTpw4AYPBYDNwumbNGjx79gzXr19HTEyMTc5UvV6Pw4cP4/r164iOjobBYMCdO3eEA8Th4eEAgNWrV9t8QXr69CkAICQkxDoAac/CHp07d8a4cePw008/Yd68eahbty5at26Nfv362aTmyUisLyK9WLNin5bPTpbTXy1KlSoFwHwcW3LsWW6zsMyGtXzuys+y6GkmyvNkWcbJkydx4cIFREVFQa/XW1O2WPrH/v3747PPPsP9+/dRsmRJbN26FZUrV7b+UBQWFgbAnLot9axPnU5n870gMws4pXTv3j18+eWX+PDDD625tDOqaNGiANKedWA0GnH+/Hnr9Tt37sDV1RWTJk0CoN52/v7++PLLL/H5559jy5YtqFq1Klq2bIl+/fohICBAMR57t7PnucuM9J6rrNinpZ8rXbq0ze0FCxaEl5eXTQoH9nMZxE7QoThwSop++uknzJkzB40bN0bNmjXh4eGR5le7atWqoU6dOti0aRO6deuGbdu2oU2bNtbOyvJGaJnhmFLbtm1tvhBlxaApYF7h7+LFi6hZs6bNF+GEhATs27cPDx48wJQpU2xmKdnjo48+smt2aurHYW8b1K5dGzt37kRwcDCOHj2Kffv2YfDgwZg4caJ1AS577pOe9Dq9jMZJRJQb/P3335g6dSrKly+PJk2aoECBAtYFkCy8vLzQo0cP/Pnnn5g6dSq2bt0Kf39/NG7c2HofrVab7vtevXr1UK9ePZsP8lnRT50/fx5//fUXOnTogC+++MJ6e2hoKCIjIzFz5kwMGDAAlSpVsrvOVq1aYcmSJThz5gyCgoJQvXp163u2ZQD1xIkTuHLlClxcXKy3PXnyBIMGDUJkZCQ6d+6MYsWKwdPTEzqdTvhlydJfyLJsc78CBQpg8ODBNvnk7GkzSZIwdepUjB49GkeOHEFQUBAWLVqExYsXY/Xq1QgICMhwrErs6Rezap+WVAypt7FcT5mqIasGgYkofzIajZg4cSIOHjyIDh06wN/fHx4eHnB3d7e5X69evfDNN9/gzz//xOjRo7Fr1y6bPJNK7/OAeRZhnTp1rNezalGfH374AZIkITg4GMHBwQCAqKgoAMDy5ctx7Ngx6wCnmhIlSqB48eI4duwYJk6caL3d29sbq1atsl7/5JNPsH//fgD2t13v3r3RrVs3nDhxAseOHcOOHTuwdOlS/PDDD2lmbr7Idvbu317p9VOpn6us2qdSPweYUyull5KIyJlw4JTSpdfr8dNPP2HgwIH4+OOPrbfv2LED69evt7lv//798dFHHyE0NBSHDh3Cjz/+aC3z8fFBwYIFUa1aNZvE0NmpVKlS6NevH2RZTnNqW1JSEuLi4qyLguQEe9tAr9fD1dUVderUQZ06dTBu3Dh8+OGHWLx4MSZNmgSNRmPXfVxdXREdHW1T9507d7IsTiKi3OCHH35AnTp1sHz5cutCfaGhoZg/f77N/QYMGIA1a9bg5MmT2Lp1K/r06WMzOBUQEICkpKQce9/z8PCw5lxN2UeZTCYYjUbExcW98KyLl156CZ6enjhz5gxOnTqF5s2bW8u8vb1Rq1YtnDx5EleuXLHeFwA2bdqEW7duYfv27TYLWh09ehRPnjxR3J/lR8QRI0YoLoQlWiAkPXq9Ht7e3ujSpQu6dOmCN954Ax07dsT69esxbdq0DMdq+ZKYehEUe/rFjO4zNUt73bp1y2b1ZktKCXtTBhERqTlz5gz27duHWbNmoWfPntbbZ86caU3pApj7hq5du2LLli2oVasWnj17Zj1jEHj+vtSpUyf06tUrR2KvVasWNBqNTd9oOVU8MTHxhU5V12g0eOWVV7Bw4UIcOXIEgYGB6d4v5UCfvW1n+T7WokULtGjRAm+88QYGDBiApUuXCgdO1bazd//pcXV1zVA/l5l9ppSyn7P8OAsAERERiI+PZz9HTo9D/5QuvV6PpKQkmxmm9+/fx++//w4A1tMhAaB79+5wd3fHu+++i8KFC1sXqgDMMye6deuGzZs32+TljIuLw6JFi3DlypUsj71FixZpctfMnDkT7u7u6NSpE2bOnIlChQrh+vXrmDlzps1KxNnBnja4cuUK6tevb80XZCHLMry8vKDRaOy6DwCUKVMGN2/etJ6aotfr8euvv2ZJnEREuUV8fDzc3Nysg6axsbH48ccfodPpbL5c1axZEzVr1sR3332H27dvo0+fPjb19OjRA2fPnrXOOAHMg5irVq2yrhCflSpWrJhuH1W+fHnUrl0bM2fORLVq1RAdHY2ZM2fiwIEDqnW6urqiSZMmOHLkCK5cuZLmC2KzZs1w6tQp/Pvvvzan6Vu+nHp5eVlvW7duHUJDQ236eZ1Oh5iYGOv1du3awdPTE/PmzbMZ5D116hRWrlyZ5subGstClKkXVTKZTNbVjjMaa8mSJaHT6WxW/j1//rxd7ZrRfaZmaa9ffvnF+iU9ISEBS5YsQfHixW1mQBMRZYblfStl/s/Tp09j3759AGy/w/Xv3x9Xr17Fjz/+iDZt2ticXVa1alVUqVIFS5YswbNnz6y3h4WF4ddff7Xm6cxKgwcPTtM3Tp48GQAwevRo68JE+/btw8yZM4XvuwAwYcIEVKxYEZMmTcKGDRts+qb79+9jzpw52LBhA6pVqwbAvrZbu3YtAgMDrakMLFL2V+mxZzt7n7v0+pwyZcrgzJkz1slBkZGRWLdunbB9MrvPlKpVq4ZKlSph5cqVNsfLwoULodPp0LlzZ9VYiHIzzjjN50aMGGE9FSOlv/76C7169cKSJUsQHBwMSZJw//59fP/99xg+fDh++OEHxMXF4eWXX4anpyd69OiB1atXY+zYsdYvsRZvv/02rl+/jldeeQW1a9eGm5sbzp8/j2LFiqFr164vHPPixYuxceNGAOZVd2NjY9GjRw8A5k7+22+/taueO3fuYNmyZWjYsKG1w8wuam3g5+eHAQMGYMKECdZTLMPDw3H//n3rqZxVq1ZVvQ8ADBw4EJs2bUKPHj1QsWJFhISEYPLkyWkGXDMSJ+UwGUCK/Iq5lhOESM7ps88+S/c9ffbs2Rg0aBC+/vpr9OnTB0WKFMG1a9fw2WefITQ0FGvXroW7uzvGjh0LwPzl8MMPP0TLli3TLFTUv39//Pvvvxg/fjxq1qyJIkWK4OrVq0hKSrI5g8Je+/bts8Zs+fLw2muvwcXFBa6urtb+S01sbCyWLVsGT09Pm8FOJa1atcLHH38MNze3NAs/NG/e3DoTN2Ve7p49e+K3335Dnz59UKNGDdy5cwcNGjTApEmT8PHHH2PatGn473//i7p162LNmjU4f/483nzzTTRr1gzffPMN3nnnHXTo0AGVKlXCkydPcOnSJYwePfqFT9scPXo0RowYgTZt2qB69eqQZRkXLlxAhQoVMHDgwEzHOnjwYCxduhTnz5+3fvkbNmwY5s2bZ5PDNrWM7jN1LlMfHx988cUX+L//+z907twZAQEBuHLlCvR6PebOnZtlp7nmZRIACc6RxsA5oiRn9uTJE3Ts2DHN7RUrVsT333+PihUr4r///S8aNGiAqKgouLm54bPPPsPw4cPx3//+F5MmTUL9+vVRv359VKlSBf/88w8WLFiQpr5vv/0Wr7/+Ojp27IgaNWpAr9fj3LlzCAwMxLBhw1447rfffts6EeP27dsICQmxfofr06cPRo0aZVc9p0+fxrJlyzB27FjFRYAB82DgypUr8dFHH+H999/HJ598guLFiyMuLg6PHj2Cj48PpkyZYt1vkyZNVNvutddeQ+XKldG1a1fUqlUL7u7uuHbtGiRJwqxZsxRj6datGzZt2iTczp79T5o0CXXr1sXixYvRt29f9O7dG0OHDsXIkSMxffp0dOnSBcWLF0dYWBjGjBmDTz/9VNjPZWafqc2aNQtjx45Fly5dUL16dYSHhyM8PBwffvih4kKYZC8JztO7OEucL0aSRa8kyrMuXLhgs4praoMHD4aLiwuCgoIQEhICHx8ftGjRAl5eXrh69SpOnz6NmjVrWvPb7NixA2+88QZ27Nih+MZ45swZXL16FRqNBgEBAWjQoIF10Hb//v14+vSpzSkiSk6dOmWT1DulEiVKoFu3bumWrVy5ErVr17aeJnfnzh3s2bMHbdu2FZ4+YGmrvn37quZFVXscojYAzKc3BAcHIyYmxjoLpVChQjZ12HOfyMhIHD9+HPHx8WjQoAEqVaqEFStWoE6dOqhdu3am46TsFRcXh0uXLmHetG24d+ux+gYOVqq8DybO6obq1aunyYNMlBH37t2zrmifnq5du8LX1xcXLlzAhQsX4ObmhsDAQBQrVgwRERHYv38/SpUqZT0DIjQ0FJ06dcL333+v+CPQtWvXcPbsWRgMBvj5+aFx48bWGRjnzp3DmTNnMHz4cNUclDdu3LBZPTYlnU5ns7J6Sjt27ICHh4d1kDQ2Nhbr1q1D3bp1Ub9+feE+AfMCf5s3b0bRokVtTrcDzGcerFy5Ei4uLhg8eLBN2ePHj3HkyBHExcWhevXqqFOnDoxGI3bu3Im4uDh06dIFJpMJu3btgoeHB5o1a2adlfT06VMcPXoUjx8/RsGCBdGgQQPrwhCW+AMDA1G5cmXV+JOSknDixAmEh4dDq9UiICAgzQBwZmI9deoUbty4gUKFCqFVq1YIDw/HkSNHMHToUCQkJCjGmpF9uri4YMOGDWjdurXNZ6LHjx8jKCgIUVFRKFGiBJo1a2b90q/UXhcvXsSJEyfwn//8J18OsFr6w+JlKsHV3Tn6F31CHB6EXWefSNli7969uH37drplPj4+6NWrFxITE3Hw4EE8fPgQZcuWRdOmTaHVahEUFITbt28jMDDQmot65syZ2LFjB/bv359m8gtg7j+CgoIQFhYGDw8P1KhRw2bCydq1a+Hv72/Xgrzbtm1DZGRkumW1atVKd7HbJ0+e4M8//7T2+4B54DQ4OBgDBgywmSkpEhkZiXPnzuHBgwfw8PBAQEAAateuneYx29t2Z8+exY0bN6yfGZo0aWLXe7Tadvbs39fXF7t27YJer0fDhg2tCz5du3YN//77L3Q6HVq0aAGNRoO//voL3bp1Q4kSJRSfq4zuc+XKlahSpYrN8xYfH48jR44gIiICBQsWRNOmTW0W+kovhoiICGzfvh2dO3dOs3BUfmfpA6uV9ICnW9rXZ24Ul2jE5fvxea4P5MApZYmpU6ciIiICK1eudHQoRE6PA6dEWWvhwoVYtGgRDh06lC8Hn4icFQdOibKP0WhEhw4d0KNHD7z99tuODoeIUuHAae7BKWSUaUePHsWOHTswb948R4dClLfIMmBygt+2+Psb5WJ37tzBr7/+iuHDh3PQlMhZ8SxFoiw3f/58PHr0SPFMCCLKJSTJfHEGzhLnC+LAKWXYuXPn8OWXX+LcuXPo3bs32rVr5+iQiIiIAABRUVGYPHkyLly4gMqVK2P06NGODomIiMjh1q9fj5UrV+LixYv49NNPrafAExFR+jhwShlWsmRJ9OzZE+PGjbNZbIKIiMjRPDw80KNHDwwaNAgdOnTgbFMiIiIANWvWRL9+/VC3bl3UrFnT0eEQEeV6HDilDCtRogQGDRrk6DCIiIjScHNzw4ABAxwdBhERUa5SvXp1VK9e3dFhEBE5DQ6cEhHlVrLsHPlDnSFGIiJyWkxxSkRE+Rd7QUfTODoAIiIiIiIiIiIiotzG6WecGgwGPH36FG5ubtBoOA5MRLmDyWRCYmIiChUqBJ3O6d9qyUmwTySi3Ih9IuUE9oFElBuxD3R+Tv+sPX36FCEhIY4Og4goXQEBAShatGjGNuap+vSC2CcSUW6W0T5RkswXZ+AsceZF7AOJKDfL8PdCdoIO5/QDp25ubgCA2XfOIVwfB3eNBp+Vb4wZt04gwWQy30mj/KVeksRf+CXBj5UaySTYUFgtNIKYNKKYMnMcpqjWTdLi/ZKB+PT+ESTKRhhN4opNRq1ytYJwZUETmUPK+AMStaHo9SqJnjcAWp253A06fFCqOT65dxSJMJjLtMrbCp83FSZZOWBZUKZarhKSwah8gBuTlMvkFNu5aTSYWaEx3rt5AokmlSccEB/DKm2o0QpeNxrlfVue04wQta/o2Pdz8cLkEg2s71FEOcFyvPmVCYCbuztMJiNCb15DuQqVodEov4/nd2wndWwj+7Cd0peYkIC7YSHsEylbWY6vgFJF4eHmAqNJxtXbEahS1hdaTd78Mp8V2E7q2Eb2YTulLz4xCSH3HrEPdGJOP3BqOQ0jXB+HWwnR8Ej+kBqSEIN4kzH5ToIBNkGZWrlooEZtoF04cJqJekVSDvJ4SOan/nbSM8TLBhgFA2gAYDSIBk4FA0tqA6cqg4IiokE00YC4JGhfANAlD/6lbiMA0OmMittpMzFwasymgVO1iYAGwfNq0CuXmQzPj5d0X3MCwmNYbeBUJ3jdaJX3rbNnQFdBZtoXAE8VoxxlOd7c3N3h4ekJo9H8unD38IRWy0EcJWwndWwj+7CdxNgnUnayHF8ebi7w9HCD0Wj+/Ofp7gqtlseeEraTOraRfdhOYuwDnRefOSIiIiIiIiIiIqJUnH7GqZVsmw4w5d/CSWxqM8aEsylF5/GLZ7hlV0rAjNabmZmfjkpvKNpvdqXWELZTJmacisLNzuYVpqoQpjtI+3fK1CvC9A2ZeN6Ex1omZu2K2kH8WDK+T7swxykRERGk5H/OwFniJCIiZ8K+xZE445SIiIiIiIiIiIgoFQ6cEhEREREREREREaWSd07VJyLKa2QZyMTCVjmGp+oTEVF2kuA8Zyk6S5xERERkl7wzcCpLgCnFpyqT5bparkGVQQlh3kpBvaL8pwBkSXm/4ngzPkCRsl4Zz9vGnlyMGd2rat2ZGG+RTYL8ksJUpOKYjEbzc2eUNNbrRtn8tygXpkklQadGsK2ozJSJXJlqOUM1GuX9arXKx6jBmDLJaYpkwpa/VY5/RSrHgyx4uZo0ggdrVMtxKspVKgpIUG9W5DglIiIiIiIiIofhqfpEREREREREREREqThs4PTu3bt4/fXX0aBBA7Rr1w5LliyxKd+9ezd69OiBevXqoWfPnjhw4IBjAiUichRZdp4LZcq1a9cwZMgQ1K9fH507d8amTZtsytetW4dOnTqhXr166NevH/7991/HBEpE5CCSk1zoxZ05cwZ9+/ZF/fr10bNnT+zfv9+m/JdffkHbtm1Rv359DB06FDdu3HBMoEREDiBJklNd8iKHDJwajUa8/vrrKF68OPbt24fvvvsOc+fOxcGDBwEAV69exVtvvYVRo0Zh7969GDZsGKZMmYLbt287IlwiIqJsExMTg1GjRqFhw4Y4dOgQ3nvvPcyYMQNXrlwBAAQFBeHzzz/Hu+++i71796JDhw4YM2YMnj596uDIiYiIMuf+/fsYPXo0XnnlFRw+fBivvfYaJk+ejAcPHgAA/vjjDyxatAhfffUVdu/ejapVq2Ls2LHQ6/UOjpyIiPILhwycHjhwAGFhYZgxYwYKFSqEevXqYceOHWjRogUAYMOGDWjdujX69OkDHx8fvPrqq2jUqBE2bNjgiHCJiIiyzV9//QUXFxe8+eabKFCgAFq1aoW9e/eiSpUqAIDVq1ejb9++aN++PXx8fDBu3DiUKFEC27Ztc3DkREREmbNq1SpUqVIFQ4YMgZeXF3r37o3du3ejePHiAIA1a9Zg9OjRaNy4MYoWLYr/+7//Q0xMDI4cOeLgyImIKL9wyOJQJ0+eRJMmTeDu7m69rVixYta/L1y4gJYtW9psU7t2bQQHBytXapTMF8uCLMbni0NBsACOrDaVWFQsqFftZB3RwkaiRYY0mRjqzsziUBldrkq0mFLKOF64YpVy4SJCaos4JW9rOTZk0/M2siwclR7VWeka5aAyunAUABgzsQiRcHEonXK8phSLP1nq0OhkaEzmv02CU7eFi3oJygDxcWgyKD83smjhKKg8dyrtrxgPF4ciO506dQqtWrWyObWlRIkS1r8vXLiASZMm2WxTu3ZtnD9/PsdiJCIiyg4nT55EmzZtbG7z9fUFAMiyjIsXL+LNN9+0lul0OtSoUQPnz59H27ZtczBSIiLKrxwycBoREYGCBQvi//7v/7B3714UKFAAr7zyCiZOnAiNRoPHjx+jUKFCNtsULlwYT548UazTTaOBh0YLD40WAKz/AxAPcAoHPwFJUC4sU1vNXFIe5NFIGRtgU5NykNJd0tr8rzaYKFqx3JSJwU/hoG0m0iZm9HkDnk/DTt1GgPi50QrK1MpFz6uwfQEYBcdSZpgE9epSvL5s2il5E5NghF84WK5C+Nxl4jnPjoFTt6x4Xpwlf6gzxJiL3b9/H0WLFsXEiRNx/Phx+Pj4YNiwYRgyZAgA4PHjxyhcuLDNNoULF0ZISIhinSaTEUaj+QLA+j+lj+2kjm1kH7ZT+kymzLWHJNnxA3Uu4Sxx5hb3799HUlISRowYgeDgYPj6+mLixIno3r07YmNjkZiYmG4f+PjxY8U6jSYZRqMJRqP5s7flf0of20kd28g+bKf0GU2Z/a7kTFm0nSXOF+OQgVODwYBDhw7hu+++w8yZM3HhwgWMGTMGxYsXx8CBAzNU58wqjWyu/1q7pcI9KaV5ZTs4OoRcb36Fdo4OwSksrMhf/YkywmAwYMeOHfjxxx/xww8/4NixYxg/fjzKlCmTZhaOvUJvXrO5fv0yZ6fag+2kjm1kH7YTkX0MBgO2b9+OOXPmoHz58ti5cyfeeecdBAQEoFy5chmq8+rtCJvrwTfCsyLUPI/tpI5tZB+2E+U1Dhk4LVSoEOrXr289Hb9OnTp4+eWXceDAAQwcOBA+Pj5pZpc+efIEPj4+inW+d/UUQuJj4KHR4tfaLTE6+BDiLb9ui2abaVVmognKMzfjVHBqtlYwM1Fl5pxIytmd7pIW88p2wMTbu5EgG2FSOUVaVC7cNjfOOFV5zlPOOJ1foR3G3dyLBNl8LImeG9Gp7QCgzeCp+mqnfBtN2TTjVJCWICnJdsbpwoptMfbGPms7mZIEM05VjjURZ5pxWs7VG5/6N83QtpS/FCpUCNWqVUO9evUAAIGBgWjTpg0OHDiANm3apNsnPn78GEWLFlWss1yFynD38ITRaMT1y+dRqVotaLVaxfvnd2wndWwj+7Cd0pcQH5fmBx0iwNwHdunSBZUrVwYAdO/eHcuWLcPhw4dRo0YNuLm5pdsHVqxYUbHOKmV94enuCqPRhOAb4ahdsTS0Wocs7eEU2E7q2Eb2YTulLy5Bn+YHHXIuDhk4rVmzJi5cuGBzm8lkgouLCwCgVq1aafKZ/vPPP2jUyHZWaUqJRhPijUbrYFu80fh84FQ0nqV2iqlgWrVGNFCmljpVUC6J9ikYuDPXa98gj2UsLt5kRLxsUM1zKqrVJFhjTLXeTAy6ComeGpXn3DI4LSc/rHj5+bEkCSrWqZ2qL9pWMKiqxpCJ9hcRbZpkk77A/Dail5KQCAMAwKhR/rJo0mfii6Sg/1UbHBUSvG4yWm9CJp5TK1kWvgflGjxVP1Nq1qyJK1eu2NyWXp/4yiuvWMvPnj2L1157TbFOjUZrM2ij1Wo5iGMHtpM6tpF92E62NILPBfaQkv85A2eJM7eoVasWHj58aHOb0WiEi4sLJElCjRo1EBwcjGbNmgEA9Ho9Ll68iFGjRinWqdVINoM2Wq2Ggzh2YDupYxvZh+1kS6uy3oYq5qtxOIcczd26dcPdu3exYsUK6PV6nDt3Dn/99Rfat28PAOjXrx8OHz6Mv/76C7GxsVizZg2Cg4PRr18/R4RLRESUbfr27Ytjx45hx44dSEpKwuHDh3HgwAG0a2dOEzJgwAD88ccfOHjwIGJiYjBv3jxER0ejW7duDo6ciIgocwYOHIg///wTQUFBSEpKwubNm3H58mW0atXKWr548WKcPXsWz549wxdffIGiRYsiMDDQwZETEVF+4ZAZpwULFsTChQvx0Ucf4YsvvkDx4sUxZswYvPzyywCASpUq4dtvv8U333yD//u//0NAQADmzp0LPz8/R4RLRESUbfz9/TF37lzMnDkT06ZNg5+fHz788EM0bWpO9dCsWTNMnz4d77//Ph48eIBq1aphwYIFKFCggIMjJyIiypy6devi008/xYwZMxAREYGAgAB8//33qFSpEgCgd+/euH//PiZMmICnT5+iQYMGmD9/PnQ6h3yNJSKifMhhPU6dOnWwceNGxfKOHTuiY8eOORgRERGRYwQGBmLbtm2K5f3790f//v1zMCIiIqKc0b17d3Tv3l2xfNy4cRg3blwORkRERPRc3vmpToY5KaMlMWPKv0XJMjOxUI0sWDxHuCAVxOk7JWECBXF2BVEOVFH+U/XcqMrtJFzYSC3HhWDbzOTnFCXolFVST5qs/5vrMJkk6wJYGkH7G43iekVtbBS0k1bluRG2vzgkYRuLnjptigW2LPFptTK0llyXgkY2GVyUK1bNJyo4XjKTU0y0qeg4zK7j93klkNUO2NyAOU6JiCgbSXCetGlOEiYRETkNCc7TuzhLnC+GGXuJiIiIiIiIiIiIUuHAKREREREREREREVEqeedUfSKivMYkmy+5nUqMZ86cQXh4OHx9fdGoUSNoNM9/s5NlGadPn0ZYWBjKli2LBg0a2GyrVk5ERERERJSnOUu+mjyKA6dERJQtEhMTMWbMGISFhaF27doIDg5G0aJFsXz5cri7u8NoNGLSpEm4ePEi6tati7Nnz6JBgwaYPXs2JElSLSciIiIiIiLKTnlr4FTG87VjUv4tSlCrOptLsMhQhray3EGwtahIZcEZk2DBqpQLR1kW0pFlKfOL2IgeSyYWnVJtxIxOxFNZEOz5IZTcRiYJcvI2oqaSVeo1Cp4b4RiQRrw4kGjxKNWllgQPyCR4PCnXApJT3Ga53fDIXXmnwgWgMnMsio5DtU0Fj1W0ragsEwvP5RWrVq3C3bt3sXXrVnh4eCAmJgYdO3bEli1b0K9fP2zbtg0XLlzApk2b4OPjgwcPHqBXr17Ys2cPOnTooFpORERERERElJ2Y45SIiLJF165d8euvv8LDwwMAUKBAAZQrVw73798HAOzduxedOnWCj48PAKB48eJo164d9uzZY1c5ERERERERUXbiwCkRUW5lmcbrDJd0+Pr6IiAgwHr9zp07uHjxIgIDAwEAoaGh8Pf3t9mmbNmyCAkJsauciIjyB0lyrgsREVHWkZzskvdw4JSIiLLdw4cPMWHCBAwaNAj169cHAMTGxlpno1p4eHggLi7OrnIiIiIiIiKi7JR3cpyaJEgmCVLyCLeUfB0A5MzkVBTmQM1gXkQAkmDIWmHylh2FgDCmFDkXZSlF/k5Zssl/mv5ulesV/bKuUclxatJkPP9spnOzKu7XUq/0/Lo1x6lyTCa1KQaCnJeifKKqv28IcqBmKmOoKO9nirL08uVKBsGetaKdqsSU0UK1hhDmMRXlThUlvc2bv7ZlxI0bNzB27Fh06tQJ7777rvV2Nzc3JCYm2tw3MTERbm5udpUTERERERERZae8M3BKRJTXyCbAJP5RI1eQlWO8fPkyhg8fjokTJ2LYsGE2ZWXKlMHdu3dtbgsPD7eenq9WTkRE+cXzyRG5n7PESURETsGZ8sA4S5wviKfqExFRtnj27BlGjx6Nt99+O82gKQAEBgbiwIEDSEpKAgDEx8fjwIEDaNmypV3lRERERERERNmJM06JiChb/Pzzz9BoNIiLi8OSJUustwcEBKBNmzbo06cPVq5cidGjR6NVq1bYvXs3ihcvjh49egCAajkRERERERFRdso7A6cy0uY4lFP9nyGiHIaCilXOrhWlPxSlVJSFuTABCPK52uSlTP5Thm1uSsWYBEEJt1XJcSouV0sUKyjL1HMuqFbU/irhyoIJ3kajaEu1U7UzPnFceAgLHqsoXy4ASAbRTgX16sRPnOg0PVnUDKo5TjOYx1TEmDdPU3gRfn5+aNmyJa5du2ZzuyVHqYeHB1avXo1169bhzp076Nq1K1599VXodDq7yomIiIiIiIiyE799EhHlVuZfNhwdhTqFEIcOHaq6qbe3N0aNGpXhciIiyvuY3o2IiPIvCc6TP9tZ4nwxzHFKRERERERERERElAoHTomIiIiIiIiIiIhS4an6RES5lGwyQTap5bh1PGeIkYiInBdPUiQionyL+WocLl8MnEqixWjUVhESPe9qCzVltF5htSrxihaWSrk4VPLfloWh1BedEhcrUXvdaASL8pg04o1l0YJKwhW2MvG8iY4l4QJPgEkUkqCB1VJcagQLgqm1v2gBKKNROSZNiMfzK1otUBFAqId1lStJuFCTYKExteNQsK0kWsxKbZEy0bGW0RSjHEskIiIiIiIicmo8VZ+IiIiIiIiIiIgoFQ6cEhEREREREREREaWSL07VJyJyTrJ6roZcwRliJCIip8Ukp0RElK+xc3GkvDNwmt4HKst1wXd6SeUAlEWJKUXzddXGEUT5LgW5GiWtuGJZlL8zxQCM5U85eVzGpJL3UyMYvMlU/l9R7kmVvJSiPJrCXJmZGOMRjmGp5U4V5UcVVawV12sSHaMqRO0kG5XLdIbnf1ueBo0R0CTfLmuV9ynKXSsJ8rUCKk0sKJPU8uWK2jDD+YiJiIiIiIiIyJnxVH0iIiIiIiIiIiKiVPLOjFMiorzGJJsvuZ0zxEhERE5LSv7nDJwlTiIichJMV+NwnHFKRERERERERERElAoHTomIiIiIiIiIiIhSyR+n6oumC6ucYSo63Ua4oI/aHGWTaFPleoWLHkFlcR05nb9ly0Vt8RzlMXZJK3owGadRWRzKJFiVSrilSr2qizxlcDvxwlLKRepnQYsWwlKJSbAAlHBRtRTbScnPg2SUUtwuWuxKUK/qoSR4zkULp6nUK4lWOBM2r52vt4ySZUDOntdXlhIe3ERERJkjSZlcjDQHOUucRETkLHiuvqNxxikRERERERERERFRKhw4JSIiIiIiIiIiIkqFA6dEREREREREREREqeSdHKeSDDn5AsDmbymjOSsBcZ7CTKQ4FW4rymOaiXpTpiGUk9tEliXz32qJNAV5KUV5V4U5V9Wo5CKVBOUaUbxqKU4tKSUtsWssiWABSfBYM5XmUXCMCvOQAhk/lgBh7k+P2y7KhSmqteTykozmS5o7pN5UdKypPdYMvuhktZ+IRMeaYFtRDuQM58pNWYVJhqye5NbhnCFGIiJybnkzaxoREZEKJvp2OIcMnN68eRO///57mttHjhwJf39/AEBMTAw2bdqEsLAw+Pv745VXXoGnp2dOh0pERJSt/v33X/z5559pbp8yZQoKFy4MAHj8+DE2bdqEyMhIVKpUCS+//DJcXAQ/bhARETmBQ4cOYd++fWlunzFjBjQa86/Xd+/exebNmxEVFYXatWuja9eu4oU9iYiIspBDTtW/efMmtm7dipIlS9pcLF8C4+LiMGDAAGzfvh0FCxbEli1bMHjwYOj1ekeES0RElG2Cg4Nx4MCBNH2iVmueOv/w4UP07t0bx44dQ8GCBbF8+XKMGzfOwVETERFl3vHjx3HmzJk0faBlYPTmzZvo1asXrly5Ai8vL8yePRvvvfeeg6MmIqL8xCEzTmNiYuDr64uxY8emW75hwwZIkoTffvsNrq6ueO2119CjRw/89ddf6NevXw5HS0TkKKYUuSNyM2eIMfeKiYlBxYoVFfvE3377DeXLl8eCBQsgSRIGDx6MDh064PDhw2jRokUOR0tE5CCcYJgnxcTEoE6dOop94Lx589CuXTt8/fXXAICXX34ZXbt2xahRo1CpUqWcDJWIyEEkOE8n6CxxvhiHzDiNiYmBl5eXYvnRo0fRunVruLq6AgDc3NzQqlUrHD16NEP7k1PkP019sR6DSpeMktUukuAi2M4kvsiy8iVNXSnitOQ6VbyYBBfhtioxZYIkyYoXjcakeJE0sviSop7U+3mh9s2pi0kSXFSOGcFxKMlQvphsL0Da25QuGoPyxZInVfkiCS72bJ/+JcOvR7ULkR3U+sSgoCB06NDBOvumUKFCaNy4MY4cOZJTIRIREWULe/rAjh07Wq/7+/ujatWq7AOJiCjHOGzGqYeHB7Zs2YJLly6haNGi6N69O3x9fQEA4eHhCAwMtNmmVKlSCA4OVqzTTdLCQ2O+ALD+ryYzC0fJogVl1KoVlYsWVFJbbElQnnKhpjTtpBKvaJEnjWifUnaOzWfsuVNbL8mycJZ7ctu4pziWTBrR43HUryuZWfxMeVsPnX2vIffk+7mnuL8s2NSkUz5eTCq7FNUrC45DtcWhRPUKX3OCpnfX2td+RDExMXBxccH69etx8+ZNlCpVCr169UKhQoUAmPvEUqVK2WxTqlQphIeHK9ZpMhlhNJovAKz/U/rYTurYRvZhO6XPZGJ7UPpiYmJQvHhxrFixAuHh4ShXrhx69eoFDw8PJCQk4NGjRyhZsqTNNn5+fggLC1Os02iSYTSaYDSaf923/E/pYzupYxvZh+2UPiMX0nV6Dhs4DQoKQrFixVC5cmWcOHECc+fOxerVq1GlShUkJiZaZ5tauLq6IjExUbHOmTUa2lz/tT5PX7THL1XaODqEXO+XSm0dHYJj1H+xu//YtWn2xEGUx8XExGDHjh1wd3dHmTJlsGvXLsyfPx8bN26Er68vEhMT0ywEpdYnht68ZnP9+uXz2RJ7XsN2Usc2sg/bicg+sbGxWL16NV599VUULVoUa9asweLFi7FhwwbrDxAv+r3w6u0Im+vBN5R/aKTn2E7q2Eb2YTtRXuOQgdPXX38do0aNgo+PDwBgzJgxGD16NBYsWIBvv/0WXl5eiIuLs9kmLi5OeBrHexdPIyQ+Bh4aLX6t3wKj/zmMeDt+3c5zM0619s84/aVKG4y5ut/cTtk24zQbf13J4HOn9vuXbHw+4/SXSm0x5vo+JCQfSyaDYOqi2lTW7CLar+qDVd7WM8y+twd3nRY/dm2KyduPIcFgbifhjFMXwYxTlV0604zTAM8C+KxGA/GOVcgmQHaCXyidIg1rLjZjxgy8//771hmmo0ePxiuvvIJly5bhv//9Lzw9PREfH2+zjVqfWK5CZbh7eMJoNOL65fOoVK2WdbEpSovtpI5tZB+2U/oS4uPS/KDzIqTkf87AWeLMLebMmQNXV1cUKFAAADB8+HB07NgRf/zxBwYMGABJktL0gbGxsShRooRinVXK+sLT3RVGownBN8JRu2JpaLUOyVDnFNhO6thG9mE7pS8uQZ/mB50XIwGSs/QtzhLni3HIwKm3t3ea26pVq4Zz584BAAICAnD79m2b8pCQEJQvX16xzkTZaDNQGm8yZs3AqWDMQjRQk6mBU2FyRJVBFMFApZTOttZ2Uhs4FexXIyiTNNk4opLRgVOV7eRUA5EJKY4lk2gQK48NnEqGF3s8CQYj4i0Dp8JmEgycZvzlCFmwU7V8usJi0caCeBN4mibZqXDhwjbXNRoNKleujAcPHgBQ7hMbNrQ908K2Dq3NoI1Wq+Ugjh3YTurYRvZhO9nS2JlCi/Ify0QaC3d3dwQEBODBgwdwdXWFn58fQkNDUa9ePet9QkJC0L59e8U6tRrJZtBGq9VwEMcObCd1bCP7sJ1saTV5czAxI27duoVnz56hYsWK1h/MsvL+2cUhR/M777yDqVOnWq/r9XocPnwYNWvWBAC0b98ef//9N548eQIAiIiIwN69e9GpU6csj0W0cJTa4lGSLCleMheU6CJaxOYFL0hZn9p+lS8mkyS4aDJ8kVUuwiYULFil1i4pF74CYHs9uy7ZtcCQymP1DNUpXrIrJskkKV/UFnESLXSVmZgEC2FxcSjKbsOGDcOXX35pvR4TE4OTJ0/a9ImbN29GQkICAOD69es4deqUzWIZREREzkaWZXTt2hW//fab9baIiAhcuHDBpg9Medr+sWPHEB4ejjZt2jgiZCIiyoC4uDgMHz4cAwYMwIwZM9CqVSvs3Lkzy+6f3Rwy4/S1117D8OHDMXDgQFSoUAGnT5+Gl5cXxo8fDwDo0qULNm/ejL59+6JBgwY4efIk2rRpg9atWzsiXCIixzCfq+/oKNQ5Q4y52IQJEzB+/HhcuXIFJUuWRFBQECpUqIBBgwYBAP7zn/9g165d6NOnD6pXr44jR45g5MiRqFatmoMjJyLKGZITnaXoLHHmBpIkYfLkyZg+fTqOHz+OQoUK4cCBA2jbtq11wsz48ePxn//8B6+++irKlSuHQ4cO4Z133kHx4sUdHD0RUU5y7s5l/vz5iI6Oxv79++Hp6YmtW7di+vTpaNq0qTVdWWbun90cMnBatWpV7NmzB0FBQYiKisLLL7+Ml156CZrkFcu1Wi1+/vlnHDt2DHfu3MGAAQPw0ksvOSJUIiKibNW0aVPs27cPx44dQ2xsLPr374/69Z+vzubh4YHff/8dR44cQWRkJEaMGIHatWs7MGIiIqKs0a1bNzRq1AgnT55EUlIShg8fjho1aljLfXx8sGnTJhw6dAhRUVGYOHEiKlWq5MCIiYjoRe3YsQPjx4+Hp6cnAKB79+749ttvcfDgQfTs2TPT989uDhk4BQAvLy906NBBsVySJDRr1gzNmjXLwaiIiIhyXuHChdGlSxfFchcXF56WSEREeVKJEiXQvXt3xXJ3d3empyEiclJ6vR63b99GxYoVbW4vX748rl+/nun75wSHDZzmGqqL0QgWPhLlMlXLbyjar7BelYoFZ8ymrNZSjSybL2orgArPxBWFq7LajyRYKEg1RWQGz4WSjSqP1WCe+Swnz4CWDZrnK5uLts1MTktRwmi1eh2QS9PmWEqVLvdFtn1RwpdNRjdU21gYcPY2fonyRbO1/qziLHESEZFzSkyMV79TLuFMsRIRUe4Xr09ydAh2Sy/WmJgYyLJsnT1q4eXlhejo6EzfPydw4JSIKJfR6XTQaDQY+GkvR4diN41GA52OXQoREWUdS38YfjvE0aG8EPaJRESUWZY+MOTuI0eH8kJS94EuLi4AAIPBYHM/g8FgLUvpRe+fE9ijExHlMq6urqhZs2aaziI30+l0cHV1dXQYRESUhzhjfwiwTyQioszLK32gt7c3vLy8EBERYbO47f3799G4ceM027/o/XMCB06JiHIhV1dXfukiIqJ8j/0hERHlV3mlD3zppZcQFBSE1q1bAwAiIyNx5coVNG3aNEvun904cKpGmHrSEcklVcpVcoo+ryf5fkYJMEnqeSdF+UQFeUpVZTBPqdp+hblVk3OYKrLkMU3OcYokDWDJcSrK9arGAakyC9wUv8RlQVNkJhdphqnmHBYVKm+sUUkLY9KJchlnaJdERERERERE+d7YsWMxcuRIFC5cGAEBAfj111/Rvn1764zShw8f4tatW3jppZfsun9OUxlBIiIiIiIiIiIiInpxDRs2xOLFi3Ht2jWsW7cO7du3xzfffGMtv3LlCn7++We775/TOOOUiIiIiIiIiIiIskWjRo3QqFGjdMsCAwMRGBho9/1zGmecEhEREREREREREaXCgVMiIiIiIiIiIiKiVPLOqfpSigtS/Z0Zmly2MozqPgXx2iyYJD2/zZ4FpYR3yfjCUZlrQsHGosdkFO9UMpjLJW3y/0YJkmUb0eJQag9G1BaZaIcCIVrFMtWQBGWycrU2D8XytyQ//1v0rAsXW1JZJEsUr0avXGYSPBYAkATPq7ANRYVcOYqIiIiIiIjIqXHGKREREREREREREVEqHDglIiIiIiIiIiIiSoUDp0RERERERERERESp5K0cpxr5eU5SjQxrpsVsSzWYy/Kfqu3XJtwXzHGaUWpVZ9euBY/JksNUkTUHqvT8utGS71R5M1GeTJv6XrDIMzzjv2+opJgV5iIV7dUmZ6ic4n/L36K2EDWDShtq4pQ3NrkoPxpJrQkFx4ssyHMsTFur0vZERERERERElLtxxikRERERERERERFRKhw4JSIiIiIiIiIiIkqFA6dEREREREREREREqeSdHKc62Xyx5CPUyYDJjiSDancR5QwVpcqUM5FcMrvyo8rp/C1DvQ0yReWxZNO+JaNgv6IyABpD8v/y8+ua5NymovyoqjktBeVujzP+nAvzgqrEJNpWFvyskrJISg5dMj5vJ2GqUsFD1cSL20HWKpdJoly9Ku8Fwpe5KJWx8HUs3CURERERERER5XKccUpERERERERERESUCgdOiYiIiIiIiIiIiFLhwCkRERERERERERFRKhw4JSIiIiIiIiIiIkol7ywOJSUvDGVd0SfFqkeZWmwpoyu8qOwzowtLZdViVlKK/yU76s0otXqF7ZCJugVlwsWU8HxhKSk5OMkoPb9NVK9RXK9LdAYXGlM7fAUxaVRiEr40RO2bYruUi0NZ2kASLQAliMkkWPxJLaYML+KEzK3zRkRERERERER5E2ecEhEREREREREREaXCgVMiIiIiIiIiIiKiVDhwSkRERERERERERJRKHspxas6raM23mOJvUfJD1fyFoiSQwm0zkxgxB3KyWtpEkpP/Vtmns+V5FOQxVctxat1WSnE9+TZRHlPJkD1tqJaiV5RPNDPpfUU5Q1O2r6U9JVOKvwXtJKvlMc2oTByjwk0zmn82My9jIiIiIiIiInI4zjglIiIiIiIiIiIiSoUDp0RERERERERERESpcOCUiIiIiIiIiIiIKJW8k+NUIz+/WK5bMhcKEhhKaokIM5MfVUSYO1VQcVblZJVS/C/ZU7EDEjaq7VIUsqgdTOKKU6Z/tfxv/VuQx1SbIKw2+2Qiz6YoF6mI0fX53ybt8/9NludE9JNMZvKCCuqVRWUqeVVF5cJ6NYL3B0EZEREREREREeV+uWLG6e3bt3Hv3r00t8fExODGjRuIiYlxQFREREQ578aNG3jw4EGa26OionDjxg0kJDjqVxoiIqLsdeXKFURFRaW5/dGjR7hx4wb0en3OB0VERPmawwdOQ0ND0aNHD8ydO9fm9rlz56J58+YYM2YMmjdvjl9++cVBERIREeWMf/75Bz169MDq1autt8myjI8++ggtW7bEmDFj0KxZM/zxxx8OjJKIiCjr7dixA7169cLff/9tvU2v1+ONN95A27Zt8dprr6Fly5Y4cOCAA6MkIqL8xuGn6n/wwQfw9/e3uS0oKAiLFy/G2rVrUa1aNZw/fx6DBw/GSy+9hHr16jkmUCIiomyUlJSEDz74AAEBATa3b9q0Cbt378aOHTtQunRpHDp0COPHj0ejRo3S9J9ERETOKDo6Gl9//XWafm3RokW4du0aDh48iMKFC2PDhg14++23sW/fPnh7ezsoWiIiyk8cOuN0w4YNMJlMaNWqlc3tW7ZsQefOnVGtWjUAQK1atdC2bVts2bLFEWESERFlu4ULF6JatWrWvs9iy5Yt6NevH0qXLg0AaNmyJWrVqoUdO3Y4IkwiIqIs980336B79+4oXry4ze1bt27FsGHDULhwYQBA3759UbBgQezfvz/ngyQionzJYTNOHz16hNmzZ2Pp0qVYv369Tdm1a9fQuXNnm9sqV66M48ePZ2xnwgVnMr6Ai5SZ9ZJE6z9ldNEjtY1TLoqUZnEoNRlsJ7V4BYtvqVOuW1StZFKpNbnc8vxKpue3aZJeILw0FQvKMrXQmHKR133xgzW6KJfFl8j47yom0WJMovXLVHYpLBc86SZXcQOLF5YSbCt6nA5PhELO5ObNm1i1ahU2bdqEmTNn2pRdu3YNffr0sbmtSpUquHbtWk6GSDngwbNExbLfTt9RLBvXNEBYr0nw2aCwp6AjICLKAadOncKRI0ewZcsWjBo1ynq70WjEzZs3UalSJZv7V6pUiX0gERHlGIcNnH7++efo27cvKlasmKbs2bNnKFCggM1tBQoUwLNnzxTrc5e08NCYLwCs/6ty1MLXggHFzA2cigqfb5umndTaIaPtlJ0Dp4K6NVrlUSuNVhyTZTF0d63W5n8A0OoEg7VqA9AZHcxVGegVjc+5u4qDEg2cyjrlmlMOjLrrtDb/m7cV7DQzA6eCl7VogNMgGvxU2a9w4FSwnbu970GU78myjA8//BATJ05EsWLF0pRHR0en6RO9vLwQGRmpWKfJZITRaL4AsP5P6cst7WQyKe9fJ/pxSCVuk6ADMhrt+5Unt7RRbsd2Sp/o2Kb8Ta/X4/3338eMGTPg4eFhUxYbGwuj0Zju98KnT58q1mk0yTAaTTAazR+iLf9T+thO6thG9mE7pc9octSgE2UVhwycHjhwAGfPnk0zq8ZCp9Ol+cBpNBqh1SoPRHwW0Njm+i+V22Q6zvzg11otHR1Crvdzm2aODsEp/PByU0eHQOSU1q1bh4SEBAwYMCDdcq1W+8J9YuhN25k41y+fz3yg+UBubqcuPspld29eyHC991/w/rm5jXITthORfebPn48KFSqgbdu2acos/ZzJZDsIYzAYoNMpf429ejvC5nrwjfAsiDTvYzupYxvZh+1EeU2OD5zq9Xp8/PHHGDJkCG7dugUAePz4MWJjY3H58mVUq1YNvr6+ePDggc12kZGRKFmypGK9M0JOICQxGh4aLX6p3AZjru1HvD2/buenGacm2xmnv9ZqidHnD5nbyRlnnBoFM06TBDNOE1VmnBrM/7trtfi5TTOM3x+EhORBC22CYMap2uGWXTNODcplBTJzqn4x+2ec/vByU0z58xgSDOZGkEVnfjpixqlnzs84DfAsgM9qNBDul+jJkyeYNWsW/ve//+HKlSsAzGddPHjwADdu3EDFihUz1CeWq1AZ7h6eMBqNuH75PCpVqyUcaM3vcks7PYxWPlV/xVnlLyGjGpUV1iuacVrIw75T9XNLG+V2bKf0JcTHpflBhyg0NBS//fYbZs+ejUuXLgEA4uPjce/ePYSGhqJcuXIoUKBAun1grVq1FOutUtYXnu6uMBpNCL4RjtoVS0MrOBstv2M7qWMb2YftlL64BH2aH3TIueT4wGliYiKKFSuGHTt2WBe2uHv3LoxGIz777DP8/vvvqF+/PoKCgqzbyLKMoKAgvPLKK4r1JpiMiDc+HwCMNxqfD5xmJhdpriN+MLJooDJlUfKAZbxkQLzqiJ/qbrOHSW2QWJTjVHnAUKtyWqI2eWBVSj4tP1FvQoLBXJ9G0FSSYAATEA+OZrQMAHwuKideVTsD80ll5S/MsuA0Q5umT44vwWREQvJrTnh2hmiQUmUcXfC0Ql9Y9OSI6xUP5opGvJWLEqByQBABePr0KQICArBy5UrrbaGhobh27RqioqLwww8/oEGDBjhx4oR1Rqper8epU6cwY8YMxXo1Gq3NoI1Wq+Ugjh2yu50iBTlMAaD6yCXKhQ9CFIu+LK88gAAABr3y+9GR714Vbttt5i4AgIeLhPWvBaDef7cgPsn8vtiyeQXF7RYNqies10UlbY6z42vOlobpaygdDx8+ROXKlfHTTz9Zb7t9+zb++usvPH78GB999BEaNGiA48ePo3Xr1gDM/ebFixfxf//3f4r1ajWSzaCNVqvhII4d2E7q2Eb2YTvZ0mry9mee/CDHB069vb2xdu1am9u++uorPHv2zHrq/oABA7B8+XJ88cUXaNeuHbZu3YonT56gX79+OR0uERFRtgkICEjTJ06dOhUVKlTA5MmTAQDDhw9Hv3798NNPP6FBgwZYuXIlihQpgg4dOjgiZCIioizRsGHDNH3goEGD0KdPH7z6qvlHndGjR2PMmDHw8/NDhQoVMH/+fDRs2BD16tVzQMRERJQf5YqfAXx9fVGqVCnr9eLFi+P333/HgwcPMGvWLCQkJGDFihVpEoMTERHlNaVLl7ZZJKpSpUr47bffcOHCBXz77bcoXLgwfvvtN2F+NyIiImcUEBCAwoULW683adIEc+fOxcGDB/H999+jZs2a+PHHHx0XIBER5Tu54lvXiBEj0txWuXJlfPfddzkfDBERkQNNmzYtzW3169fHvHnzHBANERFRzvniiy/S3NaqVSu0atXKAdEQERHlkoHTbOeIBaAyk8ZCuK34wUh2ziGWkvM2ShoZEmT1RZoy+HgkUX5IALIoj6lKjlNZMGFalCtTdQEinXljy6JAslaGnNzuJkFDqDxUaJRTkQoXeCp+NkGlXuXEnyZXlQcrCXKcCjZNuTiUZcEmWQuYZNvb0iXKcapynOmLCvKuip4AtdeF6PgXxSQqYzo3ojxLb1B+39188Z5i2ZgP/xRXHH5JuUyr/JHNEHJBXK9gsczAQZ+Lt3X1AAAkuekABODZ9cuISzR3WlvCwhQ3eyVGnM91/WuNFcvcXXLFCVFERERERPlk4JQolxrZqiG616kovI8u/vmg3t79V7BmzYnsDouIiCjHuekAFxihgR2LVgIwSjoY1X5xIyIicgKyzl04MSg1CSZIBvEEGyLKGhw4JXIg38LeqF62NJ4eXQFjQrTi/bTu3ijUfDDOnVeeyUREROTMNJIEFxdXu/tE2SjD6IizioiIiLKYJGkhuXjY3QciKT4HoyPK3zhwSuRAvx04hYFNayEx/AIebkmb08miWI/pMCQlYc2aYzkYHRERUc5JMMhw0xrt6hNlkxFJJp7ST0REeYQhHrLW1e4+UDJw4JQop/ATJ5EDRTyNwbrjF1C463+hcfdO9z4ad28U7vJfbN7yLx4+jMnhCImIiHKGLAOJRglFu78j7BOLdn8HRrWE5URERM5ENkEy6u3qAyWjHpCV860TUdbKOzNOpRQXpPrbEadxqa21pLaSkOKGKrsVrXGTYgEcSWOuSNKaIEkm1UWlJMHiORqt8pu2RuVxmgQLQBmN4qBEXYUsmIVichd3MrLWHJMheXEog4cMQ/K5gKIFnoQLIgGKKx8tOHoS/V+qCZ8Ok9L9ddGnwyRo3Apg3by9cHkYl7YCQTNFtPXJSEiq9ab8vmr5W9akuF1Qr2ifosWfgOcLdqVL+FhUXm+iegXbSqJ96vhhhiivavXlfsWyq/sPK2/47IG4YjdP5bLEdN7/LTQqHZBgcShhGWBdHOpF3b4dJSx/Gpd+hxqjAcoU9hb3ie7eSLAvDSoREZHzMMRD415ItQ9E4lMHBEeUf/HneiIHi3gWg20bTqNwl2lpfl00zzadhq3rT+NRpHKuGyIiorzAaAKMsibdGTecbUpERHmaYNYpZ5sSOQ4/eRLlAmsWH4LGzTzDJiXzbFNvrFl80EGRERER5awkk/kLYrp9ors3kvh9kYiI8ipDvLAPBHObEuU4DpwS5QIPI56lmXXK2aZERJRfpZ51ytmmRESUL6Qz65SzTYkcK+/kOBVRyQua64jiVcudKshFmjKPqeVvSWPenUYjfgPWCvI1CvcpKAMAjeD7j1p+VKOgbqNWkOPURZwLTjaYnwBLmlSTlxGm5BxwJoPykyMJygCg4vJHgp3KWDt/L7r1bWjNaWOZbbp2wT5ISQbhtooxqRwvomLhd1Mpnb9T5xjOQFDCHKaAOBdpBvOUAoAk2FYjzKsq2M6FH2qInNXVe+KF+K5u3axcaBS8X6sR5TEVUUtUbtIrl7m4i7fVuiT/n9x3anVAci5w0X4b1S4prNa3kJuwPMkEuLt72/aJzG1KRET5Qapcp8xtSuRY/NmeKJd4GPEM29adROEu06Ar4meebbr2JB5FPnN0aERERDnOMutUV8SPs02JiCj/SDHr1NIHcrYpkePwEyhRLrJ24X5o3LxR7v/2mWebLtzv6JCIiIgcwpLrtNz/7WNuUyIiyl+Sc51a+kDmNiVyHA6cEuUillmnbiWrcLYpERHle0ZZA7eSVTjblIiI8pfkWaduJatwtimRg+WPHKeZoJajU3lDtXozVm0Go0neqZz2b0kGJBkarfiNWC0HqvJ2KrklhflRxXXrdBlLdGY0ir98GQzmchfJ/PJw8dbDIJtz1hkNyvlRZUPWfKlbPX8fCni7Y82CfckVqzzrJuXnRu17pixI9yraNmWZ5W9Zk+JvwXMnKhPmKQUAneB4EeThFeYpBaDRKh9LWsG2ouPXxSUTeQ6JyKECp20Q3yEzeUyzg15lFoqoQ9WqfBQsVNz8v2vyG3zBYoA++f1WkOPUaMrUJxYr8yxTmbNNiYgo/0mKgyxJkJIymAOdiLIEB06JcpnHD6Lx9TtrHR0GERFRrsBBUyIiyp9kSHrxgpVElP143hMRERERERERERFRKhw4JSIiIiIiIiIiIkqFA6dEREREREREREREqeT7HKcZXvwJEC4Apbr4Uwb3K6mtOiWgSbFPTYr/NZIMrcriUGrlitupPE7h4lCCBXsAQCNs/+f1FnXzxhtVu2DOlR14lBgNo0nchiYXc+u4S+bH7OmhhyZ5cagko/JqSgaVxaFktwy+3FSa/m4HH+VNXVSqFpSLyuQUCybJyQs2yToZcvLyZfYuLJWGyiJOkmCxMVGZaPEnAHB1FSwOJVgYTSNaHCqDi5cRkeMZn0Q6OoSslbzIYMliBfH5m73xv+834f7DZ+Yyg168bXxybjVTcv+XEAskmt/fdv84QnGzBgGFhdU+i1deYKugR77/eEpERFlOguzqBUkfi0wuuUxE+QxnnFKe91rFNuhWuj5GVWjj6FCIiIgc5t3XOmNQ98Z457XOjg6FiIgoZ7l4QtK5Ay6ejo6EiJwMB04pTyvhXhC9y7yEqw9i8Ir/SyjuVtDRIREREeW40iUKY2SfQFx9EINRfQLhV7yQo0MiIiLKGZIGRq0brj6IgVHrBkgcBiEi+/Edg/K0ERVaIzrRiLY/BSE60YgRFVo7OiQiIqIcN21kR8Tozf1hjN6IaaM6OTokIiKinKHzsPlOCJ2HoyMiIieSZ5JISZAhSbI1t2XKv1U2zDCNMN+iSmJKWXnHsqgsEzlZU+aAtLaTRoYky8LHAqjnKlXcZyZynGrVYhK0sbvOgOJuhdC7zEv4aOc13H2WgG/338RHnV/C8pB9eJD4THFbQ3JqSl1yjlOd1gSdbFKNV5KU858CgMnTVVAoqDdJfCzJgt2aVF7honJhntIUh6ic4n/BoWsXSSd+rJIgB6roGNaq5E51EeRA1WUwv69O5fglIscypnjftfxtNMmAJMOnUmXhto8fhysXmnJffmPLbNOPdyf3hwdv4sM+gZi1eBfuPhXnOD308yjzH7IJeHITO2cPtc7UKebtpridUdCvAUC7r/Yplp34sKNimSi/ORERURrJs02//vsK7j5LwNf7ruPTLlWhNcSb+zYiIhWccUp51n/KtUNMohFzD4cAAOYeDkFMohHDyrdxZFhEREQ5yjLb1KY/5KxTIiLKD5Jnm6bsAznrlIheBAdOKU8q7lYIPfyaYNb+m4hONK/cG51owKz9N9G7TGPmOiUionzBMtv024O2/eG3B28m5zplf0hERHmUZbbpvus2feDX+64z1ykR2Y3vFJQnpZ5tamH5hZGzTomIKD9IPdvUwjrrdFhbxwRGRESU3VLNNrXgrFMiehF5JsepiEaQ31A1D6qgXC0vqIgs2lRQKGUiiWTKeDXJj0ujkaGR1fPBinN7ZqYdlB+PwSh+rEZT+uUl3Auih18TfLTzmvWXRYvoRENyrtPGWHz9ULq5Tg1G8+8JhuRfIA1GDQzJCT+TjMoJRQ0G8e8QsaWVO2bXpwbFsgf1lHPIAchUnl5HMPonKJZpVH7KER6HanmFBUyC41AlTV+Wb0dEOUObMllm8nuAViNBq5Hw2+QWwm1ffu+hcuHDO8plMY9fJMRMS5nbNN3+8OBNfNK3OYpVrYD4pPRzs/oVMfddJpMREU+AkoXdodGY+0JRvlGDUfwmqNMp96cLgm4plo1vXl5YLxEREQCb3Kbp9YHMdUpE9uKMU8pzRlZsne4vixaWXxhHVGyVs4ERERHlIKXZphbm/tCA2iW9czYwIiKi7KYw29SCs06JyF52zzgdMWIEHj5Mf4ZF4cKF0aRJE4wePRoeHnzjIccp4V4Qr/g3wsfpzDa1eD7r9CUsuXEw3VmnREQiPXr0SPd2SZLg4+OD1q1bY+jQoXBxccnhyIjMRLNNLaITDfhm/w180rkqgu9HK846JSKyePjwIUaMGJFumSRJKFGiBLp06YJ+/fpBkpzstCjKOwSzTS0465SI7GX3wOnNmzfRqVMn+Pj4pCmLjY3FX3/9hevXr2POnDlZGiDRixheoSXctDrULOmNn/vWVryft7sOrlodhlVogW8vbcvBCIkoL7h27RrGjRsHN7e0qTSePn2KxYsXIyIiAtOnT3dAdETA1OHt4eZqX3+o02pQs6Q3Tt2JyrkAicgpGQwGhISEYMKECemWP3r0CN988w1iYmIwcuTIHI6OKJnOA1qNxq4+UKvRmGedJsXmYIBE5ExeKMdp//79UaVKlXTLhg8fjrZt2yIxMTHdL5JEOSEi/inOPwlH3fISAPFMr/NPwhGZwNmmRJQxr732GgoWTH9F8h49emDcuHEcOCWHCYuIwqmLt1HVHYCHcj5R/zLFEBGdgDi9cq5tIqKUXFxcFAdOAaBZs2b46aefOHBKDiPLRiQl6fFq7RKq99Un6eEiG51t2QgiykF2D5x++umn8PPzUyw3mUyQZRkmk2OmuEtaQNLKkJIXQJK0zxc8Ei8oI168QLQAlKhe0aJHyffIxLbKRI9Hq33+3Ggk898arQla2QStygJPmYlJxGhSTrObkbV1Fl0PwqLrQcL7pH0sti8Dg8H8BVOSzLcn6l2QkLyNUbAAlKgMAIxuym2Y5K38UpSVv++aywW7FZUBwrXPxE9AijIpxf+W+kwqMWcsoIwvRCZcjA3i41BEI4hHtOAUOb85c+YIU9PIsgyjkac9O6uWlYsJy4//MFCxrMNH2xXLoi+eEe84IUZcrsQl7Q/W3686jO9XHQYAaAOUZ9sULl5YWPVP/kUAAO5a4Nt2BTBk0QkkJB/aGsHqUL8OaSis99iMdsJyIsqdChcujG+++UZ4H5PJ5LDvhEQAIBkS4ArlBWmJiF6E3QOnrVu3VixbtWoVFi1ahFatWtmd4/T06dNYuXIl7t27h5IlS2LQoEF46aWXrOXXrl3DggULEBYWhrJly2LcuHGoUKGCveESERFlmy5duiiWzZ8/H7///rvwPqkdOHAAGzZswKNHj+Dv749hw4ahRo0a1vIzZ85gyZIliIyMROXKlTFp0iT4+vpm6jEQERG9KHd3d3To0CHdMr1ej3nz5mHFihUYPXq03XVu3rwZ27ZtQ3R0NCpUqIBRo0YhICDAWn7gwAGsXr0aUVFRqF27NiZOnIhChQpl9qEQERHZJWPTrFI5d+4cOnbsiG+//dau+586dQqjRo1CjRo1MGXKFJQvXx4jR47EhQsXAAAREREYPHgwihQpgilTpqBAgQIYPHgwnjx5khXhEhERZZuzZ89i0KBB+N///mfX/Xfs2IGpU6eiWbNmmDx5Mry8vDB48GDcvXsXAHD16lWMHDkSVapUwZQpUxAXF4dhw4ZBr9dn58MgIiJ6IS4uLjhx4gSmTJmCsWPH2rXN0qVL8dVXX6Fz586YOHEiYmJiMGTIEMTEmGfgBwUFYfLkyWjatCkmTpyIGzdu4PXXX8/Oh0FERGTjhXKcKvniiy9e6P7BwcEYOXIkXnvtNQBA06ZNsWvXLhw/fhw1a9bE2rVrUbVqVbz33nsAgObNmyM4OBjr16/HmDFjsiJkIiKibDF//vwXuv/58+fx5ptvYtCgQQDMfeLmzZtx9uxZ+Pn5YenSpejcuTMmTZoEAGjSpAk6dOiAnTt3omfPnlkePxERUUZIkoRVq1a90DaXL1/G9OnT0b17dwBA3bp1Ub9+fVy5cgUNGzbEr7/+ihEjRmD48OEAgHr16qFly5Y4efKkzdmKRERE2SVLBk5fVOpE4Tdu3EBYWBhq1aoFAPjnn3/QuHFjm/u89NJLOHNGOTeYRpLNF8t1pMg/KMpxmsGciQ6jEq8oJ6tGk06OU40JGtmk2g6icpMgP6RRJc+jJZ9oetTyqoryVppMgm1V6rXkKtVqzLHpE7XQJzedSZTHVCXH6bNygnyuopygmTlEVdJsCptCsF+ja4q/k2M3ugDG5Icoejyi51Xt5SgqFj4UlXqFx4tgYr6sUc7fxRynZK9p06bZXD9z5gzi4uJQrVo1AOY+MWW/qdVq0aBBA5w5c4YDpw5SuWQBxbIzs15WLKs+Ll5YryHitnJhOnlMLbzKlBXW+9tbbRXL5hy4Kdx28aD6AACTyYhHty9jyYhG0CT3jz4FXBW30wrynxIRWaSegHPgwAF4eXmhfPnyAMx9oGXQFAAKFCiAGjVq4MyZMxw4JSKiHOGQgVOL3377DevWrUNMTAy++uor62BpZGQkihYtanPf4sWL4/jx44p1uUELD0kHd8n8Yd7yPwDhaIxocRcAkASf+4WLQ6mMWIn2ahLtVIVokSddimrdkxc+svyvk8QJ3IUDp5Jg4FSlHYySYOA029pQJabkL3vuyV8MLf8DgEkjGBwVlQHw0Akeq6MGTkUhC7Y1aJ8H5a7V2vwPiB+PSaNcKBr4B1QWesvgdgCgFRz/om01gu1cBcc2UXq++eYb7N69GyaTCYsWLbLm9U6vTyxWrBjCwsIU6zKZjDAajdYFqrhQlVhWtpPJpFyHp6u4nzC4CT6WuSi/p3i6iOuVZOX3KjeVtyrL40n9P6DSXvn0xyO+5tInel0QAcD06dNx4sQJeHl5YcWKFfDx8UFsbCxiY2NRrJjtgn3FihVDZGSkYl1Gkwyj0QSj0fzeZ/mf0sd2Usc2sg/bKX1Gk5NN1qM0HDpw2qZNG/j7++PIkSOYOXMmKlasiIoVK8JgMECrtf0kr9FohB9CP/ZvanN9fgWu1mqPOaU6OzqEXO+XSsozdV5InaypJrda0LKZo0Mgcmq9evVCnTp1sGvXLnz44YdYvnw5ihcvDqPR+MJ9YujNazbXr18+ny0x5zXZ3U7b3qyhcg+18gxKuqtY9Elj5VmjAPDo9mWb60/Cnh9bjzIXVZ7G1xzRixk0aBBatmyJjRs34oMPPsDSpUut/Zwm1eQErVYLg8GgWNfV2xE214NvhGd9wHkQ20kd28g+bCfKaxw6cFq+fHmUL18eHTp0wMOHDzF//nx88803KFSoEKKjo23uGx0dLVw98cM7xxCqj4a7pMX8Cu0w7uZeJMjJXypFM8ZUZ7iJygQzTrPrNHMVWq1gxqnL8w8Y7pIOc0p1xhv3diJBNkAnOOUYyL5T9Y258VR94/MZp79Uaosx1/chwTLTJhOn6nuGuCiH5GwzTt1tZ5wuaNkMrx8KQkLyB1zhjNMSiYplGsHxC2RixqnK61yrFcw4tTP9RWr+LgUxo3hL4X6JUqpatSqqVq2Kzp07o3///li2bBnefvttFCxY8IX7xHIVKsPdwxNGoxHXL59HpWq10gy+0nNZ2U6PY5UX7XrpzQ3CbQ2RyrOI4aI8wOnl5y+s96dJyu9FPx8JEW47r5/5Vz+TyYgnYddQpExl66n6Rbx4qn5qfM2lLyE+Ls0POkQp1alTB3Xq1EGnTp3QqVMn/PHHHxgwYAB0Op11oSiLZ8+ewd9f+X2vSllfeLq7wmg0IfhGOGpXLA2tNkvWRM6T2E7q2Eb2YTulLy5Bn+YHHXIuDhk43bhxI0qXLo0mTZpYb/P19cWdO3cAAFWqVMHVq1dttrl06ZI131t6EmFAPJJgGWVKgAHxMA8UCgdUVJIfCk/lFxVlYtBPFLDaWfyiU4eNKU7Vs1STKBuQKBtghPgUKtHbnuh0/CST+EuDUdAQBpWBSNGAreCsRGEZAMgG8+OxVB+XCMRbtjEIR2uF9SZ5K/8yro3PeN5P0fGidqakyVUw+G9v/tPkvxNkI+KTf6yQBacjGJMEg5QqpzGIBjGFA5wQPzeiFBei9whRvAnOlj+ZHGb58uVo2LAhatR4PtuwRIkSePr0KQDlPrFHjx6KdWo0WptBG61Wy0EcO2RFOxUv6KFYdnJOf+G2MQnK/YS/j3K9zwTbAYCPl/IPd22rlRRuaxn/NBqNeBIGlCjkyWPJDnzN2dII0vRQ/mU0GrFo0SJ06dIFZcuaczXrdDr4+Pjg6dOn0Ol0qFChAq5cuYJGjRoBAGRZxuXLl9GnTx/FerUayWbQRqvVcBDHDmwndWwj+7CdbOXXH5PzEocczRcuXMBnn32GqKgoAEBERAT+/vtvNGzYEADQs2dP/P3337h82Xx62NmzZ3Ho0CH07t3bEeESERFlm2PHjuGrr75CfLx54aAbN24gKCjI+iWxZ8+e2LhxI+7eNZ9uvWfPHly7dg3dunVzWMxERESZpdVqsXPnTnz//fdISkoCAJw+fRqXL1+26QOXL19u/TFx5cqVMBgMaN26tcPiJiKi/MUhM06nTp2Kt99+G23atIGvry/u3buHzp07Y8SIEQCAxo0bY8yYMejfvz98fX3x4MEDvPPOO6hevbojwiUiIso2H374Id588020aNECxYoVw7179zBw4ED06tULgPlL49mzZ9GlSxeUKFECT548wZdffglfX18HR05ERJQ5X3/9Nd566y0EBgaicOHCiIyMxOTJk62LBo8cORIXLlxA69at4ePjg/j4eMyePRteXl4OjpyIiPILhwycFihQAAsWLMCTJ0/w8OFDlCxZEt7e3jb3mTBhAoYMGYJ79+6hdOnSKFCggCNCJSIiylYlSpTAypUr8fDhQ0RFRcHPzw+enp7WckmS8MEHH2DSpEl48OABypYtCw8P5dO2iYiInEXFihXx559/4v79+4iNjUWZMmXg5uZmLXdxccGcOXMQERGBp0+fIiAgAK6u4kXtiIiIspJDF4cqUqQIihQpolhesGBBFCxYMAcjIiIicoxixYqhWLFiiuU+Pj7w8fHJwYiIiIhyRsmS4nzLvr6+PNOCiIgcwqEDp1lKMi+cZFk8SZKer5kjXIk7m/L0ivYJiBePEsUkWsUbEK8Ork2xrTZ5ESmtxgStbIJavuKMtqFwcS1AZUkqFaLVi0S7NamtmJTOdZUFpQAAaivCC+owuYgWIFLZr+DhmFRe4XIWZDm2PA2ypL4YFQBx+6ttLzieZEG9sko6Z5OgkTUZbCMuDUVEqRX3Fs+SUitXIlr8SQ3XKyAiIiIiUsalzoiIiIiIiIiIiIhS4cApERERERERERERUSocOCUiIiIiIiIiIiJKJc/kOJUk2XqxXofl75yPR5TDNDPUHotWkANSlyL/qS45p6NOa4JONqnmIjUJHo9Wo7ytyaSS91NlvyKyYFNhvku1HKciojymgnYAxPlERRGpHUqyVrRPlfYV7li86fM65Of/W/+2c9vUu1TbZwbzo6qmiRW8sETbivKfZtd7ABERERERERHlDM44JSIiIiIiIiIiIkqFA6dEREREREREREREqXDglIiIiIiIiIiIiCiVPJTj9PnFet1appw4MXM5NnM+h6FavBqNckZGbYoybXKOU63GBK1sUn0sTpevURSv2lMupfO/5W9BzlBJJZ+oSSfYNuOHoZDq05YFT6slx6qstTMtamb2KXxAoqS3KtUK6pXsTvZKRERERERERHkJZ5wSERERERERERERpcKBUyIiIiIiIiIiIqJUOHBKRERERERERERElAoHTomIiIiIiIiIiIhSyTOLQ2kkGRqNCZrkRY80GhM0svJCSbmZaAEojcoqQlqVBYqUmFRWETKalMuTjFrFMrV4hYtO5cIFqSRBSGqLQ8mCxaGybfGtzKw6ZWdMsub5/3Ly7uSCSRnapah91QNR3lhWWxxKsACU2rZERERERERElDdxxikRERERERERERFRKhw4JSIiIiIiIiIiIkqFA6dEREREREREREREqeSZHKfZRZR7MjN5KYW5MjOR41RUnjJeGZL1NlmWhDlMAcBgUh5jNxqVyxIN4rF5TQZzsmYrKZ3/rX8L4lU5HEQ5TiHIsalWr3in4mJJeAwLNk5ZpJVT/G/+W5irNDN5VzNI9JoCxE0sOkZF9artk4iIiIiIiIhyN844JSIiIiIiIiIiIkqFA6dEREREREREREREqXDglIiIiIiIiIiIiCiVfJ/jVC1PqTjHacb3K8oBKcqpqNOaVOq1L8dpaiZBDlNAnMfUIMhjqlYvoPx4TGrPjUpeVkUqm0nJ7Z/yfwm2t4m2U6QVlGfXTxgqIcmig9jOHL5y8uOWtTLk5ONPo3Kc5jRJpX0ljXK8otejOP+palhERERERERElItxxikRERERERERERFRKhw4JSIiIiIiIiIiIkqFA6dEREREREREREREqXDglIiIiIiIiIiIiCiVPLM4lAzz4kdy8so/sixZF0PKzAJP2bU4lHhRGeUyrWARGwDQCLZNSrFQkym5nUyyZL2IiBaHEi0AZRIsHGWuV6tYpnFRWWBItK6R6LkRtJFNecr/JcviUMqbidoeAIwZXRxKpV7RIkSqx6joebf3ANck1+FiAkzm50wSPdbMEMUraCeNyutGK4hXK1joSvScq71WiYiIiIiIiCh344xTIiIiIiIiIiIiolQ4cEpERERERERERESUCgdOiYiIiIiIiIiIiFLJMzlOTSbJfElO+GgyqeftBMQ5TM3lWRLeCxFFJMp/qratiFpbmUQ5TgV5TGWjSkSCclmj8tyYRPk5M5YL01yezv/WvwXbqtUr+JlCEuS8hahMhVpIojyxIjr3JOvfLpL5bcTFMwkG2ZCxCrOAKNerKE+pWrmL1qi8neC5cVHZJxERERERERHlbg4dOD1z5gzCw8Ph6+uLRo0aQaN5PrIkyzJOnz6NsLAwlC1bFg0aNHBgpERERNnHZDLh5MmTiIyMhL+/P+rVq2dTbjQaceLECURERKBy5cqoWbOmYwIlIiLKYnq9HidOnEBUVBQqVqyI6tWrpykPCgpCVFQUatWqhYoVKzooUiIiyo8cMnCamJiIMWPGICwsDLVr10ZwcDCKFi2K5cuXw93dHUajEZMmTcLFixdRt25dnD17Fg0aNMDs2bMhiaaVEREROZmoqCgMHz4cer0eVapUwalTp1CrVi38/PPP0Gg0iI+Px8iRI/Ho0SNUr14dX375JV5++WVMnz7d0aETERFlSlhYGIYPH46CBQvC398fR48eRdeuXfHpp58CAB4/fozBgwdDp9OhXLly+PTTTzF+/Hi89tprDo6ciIjyC4cMnK5atQp3797F1q1b4eHhgZiYGHTs2BFbtmxBv379sG3bNly4cAGbNm2Cj48PHjx4gF69emHPnj3o0KGDI0ImIiLKFvPnz4ebmxs2btwIrVaLiIgItG/fHkePHkWLFi3w+++/IzY2Fn/99Rc8PDxw69Yt9OrVCz179kStWrUcHT4REVGGzZo1C9WqVcO8efMAAJcuXULv3r0xfPhwVKpUCT/99BNKlCiBxYsXQ6vV4vTp0xg+fDi6d++OkiVLOjh6IiLKDxwycNq1a1e0adMGHh4eAIACBQqgXLlyuH//PgBg79696NSpE3x8fAAAxYsXR7t27YQDp7IsmS/JySgt181/Z/cjSkttYqxGkBtRI0hMKSrLDJMoXygAk0mQx1SQ4xQGlYYQ7FdYLwC4ZjCHpGruW+U2FuefFdfqUTBBsSwh1lVQb8afc1kt662gbtF+dbrnba+TTNbbdLJJddvsmjSu0SgfD1qVNtQJ8pHqBDlO3QRlHtokxTKilIYMGYKhQ4dCq9UCAHx9fVG8eHGbPrFXr17WPrN8+fJo3Lgx9uzZw4FTIiJyam+88Qa8vLys16tVqwadTof79++jUqVK2Lt3L6ZOnWrtIxs2bIiAgAAcOHAAAwYMcFTYRESUj6iMTGUPX19fBAQEWK/fuXMHFy9eRGBgIAAgNDQU/v7+NtuULVsWISEhORglERFR9itTpgxKly5tvX7u3Dk8ePAATZo0AWDuE8uWLWuzDftEIiLKC8qXL48SJUpYr+/evRvu7u6oW7cu9Ho97t69m6YP9Pf3Zx9IREQ5xqGLQwHAw4cPMWHCBAwaNAj169cHAMTGxlpn1lh4eHggLi5OsR53SQsPSQd3SWu9buGACaeqs+q0kvIMN1fBtu6C7QDxjEhNijZxS14J3fK/SRIfChqNVrHMKCiDRm16YSamH6rWnTFScr0eyY/LI8Xj0wr2qZHEv0OInnNJ0IaSYHayGll1dq0y0axRtxRl6b3mhLNks2vGqaB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"text/plain": [ "
" ] @@ -4838,6 +4838,9 @@ "images = []\n", "for column, (cube, label, unit, cmap) in enumerate(definitions):\n", " vmin, vmax = np.nanmin(cube), np.nanmax(cube)\n", + " if np.isclose(vmin, vmax):\n", + " vmin = max(0.0, float(vmin))\n", + " vmax = vmin + 1.0e-6\n", " for row, k_index in enumerate(layers_to_plot):\n", " image = axes[row, column].imshow(cube[k_index], origin=\"lower\", cmap=cmap, vmin=vmin, vmax=vmax, aspect=\"auto\")\n", " if row == 0:\n", @@ -4856,13 +4859,13 @@ { "cell_type": "code", "execution_count": 30, - "id": "eb2e224d", + "id": "7fb80a9d", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:46.869805Z", - "iopub.status.busy": "2026-08-31T17:04:46.869627Z", - "iopub.status.idle": "2026-08-31T17:04:47.466772Z", - "shell.execute_reply": "2026-08-31T17:04:47.465954Z" + "iopub.execute_input": "2026-08-31T17:14:20.250740Z", + "iopub.status.busy": "2026-08-31T17:14:20.250544Z", + "iopub.status.idle": "2026-08-31T17:14:20.823319Z", + "shell.execute_reply": "2026-08-31T17:14:20.822294Z" } }, "outputs": [ @@ -4906,7 +4909,7 @@ }, { "cell_type": "markdown", - "id": "b84db32c", + "id": "934f6cf6", "metadata": {}, "source": [ "## 9. Agent contract: fixture today, licensed RMS worker later\n", @@ -4925,13 +4928,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "5a9db6c4", + "id": "9d11e594", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:47.468738Z", - "iopub.status.busy": "2026-08-31T17:04:47.468563Z", - "iopub.status.idle": "2026-08-31T17:04:47.484413Z", - "shell.execute_reply": "2026-08-31T17:04:47.483648Z" + "iopub.execute_input": "2026-08-31T17:14:20.825108Z", + "iopub.status.busy": "2026-08-31T17:14:20.824938Z", + "iopub.status.idle": "2026-08-31T17:14:20.841046Z", + "shell.execute_reply": "2026-08-31T17:14:20.840223Z" } }, "outputs": [ @@ -5181,7 +5184,7 @@ }, { "cell_type": "markdown", - "id": "676cdf3e", + "id": "c932f109", "metadata": {}, "source": [ "### Licensed RMS adapter pattern\n", @@ -5203,7 +5206,7 @@ }, { "cell_type": "markdown", - "id": "c0657415", + "id": "a1c9f871", "metadata": {}, "source": [ "## 10. Configure and run ERT with OPM Flow\n", @@ -5218,19 +5221,22 @@ "\n", "TEMPLATE_RENDER writes one complete Flow deck per realization. The standard ERT FLOW forward\n", "model runs the real simulator. Four realizations are deliberately small enough for Colab but use\n", - "the same directory and parameter contracts as a larger study." + "the same directory and parameter contracts as a larger study. Oil remains on its 10,000 Sm3/day\n", + "ORAT control in this short forecast, so the ensemble graphics focus on sampled injection,\n", + "pressure response, and the developing produced-water response rather than magnifying tiny\n", + "floating-point differences in the controlled oil rate." ] }, { "cell_type": "code", "execution_count": 32, - "id": "3336be9e", + "id": "0283ffe4", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:47.486147Z", - "iopub.status.busy": "2026-08-31T17:04:47.485958Z", - "iopub.status.idle": "2026-08-31T17:04:47.491596Z", - "shell.execute_reply": "2026-08-31T17:04:47.490765Z" + "iopub.execute_input": "2026-08-31T17:14:20.842830Z", + "iopub.status.busy": "2026-08-31T17:14:20.842665Z", + "iopub.status.idle": "2026-08-31T17:14:20.847724Z", + "shell.execute_reply": "2026-08-31T17:14:20.847141Z" } }, "outputs": [ @@ -5468,13 +5474,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "0ee9fb9f", + "id": "5c6ef113", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:04:47.493204Z", - "iopub.status.busy": "2026-08-31T17:04:47.493010Z", - "iopub.status.idle": "2026-08-31T17:05:28.870342Z", - "shell.execute_reply": "2026-08-31T17:05:28.869428Z" + "iopub.execute_input": "2026-08-31T17:14:20.849629Z", + "iopub.status.busy": "2026-08-31T17:14:20.849442Z", + "iopub.status.idle": "2026-08-31T17:15:03.396448Z", + "shell.execute_reply": "2026-08-31T17:15:03.395693Z" } }, "outputs": [ @@ -5501,7 +5507,7 @@ " 'tool': 'ert.ensemble_experiment',\n", " 'status': 'passed',\n", " 'evidence': {'return_code': 0,\n", - " 'wall_time_seconds': 38.02129026099999,\n", + " 'wall_time_seconds': 39.124149958999965,\n", " 'realizations': 4}}" ] }, @@ -5547,13 +5553,13 @@ { "cell_type": "code", "execution_count": 34, - "id": "40618b40", + "id": "7cd5c731", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:28.872115Z", - "iopub.status.busy": "2026-08-31T17:05:28.871919Z", - "iopub.status.idle": "2026-08-31T17:05:30.542216Z", - "shell.execute_reply": "2026-08-31T17:05:30.541378Z" + "iopub.execute_input": "2026-08-31T17:15:03.398312Z", + "iopub.status.busy": "2026-08-31T17:15:03.398143Z", + "iopub.status.idle": "2026-08-31T17:15:04.994879Z", + "shell.execute_reply": "2026-08-31T17:15:04.994169Z" } }, "outputs": [ @@ -5737,7 +5743,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -5791,12 +5797,12 @@ "ensemble_figure, axes = plt.subplots(1, 3, figsize=(15.0, 4.8), constrained_layout=True)\n", "for realization, frame in ensemble_history.groupby(\"realization\"):\n", " years = frame[\"time_days\"] / 365.25\n", - " axes[0].plot(years, frame[\"oil_rate_Sm3_day\"], alpha=0.8, label=f\"R{realization}\")\n", + " axes[0].plot(years, frame[\"water_injection_Sm3_day\"], alpha=0.8, label=f\"R{realization}\")\n", " axes[1].plot(years, frame[\"field_pressure_bara\"], alpha=0.8)\n", " axes[2].plot(years, frame[\"water_rate_Sm3_day\"], alpha=0.8)\n", - "axes[0].set(xlabel=\"Time [year]\", ylabel=\"Oil rate [Sm3/day]\", title=\"ERT oil-rate ensemble\")\n", - "axes[1].set(xlabel=\"Time [year]\", ylabel=\"Pressure [bara]\", title=\"ERT pressure ensemble\")\n", - "axes[2].set(xlabel=\"Time [year]\", ylabel=\"Water rate [Sm3/day]\", title=\"ERT water ensemble\")\n", + "axes[0].set(xlabel=\"Time [year]\", ylabel=\"Water injection [Sm3/day]\", title=\"ERT sampled injection controls\")\n", + "axes[1].set(xlabel=\"Time [year]\", ylabel=\"Pressure [bara]\", title=\"ERT pressure response\")\n", + "axes[2].set(xlabel=\"Time [year]\", ylabel=\"Produced water [Sm3/day]\", title=\"ERT water-front response\")\n", "axes[0].legend(ncol=2)\n", "path = OUTPUT_DIRECTORY / \"ert_flow_ensemble.png\"\n", "ensemble_figure.savefig(path, dpi=170, bbox_inches=\"tight\")\n", @@ -5805,9 +5811,9 @@ "\n", "final_ensemble = ensemble_history.groupby(\"realization\").tail(1).merge(ensemble_parameters, on=\"realization\")\n", "relationship_figure, axes = plt.subplots(1, 2, figsize=(11.5, 4.5), constrained_layout=True)\n", - "axes[0].scatter(final_ensemble[\"PERM_MULT\"], final_ensemble[\"cumulative_oil_MSm3\"],\n", + "axes[0].scatter(final_ensemble[\"PERM_MULT\"], final_ensemble[\"water_rate_Sm3_day\"],\n", " c=final_ensemble[\"INJ_RATE\"], cmap=\"viridis\", s=100)\n", - "axes[0].set(xlabel=\"PERM_MULT\", ylabel=\"Final cumulative oil [million Sm3]\", title=\"Permeability response\")\n", + "axes[0].set(xlabel=\"PERM_MULT\", ylabel=\"Final produced water [Sm3/day]\", title=\"Permeability and water-front response\")\n", "scatter = axes[1].scatter(final_ensemble[\"PORO_MULT\"], final_ensemble[\"field_pressure_bara\"],\n", " c=final_ensemble[\"INJ_RATE\"], cmap=\"plasma\", s=100)\n", "axes[1].set(xlabel=\"PORO_MULT\", ylabel=\"Final pressure [bara]\", title=\"Porosity and injection response\")\n", @@ -5820,7 +5826,7 @@ }, { "cell_type": "markdown", - "id": "e1ad46e2", + "id": "e3433583", "metadata": {}, "source": [ "## 11. Transfer an ERT realization to NeqSim facilities\n", @@ -5835,13 +5841,13 @@ { "cell_type": "code", "execution_count": 35, - "id": "fb463548", + "id": "ecc194ce", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:30.543939Z", - "iopub.status.busy": "2026-08-31T17:05:30.543751Z", - "iopub.status.idle": "2026-08-31T17:05:30.562536Z", - "shell.execute_reply": "2026-08-31T17:05:30.561756Z" + "iopub.execute_input": "2026-08-31T17:15:04.996995Z", + "iopub.status.busy": "2026-08-31T17:15:04.996811Z", + "iopub.status.idle": "2026-08-31T17:15:05.015118Z", + "shell.execute_reply": "2026-08-31T17:15:05.014388Z" } }, "outputs": [ @@ -6319,13 +6325,13 @@ { "cell_type": "code", "execution_count": 36, - "id": "0c42e836", + "id": "9fd97b0f", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:30.564167Z", - "iopub.status.busy": "2026-08-31T17:05:30.563972Z", - "iopub.status.idle": "2026-08-31T17:05:30.576237Z", - "shell.execute_reply": "2026-08-31T17:05:30.574151Z" + "iopub.execute_input": "2026-08-31T17:15:05.016881Z", + "iopub.status.busy": "2026-08-31T17:15:05.016703Z", + "iopub.status.idle": "2026-08-31T17:15:05.031694Z", + "shell.execute_reply": "2026-08-31T17:15:05.030762Z" } }, "outputs": [], @@ -6422,13 +6428,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "fcd9a3cd", + "id": "69b3b0d6", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:30.579259Z", - "iopub.status.busy": "2026-08-31T17:05:30.578949Z", - "iopub.status.idle": "2026-08-31T17:05:31.304601Z", - "shell.execute_reply": "2026-08-31T17:05:31.302942Z" + "iopub.execute_input": "2026-08-31T17:15:05.033862Z", + "iopub.status.busy": "2026-08-31T17:15:05.033629Z", + "iopub.status.idle": "2026-08-31T17:15:05.974622Z", + "shell.execute_reply": "2026-08-31T17:15:05.973675Z" } }, "outputs": [], @@ -6492,13 +6498,13 @@ { "cell_type": "code", "execution_count": 38, - "id": "3798c345", + "id": "7c7f4acb", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:31.307526Z", - "iopub.status.busy": "2026-08-31T17:05:31.307080Z", - "iopub.status.idle": "2026-08-31T17:05:31.749746Z", - "shell.execute_reply": "2026-08-31T17:05:31.748852Z" + "iopub.execute_input": "2026-08-31T17:15:05.977880Z", + "iopub.status.busy": "2026-08-31T17:15:05.977655Z", + "iopub.status.idle": "2026-08-31T17:15:06.476797Z", + "shell.execute_reply": "2026-08-31T17:15:06.476013Z" } }, "outputs": [ @@ -6793,13 +6799,13 @@ { "cell_type": "code", "execution_count": 39, - "id": "7bf1d542", + "id": "02f9a241", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:31.752802Z", - "iopub.status.busy": "2026-08-31T17:05:31.752551Z", - "iopub.status.idle": "2026-08-31T17:05:32.228170Z", - "shell.execute_reply": "2026-08-31T17:05:32.227347Z" + "iopub.execute_input": "2026-08-31T17:15:06.479094Z", + "iopub.status.busy": "2026-08-31T17:15:06.478900Z", + "iopub.status.idle": "2026-08-31T17:15:06.657546Z", + "shell.execute_reply": "2026-08-31T17:15:06.656843Z" } }, "outputs": [ @@ -6884,7 +6890,7 @@ }, { "cell_type": "markdown", - "id": "a38889d0", + "id": "028d0adb", "metadata": {}, "source": [ "## 12. Complete input and artifact inventory\n", @@ -6898,13 +6904,13 @@ { "cell_type": "code", "execution_count": 40, - "id": "39cd2337", + "id": "c66a1127", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:32.230326Z", - "iopub.status.busy": "2026-08-31T17:05:32.230146Z", - "iopub.status.idle": "2026-08-31T17:05:32.259897Z", - "shell.execute_reply": "2026-08-31T17:05:32.259093Z" + "iopub.execute_input": "2026-08-31T17:15:06.659702Z", + "iopub.status.busy": "2026-08-31T17:15:06.659493Z", + "iopub.status.idle": "2026-08-31T17:15:06.689544Z", + "shell.execute_reply": "2026-08-31T17:15:06.688820Z" } }, "outputs": [ @@ -7174,7 +7180,7 @@ " 7\n", " ert.ensemble_experiment\n", " passed\n", - " {'return_code': 0, 'wall_time_seconds': 38.021...\n", + " {'return_code': 0, 'wall_time_seconds': 39.124...\n", " \n", " \n", "\n", @@ -7188,7 +7194,7 @@ "3 4 neqsim.generate_pvt passed {'source_commit': '1f7a01b06307d9d56451e43bb8d...\n", "4 5 opm.validate passed {'keywords': 17}\n", "5 6 opm.run passed {'return_code': 0, 'restart_steps': 25}\n", - "6 7 ert.ensemble_experiment passed {'return_code': 0, 'wall_time_seconds': 38.021..." + "6 7 ert.ensemble_experiment passed {'return_code': 0, 'wall_time_seconds': 39.124..." ] }, "metadata": {}, @@ -7220,7 +7226,7 @@ }, { "cell_type": "markdown", - "id": "76a1e692", + "id": "fdee69ca", "metadata": {}, "source": [ "## 13. Engineering validation gates\n", @@ -7234,13 +7240,13 @@ { "cell_type": "code", "execution_count": 41, - "id": "6e6fe7fb", + "id": "388f4944", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:32.261692Z", - "iopub.status.busy": "2026-08-31T17:05:32.261513Z", - "iopub.status.idle": "2026-08-31T17:05:32.277563Z", - "shell.execute_reply": "2026-08-31T17:05:32.276762Z" + "iopub.execute_input": "2026-08-31T17:15:06.691302Z", + "iopub.status.busy": "2026-08-31T17:15:06.691137Z", + "iopub.status.idle": "2026-08-31T17:15:06.706630Z", + "shell.execute_reply": "2026-08-31T17:15:06.705918Z" } }, "outputs": [ @@ -7490,13 +7496,13 @@ { "cell_type": "code", "execution_count": 42, - "id": "4a9dba2f", + "id": "14f6464c", "metadata": { "execution": { - "iopub.execute_input": "2026-08-31T17:05:32.279331Z", - "iopub.status.busy": "2026-08-31T17:05:32.279152Z", - "iopub.status.idle": "2026-08-31T17:05:32.290625Z", - "shell.execute_reply": "2026-08-31T17:05:32.289865Z" + "iopub.execute_input": "2026-08-31T17:15:06.708585Z", + "iopub.status.busy": "2026-08-31T17:15:06.708419Z", + "iopub.status.idle": "2026-08-31T17:15:06.720449Z", + "shell.execute_reply": "2026-08-31T17:15:06.719632Z" } }, "outputs": [ @@ -7553,18 +7559,30 @@ " \n", " \n", " 4\n", - " ERT low final oil\n", - " 7.305000\n", - " million Sm3\n", + " ERT minimum final pressure\n", + " 219.467545\n", + " bara\n", " \n", " \n", " 5\n", - " ERT high final oil\n", - " 7.305000\n", - " million Sm3\n", + " ERT maximum final pressure\n", + " 230.828323\n", + " bara\n", " \n", " \n", " 6\n", + " ERT minimum final water rate\n", + " 0.008757\n", + " Sm3/day\n", + " \n", + " \n", + " 7\n", + " ERT maximum final water rate\n", + " 0.010718\n", + " Sm3/day\n", + " \n", + " \n", + " 8\n", " Selected compressor power\n", " 2.680346\n", " MW\n", @@ -7574,14 +7592,16 @@ "" ], "text/plain": [ - " result value unit\n", - "0 Active cells 35838.000000 count\n", - "1 NeqSim bubble pressure 193.240400 bara\n", - "2 Base final pressure 223.618027 bara\n", - "3 Base cumulative oil 7.305000 million Sm3\n", - "4 ERT low final oil 7.305000 million Sm3\n", - "5 ERT high final oil 7.305000 million Sm3\n", - "6 Selected compressor power 2.680346 MW" + " result value unit\n", + "0 Active cells 35838.000000 count\n", + "1 NeqSim bubble pressure 193.240400 bara\n", + "2 Base final pressure 223.618027 bara\n", + "3 Base cumulative oil 7.305000 million Sm3\n", + "4 ERT minimum final pressure 219.467545 bara\n", + "5 ERT maximum final pressure 230.828323 bara\n", + "6 ERT minimum final water rate 0.008757 Sm3/day\n", + "7 ERT maximum final water rate 0.010718 Sm3/day\n", + "8 Selected compressor power 2.680346 MW" ] }, "metadata": {}, @@ -7594,7 +7614,7 @@ "{\n", " \"data_commit\": \"cad17f24e22c19c6cefe6f647185395cc0a11add\",\n", " \"neqsim_commit\": \"1f7a01b06307d9d56451e43bb8d6023d28d14007\",\n", - " \"neqsim_jar_sha256\": \"1e7d2c1c0678220db9b82385e7f614cf6e62aea367d6610c8035b707032bc739\",\n", + " \"neqsim_jar_sha256\": \"39e1de9738baaef18726c5d7a3f9cccf3a8af29a8e39761f90d92b870b0a857a\",\n", " \"grid_dimensions\": [\n", " 40,\n", " 64,\n", @@ -7615,6 +7635,14 @@ " 7.305,\n", " 7.305\n", " ],\n", + " \"ert_final_pressure_range_bara\": [\n", + " 219.46754455566406,\n", + " 230.8283233642578\n", + " ],\n", + " \"ert_final_water_rate_range_sm3_day\": [\n", + " 0.008757241070270538,\n", + " 0.010717831552028656\n", + " ],\n", " \"selected_process_realization\": 0,\n", " \"compressor_power_MW\": 2.680346000234267,\n", " \"process_mass_residual_kg_s\": 5.423572702056845e-11,\n", @@ -7656,6 +7684,14 @@ " float(final_ensemble[\"cumulative_oil_MSm3\"].min()),\n", " float(final_ensemble[\"cumulative_oil_MSm3\"].max()),\n", " ],\n", + " \"ert_final_pressure_range_bara\": [\n", + " float(final_ensemble[\"field_pressure_bara\"].min()),\n", + " float(final_ensemble[\"field_pressure_bara\"].max()),\n", + " ],\n", + " \"ert_final_water_rate_range_sm3_day\": [\n", + " float(final_ensemble[\"water_rate_Sm3_day\"].min()),\n", + " float(final_ensemble[\"water_rate_Sm3_day\"].max()),\n", + " ],\n", " \"selected_process_realization\": selected_realization,\n", " \"compressor_power_MW\": float(gas_compressor.getPower() / 1e6),\n", " \"process_mass_residual_kg_s\": float(process_mass_residual_kg_s),\n", @@ -7670,8 +7706,10 @@ " (\"NeqSim bubble pressure\", bubble_pressure_bara, \"bara\"),\n", " (\"Base final pressure\", final_results[\"base_final_pressure_bara\"], \"bara\"),\n", " (\"Base cumulative oil\", final_results[\"base_final_cumulative_oil_million_sm3\"], \"million Sm3\"),\n", - " (\"ERT low final oil\", final_results[\"ert_final_oil_range_million_sm3\"][0], \"million Sm3\"),\n", - " (\"ERT high final oil\", final_results[\"ert_final_oil_range_million_sm3\"][1], \"million Sm3\"),\n", + " (\"ERT minimum final pressure\", final_results[\"ert_final_pressure_range_bara\"][0], \"bara\"),\n", + " (\"ERT maximum final pressure\", final_results[\"ert_final_pressure_range_bara\"][1], \"bara\"),\n", + " (\"ERT minimum final water rate\", final_results[\"ert_final_water_rate_range_sm3_day\"][0], \"Sm3/day\"),\n", + " (\"ERT maximum final water rate\", final_results[\"ert_final_water_rate_range_sm3_day\"][1], \"Sm3/day\"),\n", " (\"Selected compressor power\", final_results[\"compressor_power_MW\"], \"MW\"),\n", "], columns=[\"result\", \"value\", \"unit\"]).round(6))\n", "print(json.dumps(final_results, indent=2))" @@ -7679,7 +7717,7 @@ }, { "cell_type": "markdown", - "id": "ea632faf", + "id": "f476c9ff", "metadata": {}, "source": [ "## What is demonstrated, and what is not\n", @@ -7713,7 +7751,7 @@ }, { "cell_type": "markdown", - "id": "a3bc19db", + "id": "c12153bc", "metadata": {}, "source": [ "## Suggested exercises\n", From 4e595c315ac40ff65e957185fbf2c7820efd5b92 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 19:16:59 +0200 Subject: [PATCH 17/19] Record visual validation for RMS notebook --- .../maintenance_ledger/rms_opm_ert_agent_20260831.json | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json b/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json index 9f8646b7..a8eda130 100644 --- a/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json +++ b/notebooks/maintenance_ledger/rms_opm_ert_agent_20260831.json @@ -1,7 +1,7 @@ { "schema_version": 1, "shard": "rms-opm-ert-agent-20260831", - "updated_at": "2026-08-31T17:15:07Z", + "updated_at": "2026-08-31T17:17:00Z", "notebooks": [ { "path": "notebooks/reservoir/rms_to_opm_flow_agent_ert.ipynb", @@ -107,9 +107,11 @@ "validation_checks_total": 26 }, "rendered_visual_validation": { - "renderer": "nbconvert HTML plus retained Matplotlib PNG outputs", - "figures_retained": 13, - "result": "pending human visual inspection before draft PR" + "renderer": "nbconvert 7.16.6 HTML with MathJax source and 13 retained Matplotlib PNG outputs", + "display_equations_inspected": 2, + "figures_inspected": 13, + "result": "passed", + "notes": "All retained figures were inspected at original resolution for titles, units, legends, color scales, clipping, overlap, physical sign conventions, and agreement with stored numerical tables. Visual QA replaced a misleading offset oil-rate ensemble panel with injection, pressure, and water-front responses and made the zero gas-saturation scale explicitly non-negative. Display equations use Colab-safe dollar delimiters and passed the notebook checker." }, "known_upstream_issue": null, "issue_handling": "No upstream defect is claimed; limitations distinguish teaching assumptions from decision-grade RMS studies." From 5dbc839cd02c7a0c53ffdd196bcb51d6a6dd88bb Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 19:18:16 +0200 Subject: [PATCH 18/19] Minimize catalog update for RMS notebook --- notebooks/examples_of_NeqSim_in_Colab.ipynb | 1168 +++++++++---------- 1 file changed, 584 insertions(+), 584 deletions(-) diff --git a/notebooks/examples_of_NeqSim_in_Colab.ipynb b/notebooks/examples_of_NeqSim_in_Colab.ipynb index 75f36cbb..9863eff3 100644 --- a/notebooks/examples_of_NeqSim_in_Colab.ipynb +++ b/notebooks/examples_of_NeqSim_in_Colab.ipynb @@ -1,587 +1,587 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_eRtkQnHpL70", + "language": "markdown" + }, + "source": [ + "# Oil and Gas Value Chain with NeqSim and Python" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kHt6u-utpvYf", + "language": "markdown" + }, + "source": [ + "[NeqSim (Non-Equilibrium Simulator)](https://equinor.github.io/neqsimhome/) is a Java\n", + "library for thermodynamic properties, PVT, flow, and process simulation. Google Colaboratory\n", + "(Colab) is a free Jupyter notebook environment that runs in a web browser. This collection uses\n", + "Python tools together with NeqSim Java to explain the complete oil and gas value chain: access and\n", + "exploration, fluid characterization, reservoirs, wells, subsea and SURF, facilities, products and\n", + "markets, operations, emissions, late life, and decommissioning.\n", + "\n", + "The notebooks serve both as teaching material and as transparent engineering-analysis examples.\n", + "Select **Runtime → Run all** in Colab to reproduce an executed notebook. Detailed examples identify\n", + "where NeqSim is the primary calculation engine, where it supplies thermodynamic or process\n", + "boundaries, and where specialist Python or external tools remain necessary.\n", + "\n", + "---\n", + "\n", + "***Learn to use NeqSim in Colab and contribute new material***\n", + "\n", + "Users are welcome to contribute NeqSim Colab pages. The\n", + "[validated contributor template](template.ipynb) demonstrates SRK fluid setup, ISO 6976 gas\n", + "quality, streams, mixing, compression, cooling, scenario analysis, assertions, and retained\n", + "figures. A practical introduction is available in [How to use NeqSim](howtouseneqsim.ipynb).\n", + "All notebooks are maintained in the open\n", + "[NeqSim-Colab GitHub repository](https://github.com/EvenSol/NeqSim-Colab).\n", + "\n", + "---\n", + "\n", + "**Comments and requests for new content**\n", + "\n", + "Use the [discussion forum](https://github.com/EvenSol/NeqSim-Colab/discussions) to discuss the\n", + "material. Request new content or suggest improvements by\n", + "[reporting an issue](https://github.com/EvenSol/NeqSim-Colab/issues).\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "language": "markdown", + "id": "TuNcSKktRaLf" + }, + "source": [ + "# Suggested Learning Path\n", + "\n", + "Use the catalog below as a reference library, or follow one of these short paths:\n", + "\n", + "* **Complete value chain:** begin with the orientation notebook at the start of the table of\n", + " contents, then follow its dependency-ordered path from PVT and reservoirs through retirement.\n", + "* **Field development:** exploration and PVT -> reservoir forecast -> wells and SURF -> facilities\n", + " and economics.\n", + "* **Asset lifecycle:** commissioning and start-up -> stable operation -> integrity and optimization\n", + " -> cessation and decommissioning.\n", + "\n", + "* **Beginner:** create a fluid -> read properties -> run a TP flash -> plot a phase envelope.\n", + "* **Process simulation:** separator -> compressor -> heat exchanger -> complete process model.\n", + "* **Engineering studies:** flow assurance -> process safety -> emissions -> standards checks.\n", + "* **Advanced workflows:** dynamic simulation -> digital twins -> process automation -> AI-assisted workflows.\n", + "\n", + "**Level guide:** Beginner notebooks introduce one concept at a time. Intermediate notebooks combine several NeqSim calculations. Advanced notebooks use automation, optimization, or workflow-style result objects.\n", + "\n", + "## New Capability Demonstration Notebooks\n", + "\n", + "* **Beginner** - [Modern natural-gas fluid properties with NeqSim](thermodynamics/modern_fluid_property_workflow.ipynb): create a gas fluid, run a TP flash, read properties, and generate a property table.\n", + "* **Intermediate** - [Process automation API for discoverable and safe NeqSim workflows](process/process_automation_api_demo.ipynb): build a gas conditioning process, discover unit-aware addresses, use safe and batch operations, evaluate setpoints, inspect utilization, and apply multi-area automation.\n", + "* **Advanced** - [Closed-loop process optimization](process/closed_loop_process_optimization.ipynb): sweep process setpoints and minimize compressor power.\n", + "* **Advanced** - [External nonlinear process optimization with SciPy and CasADi/IPOPT](process/external_nonlinear_process_optimization.ipynb): connect NeqSim ProcessSimulationEvaluator to scaled SLSQP and IPOPT workflows with native margins, infeasibility restoration, Pareto fronts, shadow-price checks, and discrete brownfield upgrade packages.\n", + "* **Advanced** - [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; share speed, power, map, and custom capacity constraints across typed operating-point results, bottleneck analysis, and pressure-boundary optimization; screen driver upgrades and declining inlet-pressure strategies.\n", + "* **Intermediate** - [Capacity and bottleneck analysis](process/capacity_and_bottleneck_analysis.ipynb): use utilization snapshots to identify process constraints.\n", + "* **Intermediate** - [Digital twin model vs measurement](process/digital_twin_model_vs_measurement.ipynb): build, calibrate, validate, and monitor a compressor digital twin with synthetic plant measurements.\n", + "* **Intermediate** - [Hydrate, wax, and water margin screening](flowassurance/hydrate_wax_and_water_margin_screening.ipynb): screen operating margins before detailed flow-assurance analysis.\n", + "* **Intermediate** - [Gas turbine emissions and process power](power/gas_turbine_emissions_and_process_power.ipynb): preserve the original compressor/emissions screen, then use current `GasTurbineCatalog`, `GasTurbineUnit`, ambient, degradation, and native compressor-power-consumer APIs.\n", + "* **Intermediate** - [Standards-based gas-line and relief screening with NeqSim](standards/standards_based_engineering_checks.ipynb): calculate fluid properties, line velocity, compressible pressure profiles, independent Darcy checks, preliminary choked-gas relief area, and a reusable screening workflow without claiming design-code compliance.\n", + "* **Advanced** - [Agentic process simulation with NeqSim](AI/agentic_neqsim_workflow_demo.ipynb): discover and safely address process variables, evaluate guarded operating cases, audit balances, map an operating envelope, and optimize a constrained gas-conditioning workflow with the native ProcessAutomation API." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9VqtmS_MpS6M", + "language": "markdown" + }, + "source": [ + "# Table of Contents\n", + "\n", + "## Complete oil and gas value chain\n", + "* [The complete oil and gas value chain: governed models, handoffs, and decisions](valuechain/complete_oil_and_gas_value_chain_with_neqsim.ipynb): connect eleven lifecycle stages through governed model identities and coherent realizations; keep laboratory data and specialist PVT software primary while public-PyPI NeqSim supports PVT and powers connected separation, compression, cooling, SURF, ISO 6976, emissions, deterministic and probabilistic economics, RAM, and explicit decision gates.\n", + "* **Advanced** - [Geo to market on the Norwegian Continental Shelf](valuechain/norne_geo_to_market_full_workflow.ipynb): carry pinned published Norne data through static-property QC and upscaling, a transparent reservoir forecast, a 60-realization FMU prior, four ES-MDA updates and an ERT contract; preserve realization identity through well and SURF hydraulics, hydrate, cooldown, wax, erosion and slug screens, a real NeqSim separation and export-compression process, ISO 6976 gas quality, facility feedback, emissions, market uncertainty, NPV and sensitivity analysis.\n", + "* **Advanced** - [Early-phase subsurface-to-flow-assurance data handoff](valuechain/early_phase_subsurface_to_flow_assurance_handoff.ipynb): normalize ECLIPSE/OPM summary data into a unit-explicit contract, preserve coherent realization identities, select representative uncertainty cases, run NeqSim well and TwoFluidPipe screens, and export traceable choke, MPFM and topside handoffs for automated discipline and reporting agents.\n", + "\n", + "## Fundamentals of NeqSim\n", + "* [Create and validate a NeqSim fluid from Eclipse PVT input](PVT/readEclipseFormat.ipynb): import a self-contained seven-lump SRK deck, audit properties and phase/component closure, test pressure and temperature sensitivities, verify XML round-trip identity, and connect the fluid to a high-pressure separator.\n", + "* [Thermodynamic and physical properties with NeqSim](thermodynamics/readproperties.ipynb): phase-aware equilibrium and transport properties, state sensitivities, validation, and a connected separation-and-compression application.\n", + "* [Traceable NeqSim parameter database and model audit](PVT/parameter_database.ipynb): inspect packaged component and binary-interaction data with read-only SQL, reconcile live SRK parameters, screen local interaction-parameter and pressure sensitivities, and connect the model to a compressor.\n", + "* [Controlled custom parameters and local model overlays](PVT/parameter_database2.ipynb): validated component and binary-interaction overrides without global database mutation.\n", + "* [Compare to experimental data and parameter fitting](https://github.com/equinor/neqsim-parameterfitting)\n", + "* [ThermoML model accuracy and parameter tuning with NeqSim](thermodynamics/thermoml_model_accuracy_and_parameter_tuning.ipynb): parse DOI-specific density and VLE records with ThermoMLPy, audit property provenance, test held-out model accuracy, and tune a Péneloux volume correction and PR binary interaction parameter.\n", + "\n", + "## Natural Gas Statistics\n", + "* [How natural gas is sold to Europe](gasvaluechain/european_gas_sales_market_foundation.ipynb): connect consumer procurement, shipper and TSO transport, producer netback, ISO 6976 energy settlement, NeqSim export compression, capacity dispatch, nominations, imbalance, hedging, and auditable cash closure.\n", + "* [Natural gas and the World Energy Outlook: evidence, scenarios, LNG supply, and NeqSim](gasvaluechain/energystatistics.ipynb): connect WEO 2025 and current IEA evidence to reproducible scenario stress tests, ISO 6976 gas quality, multistage compression, balances, sensitivities, and lifecycle boundaries.\n", + "* [Use of natural gas (history, present and future)](gasvaluechain/useOfNaturalGas.ipynb)\n", + "* [Natural gas in the energy transition: evidence, scenarios, and NeqSim (2025–2050)](gasvaluechain/EneregyTransition.ipynb): connect current IEA evidence to ISO 6976 fuel quality, a three-stage compression process, methane intensity, carbon capture, and lifecycle sensitivities.\n", + "\n", + "\n", + "## Thermodynamics\n", + "* [The laws of thermodynamics](thermodynamics/LawsOfThermodynamics.ipynb): verify equilibrium, state functions, energy balances, entropy generation, reversible compression, exergy, and limiting cases.\n", + "* [CO₂ low-temperature compression, relief, and depressurization](thermodynamics/CO2_low_temperature_compression_relief_and_depressurization.ipynb): compare EOS-CG, GERG-2008, PR, and SRK with Span–Wagner; map impurity-sensitive phase behaviour; simulate staged compression; and screen isenthalpic relief, HEM critical flow, and transient blowdown temperatures.\n", + "* [Energy balance for closed and open systems](thermodynamics/EnergyBalance.ipynb)\n", + "* [Equations of State](thermodynamics/EquationsOfState.ipynb)\n", + "* [From fundamental interactions to SAFT-VR Mie parameters and NeqSim](thermodynamics/saft_vr_mie_from_fundamental_models.ipynb): execute PySCF dimer calculations, SciPy mapping, FEASST Mie Monte Carlo, SGTPy association and binary phase equilibrium, teqp validation, NeqSim parameter injection, uncertainty propagation, and methane compression.\n", + "* [From molecular hydrogen bonds to SAFT-VR Mie association and NeqSim](thermodynamics/saft_vr_mie_association_from_fundamental_models.ipynb): derive water 4C site topology, hydrogen-bond energy, and NeqSim kernel volume from counterpoise-corrected molecular calculations; benchmark Dufal association with teqp; verify NeqSim mass action and Helmholtz identities; and propagate the mapped parameters to water properties and heater duty.\n", + "* [Phase equilibrium](thermodynamics/PhaseEquilibrium.ipynb)\n", + "* [Flash Calculations and Rachford-Rice](thermodynamics/RachRice.ipynb)\n", + "* [Advanced TPflash algorithms](thermodynamics/c1_co2_h2s_flash_test.ipynb)\n", + "* [Thermodynamic property charts and connected process paths](thermodynamics/ThermoPropertyCharts.ipynb)\n", + "* [Physical porperty charts](thermodynamics/physiclaPropertyChart.ipynb)\n", + "* [Thermodynamc Cycles](thermodynamics/ThermodynamicCycles.ipynb)\n", + "* [Exergy analysis](thermodynamics/ExergyAnalysis.ipynb)\n", + "* [Chemical Equilibrium](thermodynamics/ChemicalEquilibrium.ipynb)\n", + "* [Water-ammonia thermodynamics and generator screening](thermodynamics/water_ammonia_properties.ipynb): calculate pure-fluid references, binary equilibrium, composition and pressure sensitivities, EOS uncertainty, and a connected heater-separator generator workflow with recovery, carryover, and closure checks.\n", + "\n", + "## Fluid mechanics\n", + "* [Fluid mechanics](fluidflow/FluidMechanics.ipynb)\n", + "* [Natural-gas pipeline linepack and operational flexibility](fluidflow/natural_gas_pipeline_linepack.ipynb): combine NeqSim real-gas properties and native PipeBeggsAndBrills pressure profiles with inventory integration, pack/unpack transients, deliverability limits, and operating-margin checks.\n", + "* **Advanced** - [Åsgard Transport open route data to terrain-following NeqSim](fluidflow/asgard_transport_open_data_to_neqsim.ipynb): retrieve or replay public SODIR and EMODnet data, retain raw and 720 km engineering KP, create normalized five-point cross-sections and a transparent synthetic C5P candidate, and run a source-built segmented pressure/temperature model with flow sensitivity and refinement checks.\n", + "* **Advanced** - [Dynamic CO₂ and flow tracing from Åsgard and Kristin to Kårstø](fluidflow/asgard_transport_dynamic_co2_tracing.ipynb): build NeqSim Java from an exact `master` commit, mix two gas sources, solve the inlet pressure required for a fixed Kårstø boundary, compare first-order and TVD component transport with an explicit physical-dispersion sensitivity, refine grid/time resolution, and finish with a component-resolved gas/oil `TwoFluidPipe` conservation gate.\n", + "* [Norwegian NCS rich/dry gas network optimization](process/norwegian_ncs_gas_network_optimization.ipynb) — Gassco 2026 point-specific quality, NeqSim phase envelopes and ISO 6976, Beggs–Brill capacity, onshore NGL recovery, a looped gas-network solve, reported export-rate validation, and future tie-in value-chain optimization.\n", + "* [NeqSim + OpenFOAM CFD with inline flow graphics](fluidflow/neqsim_openfoam_cfd.ipynb)\n", + "* [Tonal valve and piping noise: evidence-gated PEPR workflow](fluidflow/tonal_aeroacoustic_root_cause_gate.ipynb): combine a synthetic NeqSim gas letdown, valve-noise and vibration screens, a transient compressible finite-volume CFD/CAA verification, spectra, geometry and modal-data requirements, multi-hypothesis evidence synthesis, and an executable stop gate that prevents a tonal root-cause claim when installed evidence is missing.\n", + "* [Parametric CAD-to-CFD workflow with NeqSim, CadQuery, Gmsh, and OpenFOAM](fluidflow/neqsim_cadquery_gmsh_openfoam_workflow.ipynb): generate an exact STEP internal fluid volume and named STL boundaries, mesh CadQuery geometry with Gmsh physical groups, transfer NeqSim gas properties and flow into a three-dimensional OpenFOAM RANS case, render the actual mesh and CFD fields inline, validate geometry identity, mesh quality, convergence, and flow closure, and package reusable CAD, mesh, and case artifacts.\n", + "* [P&ID and mechanical datasheet to CAD and CFD with NeqSim](fluidflow/pid_datasheet_to_cad_cfd_neqsim.ipynb): normalize reviewed equipment, stream, nozzle, and instrument tags; verify dimensions with a calibrated-image QA record; calculate a three-phase CPA inlet with NeqSim; generate an exact separator gas-space STEP model with CadQuery; retain inlet, outlet, walls, and liquid-interface groups through Gmsh; run a real OpenFOAM RANS hydraulic screen; render the solved fields; and package the traceable design basis, CAD, mesh, case, and results.\n", + "* [Wet-gas centrifugal-compressor inlet with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_compressor_inlet_wet_gas.ipynb): generate a 3D suction CAD, solve the carrier gas, and screen liquid impaction and hot-wall evaporation.\n", + "* [Liquid-valve bubble formation with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_flashing_valve.ipynb): screen single-phase flow, local cavitation with pressure recovery, and sustained flashing.\n", + "* [Finite-rate pipeline evaporation and gas dissolution](fluidflow/pipeline_evaporation_and_gas_dissolution.ipynb): calculate droplet and film evaporation, gas dissolution into oil and water, heat and Maxwell–Stefan mass transfer, slip, completion length, and incomplete phase transfer.\n", + "* [Single phase pipe flow](fluidflow/singlephaseflow.ipynb)\n", + "* [Multi phase pipe flow](fluidflow/multiphaseflow.ipynb)\n", + "* [Minimum-flow analysis for a long oil–gas–water flowline](fluidflow/minimum_flow_long_multiphase_flowline.ipynb): use the native `TwoFluidPipe` to locate terrain liquid accumulation, screen a multi-criterion minimum rate, test low-flow inventory growth and restart recovery, and quantify mesh sensitivity.\n", + "* **Advanced** - [Three-phase wellstream shutdown cooldown and executable OpenFOAM dead-leg screening](flowassurance/wellstream_shutdown_cooldown_to_openfoam_deadleg.ipynb): close a terrain-following gas–oil–water `TwoFluidPipe`, calculate axial no-touch time to an SRK-CPA hydrate-management boundary, then run an OpenFOAM 14 tee/dead-leg phase-settling and conjugate-cooldown screen with mesh, time-step, phase-volume, energy, cold-spot, hydrate-risk-volume, and heat-loss-feedback checks.\n", + "* [Flow induced vibrations (FIV)](fluidflow/FIVcalc.ipynb)\n", + "\n", + "## Heat and mass transfer\n", + "* [Non-equilibrium thermodynamics](thermodynamics/Nonequilibriumthermodynamics.ipynb)\n", + "* [Heat transfer](thermodynamics/heatTransfer.ipynb)\n", + "* [Mass transfer](thermodynamics/massTransfer.ipynb)\n", + "\n", + "## Thermodynamics of gas processing\n", + "* [PVT/density of gases](thermodynamics/density_of_gas.ipynb)\n", + "* [Phase envelopes of oil and gas](thermodynamics/Phase_envelopes_of_oil_and_gas.ipynb)\n", + "* [Water dew point calculation](thermodynamics/water_dew_point_claculations.ipynb)\n", + "* [Solubility of gases in water](thermodynamics/solubility_of_gases_in_water.ipynb)\n", + "* [Freezing point in LNG](thermodynamics/freezing_in_LNG.ipynb)\n", + "* [Phase behaviour of CO2](thermodynamics/PhaseBEhaviourCO2.ipynb)\n", + "* [Mercury in natural gas](thermodynamics/mercury_in_gas.ipynb)\n", + "* [H2S distribution in oil and gas processing](thermodynamics/H2Sdistribution.ipynb)\n", + "* [Simulation of fluids with water](thermodynamics/flash_with_salt_water.ipynb)\n", + "\n", + "\n", + "## Thermodynamic and Physical Properties\n", + "* [Thermodynamic properties](howtouseneqsim.ipynb)\n", + "* [Viscosty of fluids](thermodynamics/ViscosityOfFluids.ipynb)\n", + "* [Thermal conductivity of fluids](thermodynamics/ThermalConductivityOfFluids.ipynb)\n", + "* [Interfacial tension](thermodynamics/interfacialtension.ipynb)\n", + "* [Interface adsorption](thermodynamics/Interfacialadsorption.ipynb)\n", + "* [Diffusion coefficient](thermodynamics/diffusioncoefficients.ipynb)\n", + "\n", + "## Characterization of reservoir fluids\n", + "* [PVT of reservoir fluids](PVT/OilProperties.ipynb)\n", + "* [Characterization of a well fluid](PVT/fluidcharacterization.ipynb)\n", + "* [PVT experiments](PVT/PVTexperiments.ipynb)\n", + "* [Auditable PVT laboratory reports and fluid characterization](PVT/PVTreports.ipynb)\n", + "* [Complete PVT workflow: laboratory data, regression, separator optimization, and simulator export](PVT/pvt_workflow_from_lab_to_simulator.ipynb): characterize a reservoir oil, validate CCE and DLE, tune viscosity, optimize staged separation, and export reports, black-oil, and E300 models.\n", + "* [Black oil vs. computational simulation](PVT/blackoilvscomp.ipynb)\n", + "* [Eclipse-style PVT input and depletion-aware fluid recombination](PVT/eclipseFluidCharNeqSim.ipynb)\n", + "* [Characterization and plus fraction disitribution](PVT/GammaModel.ipynb)\n", + "* [Oil-assay cuts, pseudo-components, and fluid characterization](PVT/oilassay.ipynb)\n", + "\n", + "## Exploration and licence access\n", + "* [From opening an NCS area to an exploration discovery](fielddevelopment/ncs_area_opening_licensing_exploration_to_discovery.ipynb): follow the Norwegian opening and licensing framework through pre-qualification, licence groups and work obligations; screen synthetic public and MCS-style map layers; quantify play and prospect chance, probabilistic volumes and drilling value; characterize a hypothetical discovery sample in NeqSim; and hand an auditable discovery package to the reservoir-to-OPM Flow workflow.\n", + "* [NCS spatial history, discoveries, and future potential](fielddevelopment/ncs_spatial_history_discoveries_and_future_potential.ipynb): retrieve live SODIR discoveries, fields, wildcats, licences, plays, facilities, pipelines, and resource tables; reconstruct the staged northward development of the shelf; compare sea-area learning and opportunity archetypes; rank undeveloped discoveries with explicit limitations; and screen selected gas tie-backs with a source-built NeqSim Java master.\n", + "\n", + "## Reservoir simulations\n", + "* [Open SPE9 subsurface data with XTGeo, XTGeoViz, and NeqSim](reservoir/xtgeo_spe9_subsurface_to_neqsim.ipynb): load checksum-pinned synthetic SPE9 grid and restart properties with XTGeo; audit field-to-SI units; render maps and spaced candidate columns with XTGeoViz; parse the public PVTO deck; quantify STOIIP uncertainty; and pass rate scenarios into a composable NeqSim separation, cooling, and compression model with explicit conservation and capacity checks.\n", + "* [Introduction to reservoir simulations](reservoir/reservoirsimulation.ipynb)\n", + "* [Rock and reservoir flow from pore space to field development](reservoir/rock_flow_pore_to_field_neqsim.ipynb): use PoreSpy, OpenPNM, GSTools, SciPy, and current NeqSim Java master to connect digital rock, capillary invasion, pore-network permeability, SCAL, heterogeneous finite-volume waterflooding, well productivity, injection and water-handling constraints, surface separation, and OPM Flow handoffs.\n", + "* [A simplified reservoir simulation model](reservoir/simplereservoir.ipynb)\n", + "* [Composition gradient in a gas reservoir](reservoir/compositiongrad.ipynb)\n", + "* [NeqSim black-oil tables, OPM Flow reservoir simulation, and characterized process feeds](reservoir/neqsim_opm_flow_blackoil_coupling.ipynb): characterize a C20+ reservoir oil, generate Flow-compatible PVTO/PVDG/PVTW tables, run a 10 × 10 × 3 depletion and water-injection case, visualize the active-cell mesh and restart pressure/saturation states, reconstruct time-varying NeqSim well streams, and connect the maximum-load case to separation, compression, cooling, and oil letdown.\n", + "* [From seismic, samples, and petrophysics to an OPM Flow production forecast](reservoir/subsurface_data_to_opm_flow_production_forecast.ipynb): generate synthetic SEG-Y, LAS, RCAL/SCAL, pressure, and fluid-sample evidence; interpret petrophysics; build and upscale a seismic-guided geological model; generate all NeqSim PVT, grid, saturation-function, well, control, and schedule inputs; run low/base/high OPM Flow cases; and validate files, volumes, pressure, rates, and forecast results.\n", + "* **Advanced** - [FMU on the Norwegian Continental Shelf: Norne data to reservoir simulation, ERT and NeqSim](reservoir/fmu_norne_subsurface_to_facilities_workflow.ipynb): download a pinned public Norne model revision; parse and quality-control grid, petrophysical and well data; upscale to a transparent two-phase finite-volume reservoir model; run a base forecast; update uncertain reservoir parameters with ES-MDA and an ERT-ready case; propagate Q10/Q50/Q90 rates through a real NeqSim separation and compression process; apply facility constraints; and close the feedback loop to the reservoir controls.\n", + "* [ERT + NeqSim integrated field-development uncertainty](reservoir/ert_neqsim_integrated_field_development.ipynb): run 24 coupled reservoir, well, SURF, NeqSim process, and economic realizations with ERT; quantify P10/P50/P90 value, Q10/Q50/Q90 facility loads, capacity-exceedance probabilities, and dominant uncertainty drivers; and select internally consistent representative cases.\n", + "* [Stochastic reservoir-to-market optimization](reservoir/stochastic_reservoir_to_market_optimization.ipynb): preserve twelve internally consistent ERT, OPM Flow, NeqSim Java master, process, market, and economic realizations while optimizing well chokes, compressor configuration, host holdback, and common upgrade timing with Pyomo/HiGHS; rank complete P90/P50/P10 outcomes and quantify VSS and EVPI.\n", + "* [Discovery 1 NCS tie-in decision with OPM Flow and NeqSim master](reservoir/ncs_discovery_1_tie_in_opm_neqsim_master.ipynb): a guided neutral-labelled, composite open-data teaching case with learning objectives, 52 equations, interpretation prompts, 12 exercises, and a glossary. It uses public SODIR/FactMaps information, a public E300 analogue, OPM Flow, and NeqSim to explain reservoir-to-host coupling, holdback, utilization, and uncertainty. It contains no internal project data and is not a basis for real development decisions.\n", + "\n", + "## Wells\n", + "* [Introductin to oil and gas wells](well/wellcalcs.ipynb)\n", + "* [Rock-derived well productivity and injectivity with OPM Flow and NeqSim](well/rock_derived_productivity_injectivity_opm_neqsim.ipynb): derive permeability-thickness and skin from well-test evidence, generate SCAL with pyscal, run OPM Flow producer, injector, and bubble-point depletion cases, extract guarded pressure-to-rate curves with resdata, check voidage and fracture margin, and hand the inflow relationship to NeqSim wellbore and separation models.\n", + "* [Well nodal analysis and ECLIPSE VFPPROD lift curves](well/well_nodal_analysis_and_eclipse_vfp.ipynb): calculate gas-well IPR and VLP curves with NeqSim, solve and independently verify their operating-point crossing, screen tubing-head pressure, tubing size, and reservoir depletion, generate a rate–THP lift-curve family, and export a validated five-dimensional VFPPROD include.\n", + "* [Integrated wells and production technology](well/integrated_wells_drilling_completion_lift_injection_intervention.ipynb): connect minimum-curvature drilling geometry, NeqSim well construction and cost, completion sensitivity, artificial-lift screening, water injection, well-integrity evidence, and acid intervention as one governed lifecycle.\n", + "\n", + "## Production technology\n", + "* [Production technology with NeqSim](production/production_technology.ipynb)\n", + "* [Well chemistry and water management with NeqSim](production/well_chemistry_and_water_management.ipynb)\n", + "* [Produced-water, sand, and production-chemicals management](production/produced_water_sand_chemicals_management.ipynb): connect three-phase separation, water quality, hydrocyclone/deoiling, discharge constraints, scale, corrosion, hydrate and wax inhibitor boundaries, MEG/TEG recovery, sand erosion, chemical consumption, and emissions.\n", + "\n", + "## Subsea facilities\n", + "* [Subsea production equipment and system screening with NeqSim](subseaequipment/subseaequipment.ipynb)\n", + "* [Simulation of subsea processes with NeqSim](subseaequipment/subsea_process_simulation.ipynb)\n", + "* [Multiphase flow of reservoir fluids with NeqSim](subseaequipment/multiphase_reservoir_fluid_flow.ipynb)\n", + "\n", + "## Unit Operations\n", + "* [Three-phase production separators with NeqSim](process/Separators.ipynb)\n", + "* [Heat Exchangers](process/heatexchangerDescription.ipynb)\n", + "* [Compressors and expanders](process/GasCompressors.ipynb)\n", + "* [Turboexpander-compressors](process/TurboExpanderCompressor_Example.ipynb)\n", + "* [Compressor curves and operating-envelope calculations](process/compressor_curves_and_compressor_calculations.ipynb)\n", + "* [Pumps](process/pumps.ipynb)\n", + "* [Valves](process/valves.ipynb)\n", + "* [Manifolds and pipes](process/manifoldsandpipes.ipynb)\n", + "* [Absoprtion](process/absorption.ipynb)\n", + "* [Adsorption](process/adsorption.ipynb)\n", + "* [Distillation](process/distillationoilgas.ipynb)\n", + "* [Mass transfer unit operations](process/masstransferMeOH.ipynb)\n", + "\n", + "## Process simulation\n", + "* [Reservoir-fluid tuning to a reference-process GOR with NeqSim](process/reservoirGORprocesstuning.ipynb): tune a bounded gas endmember against a four-stage separation train, close mass and energy balances, and quantify target and process-definition sensitivities.\n", + "* [Multistage oil stabilization with NeqSim](process/Simulationofanoilstabilizationprocess.ipynb): characterize a synthetic reservoir fluid, solve three equilibrium flash stages, close total/component/energy balances, screen separator capacity, and quantify volatility–recovery trade-offs.\n", + "* [Simulation of a TEG dehydration process](process/simulationTEG.ipynb)\n", + "* [Reporting simulation results and report using field/SI units](process/processreportsandunits.ipynb)\n", + "* [Process simulation using neqsim](process/comparesimulations.ipynb)\n", + "* [Comparsion of process simulation using neqsim, UNISIM and DWSIM](process/comparesimulations2.ipynb)\n", + "* [Integrated oil stabilization, gas recompression, TEX, and NGL stabilization](process/oil_and_gas_Process_with_ngl_stabilizer_and_tex_process.ipynb): build a synthetic rich-fluid case through four-stage stabilization, three-stage flash-gas recompression, hydrocarbon-dew-point cooling, turboexpansion, a MESH-residual NGL stabilizer, product-quality checks, balances, and independent operating scenarios.\n", + "* [MEG regeneration and reclamation](process/MEGprocess.ipynb)\n", + "* [Development of large process models - use of sub-models](process/demo_field_process_model.ipynb)\n", + "\n", + "## Dynamic process simulations\n", + "* [Simulations of dynamic operation of a separator](process/dynamicsimul.ipynb)\n", + "* [Dynamic compressor calculations: speed response, pressure control, anti-surge, combined control, and startup](process/dynamiccompressor.ipynb): executable five-scenario workbook using current NeqSim thermodynamics, explicit two-volume mass balances, bounded PI and recycle controls, and engineering audits.\n", + "* [Dynamic compressor discharge-volume transient and PI flow control with NeqSim](process/dynamic_compressor_discharge_volume_control.ipynb): current complementary real-gas transient and controller example.\n", + "* [Dynamic compressor maps and anti-surge control](process/dynamiccompressor_dyn.ipynb): generate a five-speed chart with surge and stone-wall boundaries, compare native `AntiSurge` strategies, run a closed-loop turndown and recovery study, and preserve the five original connected-process transient workflows.\n", + "* [Integrated dynamic SURF–topside–control simulation](process/integrated_dynamic_surf_topside_control.ipynb): couple the corrected native `TwoFluidPipe` transient outlet process stream, terrain accumulation, and closed-ledger Lagrangian slug tracking to a dynamic receiving separator, compressor map, anti-surge recycle, stonewall arbitration, filtered pressure control, and fail-closed continuity and multiphase-flux audits using NeqSim Java built from `master`.\n", + "* [Custom external unit operation in dynamic simulation](process/dynamic_external_unit_operation.ipynb): implement a Python heater with thermal inertia, integrate it with NeqSim streams and equipment, and validate its energy balance and analytical response.\n", + "* [Pure component operations](process/singlecomponent.ipynb)\n", + "* [Pure component dynamic operations](process/singlecomponent_dyn.ipynb)\n", + "* [Dynamic control of an oil and gas separator](process/dynsep.ipynb)\n", + "* [Dynamic process simulation using reinforcement learning](process/RL_Process_Control.ipynb)\n", + "\n", + "## Gas processing design\n", + "* [Design of a gas-liquid separator](process/gas_oil_separation.ipynb)\n", + "* [Design of a TEG-dehydration process](process/TEGdehydration.ipynb)\n", + "* [Design of a shell and tube heat exchanger](process/heatexchanger.ipynb)\n", + "* [Offshore topside gas-processing train screening with NeqSim](process/topsideprocess.ipynb): cool and separate a synthetic well fluid, stabilize liquid, compress and cool export gas, close balances, and screen equipment capacity and operating sensitivities.\n", + "* [NGL extraction and fractionation with NeqSim](process/NGLextractionprocess.ipynb): model a characterized feed through cooling, expansion, scrubbing, heat recovery, MESH-residual fractionation, export compression, balance checks, product-quality calculations, and operating sensitivity.\n", + "* [Natural-gas compressor trains with NeqSim](process/GasCompressorTrain.ipynb): compare shortcut, detailed-EoS, Schultz, SRK, GERG-2008, and map-based methods; build two-stage compression, generate lift curves, and evaluate recycle-based antisurge control.\n", + "* [Automated process design in NeqSim](process/automatedprocessdesign.ipynb): build and audit a valve-cooler-separator-compressor flowsheet, screen 42 design points, apply constraints, trace a Pareto frontier, and export an automation snapshot.\n", + "* [Engineering calculations using the Python Fluids package](https://fluids.readthedocs.io/)\n", + "* [Engineering calculations using the Python heat transfer package](https://ht.readthedocs.io/)\n", + "* [Calculation of flow in fittings, valves, orifice plates etc.](process/fluidsandneqsim.ipynb)\n", + "* [NeqSim-connected separator sizing in SI units](process/sepsioze.ipynb): build a four-stage production process, extract phase properties, preserve and refresh seven vertical and horizontal two- and three-phase sizing cases, validate balances, and screen throughput and temperature sensitivities.\n", + "* [Gibbs reactor](reactions/GibbsReactor1.ipynb)\n", + "\n", + "## Downstream processing\n", + "* [How crude oil is sold to Europe](gasvaluechain/european_crude_oil_sales_foundation.ipynb): follow a synthetic North Sea cargo through quality, net standard quantity, quotation-period pricing, terminal and tanker logistics, buyer value, producer netback, and cash reconciliation.\n", + "* [Oil-refinery simulation foundation with NeqSim](process/neqsim_oil_refinery_simulation_foundation.ipynb): connect a synthetic TBP assay to a current-source NeqSim refinery front end, component and energy closure, cut recoveries, heat recovery, direct emissions, temperature sensitivity, margin, and crude-slate optimisation.\n", + "* [Crude-oil preflash and atmospheric flash-zone screening with NeqSim](process/oilrefineries.ipynb): characterize a synthetic TBP assay, model staged flash separation, close mass and energy balances, and screen operating sensitivities.\n", + "\n", + "## Refrigeration and heat pumps\n", + "* [Air and water cooling with NeqSim](process/air_and_water_cooling.ipynb)\n", + "* [Propane mechanical refrigeration with NeqSim](process/MechanicalCooling.ipynb): build and validate a vapour-compression cycle, balances, COP, scale-up, and operating sensitivities.\n", + "* [Propane heat-pump performance and operating limits with NeqSim](process/heatpumps.ipynb): calculate heating COP, source and sink sensitivities, seasonal performance, and preliminary 1 MW utility sizing.\n", + "\n", + "## LNG (liquified natural gas)\n", + "* [LNG process efficiency: SMR, C3MR, DMR, and nitrogen expansion](process/LNG_Liquefaction_Processes.ipynb)\n", + "* [Ship transport and LNG (LNG ageing)](process/lngageing.ipynb)\n", + "\n", + "## Hydrogen\n", + "* [Thermodynamic and physical properties of hydrogen](thermodynamics/ThermodynamicsOfHydrogen.ipynb)\n", + "* [Hydrogen production by reforming, shift, cooling, separation, and compression](thermodynamics/productionOfHydrogen.ipynb): preserve the original pre-reforming, thermochemistry, oxidation, and partial-oxidation cases; solve current NeqSim Gibbs equilibrium; audit balances and sensitivities; and connect syngas to a composable downstream process.\n", + "* [Hydrogen pipeline transport and compression with NeqSim](hydrogen/transportOfHydrogen.ipynb): preserve the original pure-H₂ property, two-stage compression, 500 km pipeline, profile, and ISO 6976 examples; add property-model comparison, mass and energy audits, diameter and throughput sensitivities, and seven inspected figures.\n", + "* [Liquefaction of hydrogen](hydrogen/liquefaction_of_hydrogen.ipynb)\n", + "\n", + "## Methanol\n", + "* [Thermodynamics of methanol](thermodynamics/ThermodynamicsOfMethanol.ipynb)\n", + "* [Physical properties of methanol](thermodynamics/PhysicalPropertiesOfMethanol.ipynb)\n", + "* [Production of methanol](reactions/Methanol_Production.ipynb)\n", + "\n", + "## Ammonia\n", + "* [Thermodynamics of ammonia](thermodynamics/ThermodynamicsOfAmmonia.ipynb): pure-ammonia phase equilibrium, public-property validation, model sensitivity, and process heating.\n", + "* [Physical properties of ammonia](thermodynamics/PhysicalPropertiesOfAmmonia.ipynb): phase-aware caloric and transport properties, validation, sensitivities, heat-transfer screening, and process integration.\n", + "* [Production of ammonia from natural gas](reactions/blue_ammonia_production.ipynb): steam reforming, water-gas shift, CO2 capture, compression, synthesis, and product recovery.\n", + "* [Ammonia as a refrigerant](process/ammonia_refrigeration.ipynb): saturation properties, vapour-compression cycle, composable process model, balances, and operating sensitivities.\n", + "* [Power production from ammonia](power/ammonia_power_generation.ipynb): atom-balanced combustion products, Brayton-cycle streams, power and efficiency balances, sensitivities, and emissions limitations.\n", + "\n", + "## Process safety\n", + "* Simulation of process blow down\n", + "* [Facilitator-ready HAZOP workshop with NeqSim](process/hazop_workshop_with_neqsim.ipynb): build a calculated inlet-separation and export-compression process; discover native nodes; apply facilitator-reviewed IEC 61882 deviations; quantify blocked outlet, compressor temperature, and gas blow-by; rank risk and produce an action register.\n", + "* [Loss-of-containment consequence analysis with NeqSim](process/loss_of_containment_consequence_analysis.ipynb): calculate transient source terms, choked-flow validation, 0.5 LFL and H2S endpoints, jet-fire radiation, VCE overpressure, and effect envelopes.\n", + "* [Barrier performance and ESD response-time verification with NeqSim](process/barrier_performance_and_esd_response_time.ipynb): derive process safety time; verify native detection, logic, and final-element timing; build cause-and-effect, evidence-linked performance standards, SCEs, and a barrier register; screen impairments.\n", + "* [DEXPI 2.0 to safety-study workflow with NeqSim](process/dexpi_safety_study_workflow.ipynb): export a calculated process to DEXPI 2.0; audit equipment, piping, instrumentation, design conditions, and safety semantics; build HAZOP nodes, C&E records, and a cross-study readiness matrix; retain missing SIS metadata in a controlled register.\n", + "* [Integrated SIL, ESD, HIPPS, and depressurization safety study](process/integrated_sil_esd_hipps_depressurization_study.ipynb): connect LOPA and native SIL verification to 2oo3 HIPPS voting, ESD timing, real-gas pressure rise, cold and fire blowdown, simultaneous flare load, and header-Mach sensitivity.\n", + "* [ESD, PSV, and controlled gas depressurization with NeqSim](process/ESD_PSV_Process_Safety_Demo.ipynb): calculate SRK gas properties, native PSV hysteresis and API 520 screening, blocked-outlet protection, manual and PSHH-triggered ESD blowdown, ISO 5167 orifice flow, balances, and blowdown-orifice trade-offs.\n", + "* [ESD system, alarm and flare](process/ESD_Fire_Alarm_System_Tutorial.ipynb)\n", + "* [Alarm handling in NeqSim](process/Alarm_Handling_NeqSim.ipynb)\n", + "* [Use of process simulators and NeqSim for process safety design](process/neqsim_process_safety_design.ipynb)\n", + "* [High Integrity Pressure Protection System (HIPPS) and process simulation](process/HIPPS_Safety_Simulations.ipynb)\n", + "* [Bow-tie, LOPA, and safety-instrumented risk analysis with NeqSim](process/bowtie_lopa_sif_risk_analysis.ipynb): connect an SRK inlet-separation process to native bow-tie generation, SIF PFD/SIL verification, LOPA, proof-test sensitivity, conservation checks, and a combined risk-and-capacity screen.\n", + "\n", + "## Power production\n", + "* [Electrical engineering fundamentals for oil and gas operations](power/electrical_engineering_fundamentals_oil_gas.ipynb): connect NeqSim compressor and water-injection pump duties to three-phase power, motors and VFDs, load lists, transformers, cables, fault current, starting, protection, harmonics, reliability, and operating constraints.\n", + "* [NCS wind, battery, and gas-turbine electrification with NeqSim](power/ncs_wind_battery_gas_electrification.ipynb): build NeqSim from a pinned repository commit, derive an offshore process load, dispatch Hywind-scale synthetic wind and balancing gas, evaluate native battery storage, and screen direct CO₂, curtailment, ramps, reserve, and field-maturity sensitivities.\n", + "* [Process-coupled offshore electrification with NeqSim](power/process_coupled_offshore_electrification_study.ipynb): extend a real separation and recompression process with 70-to-160 bara export compression, derive flow-dependent electrical demand from solved equipment duties, and compare gas turbines, shore power, wind, battery storage, and hybrid operation.\n", + "* [Natural-gas combustion, burner devices, and NeqSim integration with Cantera](reactions/natural_gas_combustion_with_cantera.ipynb): compare fuel and oxidizer blends; boiler, furnace, low-NOx, gas-turbine, duct-burner, and flare cases; validate composition and mass closure; model staged combustion; and run a Cantera custom unit inside a NeqSim process.\n", + "* [Gas-fired power plants with NeqSim](power/Gas_fired_power_plants.ipynb): fuel quality, stoichiometric combustion, Brayton-cycle states, heat recovery, balances, direct CO2 intensity, and operating sensitivities.\n", + "* [Natural-gas combined-cycle power plant with NeqSim](power/combined_cycle_power_plant.ipynb): Brayton gas turbine, single-pressure HRSG, CPA water/steam Rankine cycle, balances, quality checks, and operating sensitivities.\n", + "\n", + "## CO2 removal and handling\n", + "* [Thermodynamic and physical properties of CO2 — model selection, phase envelope, depressurization, and export conditioning](thermodynamics/ThermodynamicandphysicalpropertiesofCO2.ipynb)\n", + "* [EOS-CG and GERG-2008 for CO2](thermodynamics/EOSCG_vs_GERG2008.ipynb)\n", + "* [CO₂-rich phase envelopes, solids, hydrates, and conditioning](thermodynamics/phaseenvelopesofCO2richmixtures.ipynb): compare pure CO₂, CO₂–CH₄, and CO₂–CH₄–H₂S phase behavior; screen solid and hydrate boundaries; map properties; and connect the results to compression, cooling, throttling, separation, balances, and operating sensitivity.\n", + "* [Thermodynamics of CO2 rich gases and water](thermodynamics/CO2richandwater.ipynb)\n", + "* [CO₂ solubility, speciation, and MDEA gas treating](thermodynamics/CO2alkonolamines.ipynb): compare Electrolyte-CPA and Electrolyte-ScRK equilibrium, inspect aqueous speciation, screen loading, temperature, model, and solvent-rate sensitivities, and run a balanced stream–mixer–separator contact process.\n", + "* CO2 removal from natural gas\n", + "* CO2 removal from gas fired power plants\n", + "* [CO₂ compression, intercooling, and dense-phase pumping with NeqSim](process/CO2_compression.ipynb): build a three-stage PR-EOS compression train with intercooling, dense-phase pumping, injection conditioning, phase-envelope diagnostics, stage-count and intercooler sensitivities, mass and energy closure, and operating-limit checks.\n", + "* [CO₂ dehydration for pipeline transport with NeqSim](process/CO2_Dehydration_Pipeline.ipynb): map saturated water content, impurity phase behaviour, transport density, TEG absorption, circulation and purity sensitivities, hydraulic screening, and mass balances.\n", + "* CO2 depressurization\n", + "* [CO2 injection pipelines](fluidflow/CO2pipeline.ipynb)\n", + "* [CO2 chain from compression to pipeline](process/co2_chain_from_compression_to_pipeline.ipynb): build and validate staged compression, intercooling, dense-phase diagnostics, and an 80 km NeqSim pipeline design study.\n", + "* [CO₂ trace-acid formation, partitioning, and compression conditioning with NeqSim](thermodynamics/CO2reactions.ipynb): preserve the original acid, sulfur, and pH lessons; apply an oxygen-limited element-balanced formation screen; refresh Peng–Robinson and Electrolyte-CPA results; and run a two-stage compression, intercooling, and separation sensitivity workflow.\n", + "* [CO2 and reactions in a recompressor train](thermodynamics/Co2_recompression.ipynb)\n", + "\n", + "## Gas and oil transport\n", + "* [Design of a gas pipeline](fluidflow/gaspipeline.ipynb)\n", + "* [Design of a multi-phase pipeline](fluidflow/twophasepipeline.ipynb)\n", + "* [Two-fluid transient, slug-flow, and S-riser modelling](fluidflow/two_fluid_transient_slug_flow.ipynb): compare `TwoFluidPipe` with Beggs–Brill, map flow regimes, simulate pressure and liquid-inventory transients, and study an oil-and-gas wellstream in an S-riser.\n", + "* [Flexible multiphase flowlines and risers](fluidflow/flixiblepipes.ipynb): use current `PipeBeggsAndBrills` profiles, thermal modes, connected lazy-wave-riser legs, API RP 14E velocity screening, and diameter/rate design scenarios.\n", + "* [Transient multiphase flow with NeqSim's drift-flux model](process/transient_multiphase_flow_tutorial.ipynb): build an SRK gas-condensate fluid, simulate horizontal and terrain flow, inspect drift-flux closure and accumulation, exercise controlled slug diagnostics, validate a vertical-riser initial state, test mesh sensitivity, and connect a flow-step case to a receiving separator.\n", + "* [Water-hammer simulation with NeqSim](process/water_hammer_simulation_tutorial.ipynb): use the released `WaterHammerPipe` MOC solver for valve closure, pressure histories and envelopes, closure-time sensitivity, cavitation screening, elevation, material effects, and ESD design.\n", + "\n", + "## Flow assurance\n", + "* [Thermodynamics of natural gas hydrates — CPA equilibrium, MEG and salt inhibition, and arrival-process screening](thermodynamics/thermodynamics_of_natural_gas_hydrates.ipynb)\n", + "* [Wax appearance, model tuning, and flowline operability with NeqSim](thermodynamics/thermodynamicsOfWax.ipynb): characterize a reservoir fluid, calculate and tune wax precipitation, map WAT and wax fraction, and screen cooling and insulation scenarios along a segmented flowline.\n", + "* [Mineral-scale thermodynamics and flowline control with NeqSim](thermodynamics/ThermodynamicsOfMineralScale.ipynb): calculate Electrolyte-CPA aqueous speciation and saturation indices, screen water chemistry and incompatible-brine mixing, profile flowline risk, assess inhibitor dose, and couple multiphase hydraulics to deposition.\n", + "* [PVT property tables for multiphase flow simulators](thermodynamics/PVTtableGeneration.ipynb): generate and audit OLGA-style tables for wet gas, characterized condensate, and an Eclipse-described well fluid; inspect phase/property grids, qualify interpolation resolution, and retain engineering checks.\n", + "* [Top-of-line condensation and MEG carryover with NeqSim](process/topoflinecondensation.ipynb): use CPA fugacity equilibrium to saturate gas with MEG-water liquid, quantify cold-wall condensate, close flash balances, screen a cooling flowline profile, and compare inhibitor strengths.\n", + "* [MEG injection and evaporation of water](thermodynamics/MEG_injection_and_evaporation.ipynb): model SRK-CPA water/MEG equilibrium, hydrate inhibition, evaporation, and a validated saturation–injection–separation workflow.\n", + "* [Asphaltenes in oil and gas production](thermodynamics/Asphaltene_Modeling_Tutorial.ipynb)\n", + "\n", + "## Process control\n", + "* [Dynamic process control with NeqSim](process/process_control_with_neqsim_v2.ipynb): build native separator pressure and level loops, connect two-stage separation and flash-gas recompression, implement cascade and ratio control, compare PI tuning, and filter transmitter noise.\n", + "* [Reinforcement Learning integration](process/reinforcement_learning_integration.ipynb)\n", + "* [Data-driven separator pressure control with NeqSim](process/data_driven_control_strategies.ipynb): build an SRK wellstream, equilibrium separator, gas valve, and pressure transmitter; derive real-gas inventory and valve-capacity tables; apply deterministic Kalman estimation and constrained MPC; and validate disturbance rejection, control bounds, and mass closure.\n", + "* [Model predictive control](process/model_predictive_controller_examples.ipynb)\n", + "* [DEXPI standard](process/dexpi_demo.ipynb)\n", + "\n", + "# Process automation and logics imlementation\n", + "* [Process logic and safeguarding with NeqSim](process/NeqSim_Process_Logic_Demo.ipynb): connect an SRK-CPA wellstream and heated three-phase separator to pressure detectors, 2oo3 HIPPS voting, sequenced ESD actions, fire-and-gas detection, response scenarios, and engineering checks.\n", + "* [Process alarms, interlocks, and sequential logic around a NeqSim model](process/ProcessLogicIntegrated.ipynb): build a current NeqSim valve-and-separator flowsheet, implement priority, deadband, latching, delayed blowdown, reset permissives, and validate a real-gas vessel balance.\n", + "\n", + "## Oil and Gas metering and analysis\n", + "* [Multi phase measurments](process/MultiphaseflowMeasurement.ipynb)\n", + "* [Allocation of production](process/allocationoilandgas2.ipynb)\n", + "\n", + "## Gas and Oil specifications\n", + "* [Calorific value of natural gas (ISO6976)](gasquality/CalorificValueNaturalGas.ipynb)\n", + "* [Natural-gas blending and quality optimization](gasquality/gas_blending_quality_optimization.ipynb)\n", + "* [Oil quality specifications: assay characterization, API gravity, viscosity, vapor pressure, and stabilization](gasquality/oilqualityspecifications.ipynb): executable SRK workflow with ASTM D6377 screening, six technical figures, and a connected valve-conditioner-separator application.\n", + "* [Hydrocarbon dew point](gasquality/hydrocarbon_dew_point_of_natural_gas.ipynb)\n", + "* [Oil vapour pressure: TVP, VPCR4, and RVP](process/TVP_RVP_Study.ipynb): calculate method-specific vapour pressure, composition and temperature sensitivity, and stabilization-pressure trade-offs.\n", + "\n", + "## Process simulation using NeqSim\n", + "* [Simulation of oil stabilization](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/oilstabilizationprocess.ipynb)\n", + "* [Produced-water treatment with NeqSim](process/producedwatertreatment.ipynb): preserve the two legacy CPA separation studies and hydrocyclone video; quantify flare gas, dissolved hydrocarbons, and BTEX over pressure and temperature; use the released Electrolyte-CPA water builder and native hydrocyclone sizing, PDR, droplet-distribution, capacity, reject-flow, and oil-in-water screening APIs; and close the connected process mass balance.\n", + "* [Simulation of TEG dehydration process](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/TEGprocessHX.ipynb)\n", + "* [Simulation of MEG hydrate inhibition and regeneration](process/MEGwaterprocess.ipynb): CPA hydrate envelopes, cold liquid dropout, and rich-MEG regeneration balances.\n", + "* [Exergy analysis of oil and gas processes](process/exergyanalysisofoilprocess.ipynb): preserve the legacy offshore process example while building a self-contained three-stage stabilization and export train, closing mass, component, and energy balances, auditing entropy and exergy, ranking irreversibility, and screening separator-pressure trade-offs.\n", + "* [Process equipment weight screening with NeqSim](process/weightofoilprocess.ipynb): rebuild the original offshore HP/MP/LP stabilization process with current APIs, close gas/oil/water balances, calculate native equipment and discipline weights, inspect separator dimensions, and rerun capacity sensitivities.\n", + "* [Optimization of a field producing from both gas and oil reservoir](reservoir/optimizationofoilandgasproduction.ipynb)\n", + "* [Process flow diagrams driven by NeqSim results](process/process_flow_diagram.ipynb): characterize a rich wellstream, connect separation, gas compression, and liquid throttling, verify balances, render a calculated pyflowsheet SVG with embedded tables, and screen export pressure.\n", + "* [Integration of third party tools into neqsim process simulations](process/neqsimreaktoro.ipynb)\n", + "* [Adding a new unit operation using python](process/newunitoperation.ipynb)\n", + "* [Adding a ML based unit operation](process/heat_exchanger_ml_external_unit.ipynb)\n", + "\n", + "\n", + "##Integrated modelling reservoir, well, transport and process\n", + "* [Reservoir-to-market system curves with a capacity-designed topside](reservoir/reservoir_well_topside_market_system_curves.ipynb): connect one fluid from reservoir inflow through well and flowline lift, HP/MP/LP separation, flash-gas recompression, two-stage export compression, separator and scrubber sizing, complete compressor maps, reservoir-pressure nodal matrices, debottlenecking, depletion, and capacity-bounded ECLIPSE VFPPROD export.\\n* [Integrating reservoir and process simulations](reservoir/reservoirandprocess.ipynb)\n", + "* [Ensemble-based integrated reservoir and process modelling](reservoir/ensamble_based_modelling.ipynb): update a reproducible reservoir ensemble with production history, transfer posterior rates through an explicit ERT-compatible contract, run a NeqSim choke, cooler, three-phase separator, and compressor, and quantify bottleneck probability and constrained production.\n", + "\n", + "## NeqSim PVT and Process Simulation API\n", + "* [How to create an API using NeqSim and Python](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/python)\n", + "* [How to create an API using NeqSim and Java](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/java)\n", + "* [NeqSim PVT API: executable property workflows](API/NeqSimPVTAPI.ipynb): build SRK, GERG-2008, and CPA fluids; run unit-aware TP flashes; characterize heavy fractions; retrieve named phase properties; compare gas models; sweep phase behavior; and validate closure and property trends.\n", + "* [NeqSim Process API: validated engineering calculation services](API/NeqSimProcessAPI.ipynb): build unit-aware request contracts for SRK-CPA TEG dehydration and a staged offshore process, return finite JSON-compatible results, reject invalid inputs, and screen independent operating scenarios.\n", + "\n", + "# Online process simulation\n", + "* [Online oil-stabilization simulation with NeqSim](process/onlineprocesssimulation.ipynb): build a PR well fluid, solve three-stage stabilization, recompress flash gas, update live inputs, run historian scenarios, and validate mass closure, product quality, power, and recycle convergence.\n", + "* [Implementing effective online process simulations](process/online_process_simulation_demo1.ipynb): build a four-stage stabilization, gas recompression, dew-point control, recycle, oil export, and produced-water process; compare cold, warm, and single-step online strategies over 30 points; inspect structured reports; and validate conservation, runtime, and provisional-result quality.\n", + "\n", + "## Process plant operation\n", + "* [Weather-aware gas processing and power generation with NeqSim](process/weatherandprocess.ipynb): preserve daily, hourly, station, and forecast workflows; calculate SRK air properties, export-compressor demand, turbine capacity, fuel, and direct emissions with deterministic weather fallbacks.\n", + "* Condition based monitoring\n", + "* [Condition-based monitoring of heat exchangers with NeqSim](process/heat_exchanger_condition_monitoring.ipynb): normalize historian data with native SRK and SRK-CPA streams, invert the two-stream HeatExchanger for effective UA, audit hot/cold duty consistency, detect fouling and sensor bias, verify cleaning recovery, and return reusable equipment-health snapshots.\n", + "\n", + "## Operations and asset management\n", + "\n", + "* [Facility lifecycle from commissioning to decommissioning](operations/facility_lifecycle_commissioning_to_decommissioning.ipynb): connect nitrogen properties, drying and inerting, hydrocarbon introduction, process ramp-up, shutdown inventory and low-temperature screening, late-life economic limits, cessation, P&A, removal schedules, waste and recycling, cost uncertainty, and emissions using public-PyPI NeqSim with Python.\n", + "* [Operations and asset management with NeqSim and Python](operations/operations_asset_management_with_neqsim.ipynb): connect source-built NeqSim process and failure consequences to censored reliability fitting, condition trends, maintenance-policy and shared-crew RAM simulation, availability, and uncertainty-weighted production accounting.\n", + "\n", + "## Materials in gas processing\n", + "* Material selection in gas processing\n", + "* [CO₂ corrosion, inhibition, and pipeline integrity](material/corrosionandthermo.ipynb): Electrolyte-CPA speciation, NORSOK M-506 screening, mechanistic inhibition, material selection, and coupled flowline integrity.\n", + "* [Elemental sulfur (S8) in natural gas processing](material/elemental_sulfur.ipynb)\n", + "* [Acid partitioning between gas, oil, and water](thermodynamics/formicacid_calculation.ipynb)\n", + "\n", + "## Emissions\n", + "* [CO2 emissions (scope 1, 2 and 3)](https://colab.research.google.com/drive/1Iwja5bKiP-fC2tS9O97-06j5XhqE0lJK#scrollTo=TgILfzXSzDAP)\n", + "* [Hydrocarbon emissions from processing and transport](emissions/Hydrocarbon_emissions.ipynb): use CPA phase equilibrium, high-pressure separation, isenthalpic letdown, low-pressure flash-gas accounting, methane/VOC breakdown, operating maps, and mitigation scenarios.\n", + "* [Chemicals consumption (glycols/etc)](process/cemicalsconsumption.ipynb): use CPA phase equilibrium and a connected SimpleTEGAbsorber to quantify equilibrium vapor loss, water removal, packing capacity, circulation sensitivity, and annual TEG make-up.\n", + "* [CO₂ emissions across the natural-gas value chain with NeqSim](emissions/CO2emsissionsinthevaluechain.ipynb): connect SRK separation, offshore and onshore compression, cooling, ISO 6976 gas quality, composition-derived carbon factors, Scope 1/2/3 boundaries, electrification scenarios, avoided-cost screening, and pressure–power sensitivity.\n", + "\n", + "##Pipeline network optimization\n", + "* [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; use the same speed, power, map, and custom capacity constraints in typed operating-point results, bottleneck analysis, and maximum-throughput pressure-boundary optimization; quantify driver-upgrade value and production gains from declining inlet pressure.\n", + "* [Dry gas parallel-pipeline network optimization](process/PipeNetworkOptim.ipynb): balance unequal branches, screen capacity and diameters, map throughput, and connect compression, cooling, splitting, pipe hydraulics, and delivery-node mixing.\n", + "* [Multiphase network modeling and optimization](process/MultiphaseOptim.ipynb): balance two three-phase well branches at a shared manifold, screen export bore and backpressure, model thermal export hydraulics, and close a receiving-separator mass balance.\n", + "\n", + "\n", + "## Machine learning techniques and artificial intelligence (AI) in gas processing simulation\n", + "* Use of Languge Models and NeqSim\n", + "* Machine learning and PVT\n", + "* [Machine learning and process simulation](process/Machine_learning_and_process_simulation.ipynb)\n", + "* [AI-assisted gas-processing simulation and design with NeqSim](process/AIgasprocessing.ipynb): generate audited choke–cooler–three-phase-separator–compressor cases, fit and validate a transparent quadratic surrogate, screen constrained operation, verify AI recommendations in NeqSim, and demonstrate synthetic residual monitoring.\n", + "* [Machine-learning-driven heat exchanger unit operations](process/heat_exchanger_ml_external_unit.ipynb)\n", + "\n", + "## Innovative technologies combining AI technologies and NeqSim\n", + "* [Real-Time process monitoring for operational safety and compliance](AI/Real_Time_process_monitoring_for_operational_safety_and_compliance.ipynb)\n", + "* [NeqSim and Data Analytics using Seeq](AI/NeqSim_and_Seeq.ipynb)\n", + "* Optimization of process parameters to maximize efficiency and production\n", + "* Anomaly detection to detect unusual patterns or deviations in process data\n", + "* Integration of different data sources to analyze data from different sources (sensor data, simulation data, weather data and external environmental data) to provide a more holistic understanding of processes\n", + "* Digital twins used to simulate and optimize processes in a virtual setting before implementation in the real world\n", + "* Self-adjusting control systems that adjust themselves based on continuous feedback and changes in process data\n", + "* Predicting oil spills or gas leaks for environmental monitoring\n", + "* Virtual measurements in oprocess plants using neqsim and AI technologies\n", + "* [IoT and Industry 4.0](AI/IoT_and_Industry4.0_with_NeqSim.ipynb)\n", + "\n", + "\n", + "## Statistics\n", + "* [Statistical Sites on the World Wide Web](statistics/WorldStats.ipynb)\n", + "* [World oil and natural-gas production statistics connected to NeqSim](statistics/worldOilandGasProduction.ipynb): audit an embedded 2024 Energy Institute/Our World in Data snapshot, close world totals, calculate producer shares and grouped concentration, apply SRK and ISO 6976 gas quality, bridge energy to standard volume, and screen export compression and direct-combustion scenarios.\n", + "* [Oil and Gas Price Statistics and Analysis](statistics/OilandGasPriceStatistics.ipynb)\n", + "* [Norwegian Continental Shelf production statistics and NeqSim export-process capacity screening](statistics/ProductionfromNorwegianContinentalShelf.ipynb)\n", + "* [CO2 emissions from oil and gas production](statistics/CO2emissionsNorwegianContinentalShelf.ipynb)\n", + "* [Statistics of CO2 in atmosphere](statistics/CO2inatm.ipynb)\n", + "\n", + "## Energy System Modelling\n", + "* [Energy system modelling](https://oemof.org/)\n", + "* [Thermal Engineering Systems](https://tespy.readthedocs.io/en/main/index.html)\n", + "* [A Sector-Coupled Open Optimisation Model of the European Energy System](https://pypsa-eur.readthedocs.io/en/latest/)\n", + "* [Examples of NeqSim in Energy System Modelling](energyopt/ThermalEnergyAndNeqSim.ipynb#scrollTo=C7-qUy51VbFs)\n", + "* [PyPSA-Earth example for Norway](energyopt/PyPSA-Earth_Norway.ipynb)\n", + "\n", + "## Economic analysis\n", + "* [NeqSim-connected NCS gas-field economy analysis](fielddevelopment/economy.ipynb): connect SRK separation and export compression, native mechanical-design cost estimation, declining field pressure and production, corrected tax-rate algebra, cash flow, NPV, IRR, payback, break-even, and commercial sensitivities.\n", + "* [Integrated process cost estimation and economic screening](fielddevelopment/process_cost_estimation_and_economics.ipynb): connect a gas-process simulation to mechanical design, CAPEX, OPEX, location/material/CEPCI sensitivities, and financial metrics.\n", + "\n", + "## Earth Tools and Metocean Data\n", + "* [Metocean and field development with NeqSim](fielddevelopment/metocean_and_field_development.ipynb): use a checksum-controlled public NORA10 teaching series with metocean statistics, weather windows, extremes, joint contours, spectra, tides, and NeqSim seasonal tie-back cases.\n", + "* [Earth tools and field development with NeqSim](fielddevelopment/earth_tools_and_field_development.ipynb): combine CRS-safe GeoPandas vectors, raster bathymetry, exclusion-aware least-cost routes, network analysis, interactive mapping, NeqSim tie-back hydraulics, and host economics.\n", + "\n", + "## Integrated energy and emission calculations\n", + "* [Integrated NeqSim + eCalc field-life, compressor-map, and offshore-energy study](power/neqsim_ecalc_integrated_energy_emissions.ipynb): combine three-pressure separation and recompression, complete NeqSim-generated compressor maps with surge and stonewall boundaries, declining inlet-pressure field life, native wind and battery dispatch, and eCalc fuel, CO₂, capacity, recirculation, and intensity accounting.\n", + "\n", + "## Northern Lights CCS open-data calculations\n", + "The numbered series uses the Equinor Northern Lights Databricks Marketplace listing as the authoritative full-data boundary and small hash-checked Eos snapshots for credential-free execution. NeqSim owns thermodynamics, wells, pipelines, facilities, and operations; OPM Flow owns dynamic storage-reservoir simulation.\n", + "* [01 - Northern Lights open-data foundation and NeqSim model basis](fielddevelopment/northern_lights/01_northern_lights_open_data_foundation.ipynb): audit licensed Eos trajectory, interpreted formation, UCS, and public well-test evidence; build an exact source-master NeqSim runtime; calculate EOS-CG CO₂ and CPA water anchors, a frictionless hydrostatic injection screen, and Phase 1/2 capacity translations; then issue explicit NeqSim and OPM Flow handoffs.\n", + "\n", + "## Volve field calculations\n", + "This principal eight-part field-lifecycle collection uses the Equinor Volve Data Village listing as its authoritative measured-data boundary and carries versioned handoff contracts from one discipline to the next. It follows the [NeqSim Field Development and Operations book](https://equinor.github.io/neqsimhome/doc/field_development_and_operations/book_standalone.html) from SEG-Y and development framing through OPM Flow, production technology, facilities, constrained operations, late life, shutdown, and decommissioning. A clean teaching fallback is stored for reproducibility, but it is explicitly labelled and is not represented as measured Volve data. Run the numbered notebooks in order.\n", + "* [01 - Volve seismic, wells, and static model](fielddevelopment/volve/01_volve_seismic_wells_static_model.ipynb): inventory and select Marketplace SEG-Y and well assets; perform geometry, amplitude, horizon, depth-conversion, log, petrophysical, fluid-density, volumetric, and uncertainty screens; then write the geoscience-to-reservoir handoff.\n", + "* [02 - Volve PVT, black oil, and reservoir](fielddevelopment/volve/02_volve_pvt_blackoil_reservoir.ipynb): select PVT, Eclipse/OPM, and production assets; characterize the fluid with NeqSim SRK; generate black-oil/DLE properties; screen history, material balance, depletion, and OPM Flow acceptance; then write the reservoir forecast handoff.\n", + "* [03 - Volve wells, SURF, and flow assurance](fielddevelopment/volve/03_volve_wells_surf_flow_assurance.ipynb): connect trajectories and completions to productivity, injectivity, IPR/VLP, NeqSim wellstreams, pipeline hydraulics, thermal response, hydrate margins, cooldown, and the facility-inlet envelope.\n", + "* [04 - Volve facilities and processing](fielddevelopment/volve/04_volve_facilities_processing.ipynb): model inlet conditioning, three-phase separation, oil stabilization, gas compression and cooling with NeqSim; locate bottlenecks; and screen produced water, chemicals, utilities, emissions, availability, and safeguarding.\n", + "* [05 - Volve integrated field twin and decisions](fielddevelopment/volve/05_volve_integrated_field_twin.ipynb): assemble all discipline contracts; reconcile history and capacity ownership; test interventions, uncertainty, economics, emissions, and decision gates; and issue a traceable integrated field-evaluation handoff.\n", + "* [06 - Volve reservoir simulation and production history matching](fielddevelopment/volve/06_volve_reservoir_history_matching.ipynb): obtain and audit the Marketplace production workbook; calculate NeqSim PVT anchors; run and fit a communicating two-tank reservoir simulator to pressure, oil, gas, and water; test a holdout period; diagnose identifiability; propagate uncertainty; and expose an optional controlled OPM Flow acceptance path.\n", + "* [07 - Volve all wells, nodal analysis, gathering, and full SURF](fielddevelopment/volve/07_volve_all_wells_gathering_surf.ipynb): simulate every active producer with IPR, NeqSim-calibrated tubing VLP, choke and branch loss; solve two manifolds, trunks, a common riser, and host backpressure; verify the network in a composable NeqSim ProcessSystem; and evaluate integrity, late-life, outage, shutdown, cooldown, hydrate, and restart cases.\n", + "* [08 - Volve closed-loop full-field development and operations](fielddevelopment/volve/08_volve_closed_loop_field_development_operations.ipynb): convert the static handoff into development scenarios and producer/injector placement; generate OPM well schedules and an OPM-primary history-match ensemble; feed NeqSim well, SURF, separation, compression, water, export, and injection limits back into monthly reservoir controls; and calculate the economic limit, shutdown handoff, plugging inventory, removal sequence, and decommissioning uncertainty.\n", + "* [Compact Volve full-field precursor with OPM Flow and NeqSim](fielddevelopment/volve_full_field_development_opm_neqsim.ipynb): compare producer/injector layouts in a reduced teaching sector and connect reservoir states to PVT, wells, SURF, topsides, schedule, economics, and uncertainty.\n", + "\n", + "## Field development\n", + "* **Advanced** - [NCS resource classification and project maturation with NeqSim](fielddevelopment/ncs_resource_classification_with_neqsim.ipynb): explain RC0-RC9, F/A project categories, BOI/BOK/BOV/BOG/PDO transitions, low/base/high uncertainty and discovery chance; build a governed project register with pandas and NetworkX; derive marketable gas and condensate yields with a source-traceable NeqSim process; connect volumes to capacity-limited profiles, synthetic economics, public SODIR data contracts, and reusable CSV/JSON handoffs; and link the exploration-to-market notebook chain.\n", + "* [SEG-Y with segyio to field-development decisions with NeqSim](fielddevelopment/segyio_to_field_development_workflow.ipynb): create and round-trip a synthetic 3-D SEG-Y cube, derive similarity and fault masks, interpret top/base/thickness, propagate low/base/high STOIIP, screen wells and field-life capacity, run NeqSim three-phase separation and export compression, and map the evidence to DG0-DG4 handoffs and decisions.\n", + "* [Offshore facility concept selection with NeqSim](fielddevelopment/offshore_facility_concept_selection_with_neqsim.ipynb): compare nine facility concepts using infrastructure distance, water depth, metocean extremes, environmental loads, weather operability, NeqSim tie-back hydraulics and process simulation, hard technical gates, lifecycle economics, weighted MCDA, TOPSIS, sensitivity analysis, and Monte Carlo robustness.\n", + "* [Norwegian field and area development lifecycle screening with NeqSim](fielddevelopment/norwegian_field_and_area_development_with_neqsim.ipynb): connect synthetic depletion and well deliverability to SRK phase behavior, choke/cooler/separator/compressor process models, SURF hydraulics, capacity constraints, export-pressure concepts, brownfield tie-in holdback, direct compressor emissions, product screens, and discounted cash flow.\n", + "* **Advanced** - [NCS tie-in from reservoir to market: host holdback, quality, tariffs, and tax](fielddevelopment/ncs_tie_in_quality_tariff_tax.ipynb): integrate a source-traceable NeqSim gas-condensate process, ASTM D6377 liquid vapour pressure, ISO 6976 gas blending, boundary-specific public quality examples, dated August 2026 Gassco K/I/O tariff and booking-vintage screens, host-priority/firm-tie/pro-rata holdback allocation, accepted-production economics, simplified petroleum tax, uncertainty, and multidisciplinary decision gates.\n", + "* [Producer and injector well-count and placement workflow](fielddevelopment/well_count_and_placement_workflow.ipynb): screen counts from deliverability, injectivity, and voidage replacement; optimize robust discrete layouts with pymoo NSGA-II; verify four concepts in OPM Flow; and connect the selected concept to NeqSim PVT, wells, SURF, facilities, capacity, economics, and uncertainty.\n", + "* [Discovery 1 DG1 SURF and host-facility design with NeqSim](fielddevelopment/discovery_1_dg1_surf_facilities_design.ipynb): mature the neutral-labelled Discovery 1 reservoir-to-host handoff into a 50-deliverable educational DG1 learning package covering flow assurance, subsea architecture, pipeline sizing, host processing, utilities, safety, cost, schedule, risk, and decision gates.\n", + "* Process design basis\n", + "* Proces design in a field development scenario\n", + "* [NPV of a gas-field tie-back with NeqSim](fielddevelopment/npv.ipynb): connect SRK fluid properties, tank depletion, well and tie-back hydraulics, host-arrival control, export compression, Norwegian petroleum-tax screening, break-even, coupled decision sensitivities, and driver-emissions scenarios.\n", + "\n", + "## Field developement case studies\n", + "* [Field development case 1 (combined oil and gas field)](reservoir/fieldDevelopment1.ipynb)\n", + "* Field development case 2 (rich gas and power from shore case)\n", + "* Field development case 3 (sales gas and power from shore)\n", + "* Field development case 4 (hydrogen production with power from shore/wind)\n", + "* Offshore field and onshore NGL process\n", + "\n", + "## Bio Engineering\n", + "* [Food-waste anaerobic digestion and biomethane-to-grid with NeqSim](bioprocesses/bioandneqsim.ipynb): connect empirical digestion, thermodynamic streams, gas upgrading, compression, cooling, mass balances, design sensitivities, and sustainability metrics.\n", + "* [Biomass to sustainable aviation fuel with NeqSim](bioprocesses/biomass_to_sustainable_aviation_fuel_with_neqsim.ipynb): compare HEFA, gasification–FT, ATJ, fast pyrolysis, HTL, and a biogenic-CO₂ hybrid with sourced yields, oil quality, NeqSim ASTM oil-quality calculations, lifecycle data, logistics, economics, uncertainty, and validation.\n", + "\n", + "## Standards\n", + "* Energy Institute - Guideline for Flow Induced Vibrations (FIV) control in Production\n", + "* [NORSOK S-001 technical-safety screening with NeqSim](standards/technicalsafetyP0001.ipynb): flash a synthetic high-pressure gas, screen secondary pressure protection, run native transient-wall vessel blowdown and API pool-fire scenarios, compare BDV bore and flare-load trade-offs, and integrate 20 engineering checks.\n", + "* [NORSOK P-002 process-system design screening with NeqSim](standards/norsokP0002.ipynb)\n", + "* NORSOK I-106, Fiscal metering systems for hydrocarbon liquid and gas,\n", + "* (ISO 13703 - Design and installation of piping systems on offshore production platforms\n", + "* [API 521 blocked-liquid thermal-expansion and relief screening with NeqSim](standards/API521_thermal_expansion.ipynb): reproduce the original PR fluid and linear pressure calculation, correct derivative units and sign, solve a nonlinear density closure, estimate heat-duty relief load, exercise native Stream and SafetyValve blowdown, and verify 16 engineering checks.\n", + "\n", + "## Development of Process Digital Twins\n", + "* [Oil Process](process/oilgasprocess1.ipynb)\n", + "* Monitoring of Cricondenbar of Natural Gas\n", + "* Monitoring of TEG dehdydration processes\n", + "\n", + "## Excercise\n", + "* Excercise 1 - [Phase behaviour of reservoir fluids](excercise/Excercise_Phase_Behaviour_of_Reservoir_Fluids%20(1).ipynb)\n", + "* Excercise 2 - [Design of a gas-oil separation process](excercise/Design_of_a_separation_process_in_HYSYS.ipynb)\n", + "* Excercise 3 - [Design of a TEG dehydration process](excercise/Design_of_a_TEG_dehydration_process.ipynb): preserve and repair the three original CPA and Campbell examples; quantify wet-gas water, dew point, hydrate temperature, lean-TEG equilibrium, circulation, stage sensitivity, and a native `SimpleTEGAbsorber` workflow with total and water balances.\n", + "* Excercise 4 - [Export-gas phase envelopes and hydrocarbon dew-point control](excercise/calculationofphaseenvelopes.ipynb): preserve three original SRK envelope cases, correct temperature units, compare pressure-specific dew points, validate a Stream dew specification, and run a connected cooler and scrubber with balances and operating sensitivities.\n", + "* Excercise 5 - [Gas processing and the gas value chain](gasvaluechain/GasProcessingChain.ipynb)\n", + "\n", + "## TEP4185 - Gas Process Technology\n", + "* [NeqSim Thermodynamics](https://colab.research.google.com/drive/1c6OY3O1xX8nmaU5JadF3CIjaj4CSYb1Q?usp=sharing)\n", + "* [Pandas + NeqSim — Data‑driven Thermodynamics](https://colab.research.google.com/drive/1ngKz_Ry8PkJwENdNXIkZpO8PmARiqyGr?usp=sharing)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RMS-origin reservoir automation\n", + "\n", + "* [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and a governed RMS-agent contract.\n" + ] + } + ], + "metadata": { + "colab": { + "name": "examples of NeqSim in Colab.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "_eRtkQnHpL70", - "language": "markdown" - }, - "source": [ - "# Oil and Gas Value Chain with NeqSim and Python" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kHt6u-utpvYf", - "language": "markdown" - }, - "source": [ - "[NeqSim (Non-Equilibrium Simulator)](https://equinor.github.io/neqsimhome/) is a Java\n", - "library for thermodynamic properties, PVT, flow, and process simulation. Google Colaboratory\n", - "(Colab) is a free Jupyter notebook environment that runs in a web browser. This collection uses\n", - "Python tools together with NeqSim Java to explain the complete oil and gas value chain: access and\n", - "exploration, fluid characterization, reservoirs, wells, subsea and SURF, facilities, products and\n", - "markets, operations, emissions, late life, and decommissioning.\n", - "\n", - "The notebooks serve both as teaching material and as transparent engineering-analysis examples.\n", - "Select **Runtime \u2192 Run all** in Colab to reproduce an executed notebook. Detailed examples identify\n", - "where NeqSim is the primary calculation engine, where it supplies thermodynamic or process\n", - "boundaries, and where specialist Python or external tools remain necessary.\n", - "\n", - "---\n", - "\n", - "***Learn to use NeqSim in Colab and contribute new material***\n", - "\n", - "Users are welcome to contribute NeqSim Colab pages. The\n", - "[validated contributor template](template.ipynb) demonstrates SRK fluid setup, ISO 6976 gas\n", - "quality, streams, mixing, compression, cooling, scenario analysis, assertions, and retained\n", - "figures. A practical introduction is available in [How to use NeqSim](howtouseneqsim.ipynb).\n", - "All notebooks are maintained in the open\n", - "[NeqSim-Colab GitHub repository](https://github.com/EvenSol/NeqSim-Colab).\n", - "\n", - "---\n", - "\n", - "**Comments and requests for new content**\n", - "\n", - "Use the [discussion forum](https://github.com/EvenSol/NeqSim-Colab/discussions) to discuss the\n", - "material. Request new content or suggest improvements by\n", - "[reporting an issue](https://github.com/EvenSol/NeqSim-Colab/issues).\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "language": "markdown", - "id": "TuNcSKktRaLf" - }, - "source": [ - "# Suggested Learning Path\n", - "\n", - "Use the catalog below as a reference library, or follow one of these short paths:\n", - "\n", - "* **Complete value chain:** begin with the orientation notebook at the start of the table of\n", - " contents, then follow its dependency-ordered path from PVT and reservoirs through retirement.\n", - "* **Field development:** exploration and PVT -> reservoir forecast -> wells and SURF -> facilities\n", - " and economics.\n", - "* **Asset lifecycle:** commissioning and start-up -> stable operation -> integrity and optimization\n", - " -> cessation and decommissioning.\n", - "\n", - "* **Beginner:** create a fluid -> read properties -> run a TP flash -> plot a phase envelope.\n", - "* **Process simulation:** separator -> compressor -> heat exchanger -> complete process model.\n", - "* **Engineering studies:** flow assurance -> process safety -> emissions -> standards checks.\n", - "* **Advanced workflows:** dynamic simulation -> digital twins -> process automation -> AI-assisted workflows.\n", - "\n", - "**Level guide:** Beginner notebooks introduce one concept at a time. Intermediate notebooks combine several NeqSim calculations. Advanced notebooks use automation, optimization, or workflow-style result objects.\n", - "\n", - "## New Capability Demonstration Notebooks\n", - "\n", - "* **Beginner** - [Modern natural-gas fluid properties with NeqSim](thermodynamics/modern_fluid_property_workflow.ipynb): create a gas fluid, run a TP flash, read properties, and generate a property table.\n", - "* **Intermediate** - [Process automation API for discoverable and safe NeqSim workflows](process/process_automation_api_demo.ipynb): build a gas conditioning process, discover unit-aware addresses, use safe and batch operations, evaluate setpoints, inspect utilization, and apply multi-area automation.\n", - "* **Advanced** - [Closed-loop process optimization](process/closed_loop_process_optimization.ipynb): sweep process setpoints and minimize compressor power.\n", - "* **Advanced** - [External nonlinear process optimization with SciPy and CasADi/IPOPT](process/external_nonlinear_process_optimization.ipynb): connect NeqSim ProcessSimulationEvaluator to scaled SLSQP and IPOPT workflows with native margins, infeasibility restoration, Pareto fronts, shadow-price checks, and discrete brownfield upgrade packages.\n", - "* **Advanced** - [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; share speed, power, map, and custom capacity constraints across typed operating-point results, bottleneck analysis, and pressure-boundary optimization; screen driver upgrades and declining inlet-pressure strategies.\n", - "* **Intermediate** - [Capacity and bottleneck analysis](process/capacity_and_bottleneck_analysis.ipynb): use utilization snapshots to identify process constraints.\n", - "* **Intermediate** - [Digital twin model vs measurement](process/digital_twin_model_vs_measurement.ipynb): build, calibrate, validate, and monitor a compressor digital twin with synthetic plant measurements.\n", - "* **Intermediate** - [Hydrate, wax, and water margin screening](flowassurance/hydrate_wax_and_water_margin_screening.ipynb): screen operating margins before detailed flow-assurance analysis.\n", - "* **Intermediate** - [Gas turbine emissions and process power](power/gas_turbine_emissions_and_process_power.ipynb): preserve the original compressor/emissions screen, then use current `GasTurbineCatalog`, `GasTurbineUnit`, ambient, degradation, and native compressor-power-consumer APIs.\n", - "* **Intermediate** - [Standards-based gas-line and relief screening with NeqSim](standards/standards_based_engineering_checks.ipynb): calculate fluid properties, line velocity, compressible pressure profiles, independent Darcy checks, preliminary choked-gas relief area, and a reusable screening workflow without claiming design-code compliance.\n", - "* **Advanced** - [Agentic process simulation with NeqSim](AI/agentic_neqsim_workflow_demo.ipynb): discover and safely address process variables, evaluate guarded operating cases, audit balances, map an operating envelope, and optimize a constrained gas-conditioning workflow with the native ProcessAutomation API." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9VqtmS_MpS6M", - "language": "markdown" - }, - "source": [ - "# Table of Contents\n", - "\n", - "## Complete oil and gas value chain\n", - "* [The complete oil and gas value chain: governed models, handoffs, and decisions](valuechain/complete_oil_and_gas_value_chain_with_neqsim.ipynb): connect eleven lifecycle stages through governed model identities and coherent realizations; keep laboratory data and specialist PVT software primary while public-PyPI NeqSim supports PVT and powers connected separation, compression, cooling, SURF, ISO 6976, emissions, deterministic and probabilistic economics, RAM, and explicit decision gates.\n", - "* **Advanced** - [Geo to market on the Norwegian Continental Shelf](valuechain/norne_geo_to_market_full_workflow.ipynb): carry pinned published Norne data through static-property QC and upscaling, a transparent reservoir forecast, a 60-realization FMU prior, four ES-MDA updates and an ERT contract; preserve realization identity through well and SURF hydraulics, hydrate, cooldown, wax, erosion and slug screens, a real NeqSim separation and export-compression process, ISO 6976 gas quality, facility feedback, emissions, market uncertainty, NPV and sensitivity analysis.\n", - "* **Advanced** - [Early-phase subsurface-to-flow-assurance data handoff](valuechain/early_phase_subsurface_to_flow_assurance_handoff.ipynb): normalize ECLIPSE/OPM summary data into a unit-explicit contract, preserve coherent realization identities, select representative uncertainty cases, run NeqSim well and TwoFluidPipe screens, and export traceable choke, MPFM and topside handoffs for automated discipline and reporting agents.\n", - "\n", - "## Fundamentals of NeqSim\n", - "* [Create and validate a NeqSim fluid from Eclipse PVT input](PVT/readEclipseFormat.ipynb): import a self-contained seven-lump SRK deck, audit properties and phase/component closure, test pressure and temperature sensitivities, verify XML round-trip identity, and connect the fluid to a high-pressure separator.\n", - "* [Thermodynamic and physical properties with NeqSim](thermodynamics/readproperties.ipynb): phase-aware equilibrium and transport properties, state sensitivities, validation, and a connected separation-and-compression application.\n", - "* [Traceable NeqSim parameter database and model audit](PVT/parameter_database.ipynb): inspect packaged component and binary-interaction data with read-only SQL, reconcile live SRK parameters, screen local interaction-parameter and pressure sensitivities, and connect the model to a compressor.\n", - "* [Controlled custom parameters and local model overlays](PVT/parameter_database2.ipynb): validated component and binary-interaction overrides without global database mutation.\n", - "* [Compare to experimental data and parameter fitting](https://github.com/equinor/neqsim-parameterfitting)\n", - "* [ThermoML model accuracy and parameter tuning with NeqSim](thermodynamics/thermoml_model_accuracy_and_parameter_tuning.ipynb): parse DOI-specific density and VLE records with ThermoMLPy, audit property provenance, test held-out model accuracy, and tune a P\u00e9neloux volume correction and PR binary interaction parameter.\n", - "\n", - "## Natural Gas Statistics\n", - "* [How natural gas is sold to Europe](gasvaluechain/european_gas_sales_market_foundation.ipynb): connect consumer procurement, shipper and TSO transport, producer netback, ISO 6976 energy settlement, NeqSim export compression, capacity dispatch, nominations, imbalance, hedging, and auditable cash closure.\n", - "* [Natural gas and the World Energy Outlook: evidence, scenarios, LNG supply, and NeqSim](gasvaluechain/energystatistics.ipynb): connect WEO 2025 and current IEA evidence to reproducible scenario stress tests, ISO 6976 gas quality, multistage compression, balances, sensitivities, and lifecycle boundaries.\n", - "* [Use of natural gas (history, present and future)](gasvaluechain/useOfNaturalGas.ipynb)\n", - "* [Natural gas in the energy transition: evidence, scenarios, and NeqSim (2025\u20132050)](gasvaluechain/EneregyTransition.ipynb): connect current IEA evidence to ISO 6976 fuel quality, a three-stage compression process, methane intensity, carbon capture, and lifecycle sensitivities.\n", - "\n", - "\n", - "## Thermodynamics\n", - "* [The laws of thermodynamics](thermodynamics/LawsOfThermodynamics.ipynb): verify equilibrium, state functions, energy balances, entropy generation, reversible compression, exergy, and limiting cases.\n", - "* [CO\u2082 low-temperature compression, relief, and depressurization](thermodynamics/CO2_low_temperature_compression_relief_and_depressurization.ipynb): compare EOS-CG, GERG-2008, PR, and SRK with Span\u2013Wagner; map impurity-sensitive phase behaviour; simulate staged compression; and screen isenthalpic relief, HEM critical flow, and transient blowdown temperatures.\n", - "* [Energy balance for closed and open systems](thermodynamics/EnergyBalance.ipynb)\n", - "* [Equations of State](thermodynamics/EquationsOfState.ipynb)\n", - "* [From fundamental interactions to SAFT-VR Mie parameters and NeqSim](thermodynamics/saft_vr_mie_from_fundamental_models.ipynb): execute PySCF dimer calculations, SciPy mapping, FEASST Mie Monte Carlo, SGTPy association and binary phase equilibrium, teqp validation, NeqSim parameter injection, uncertainty propagation, and methane compression.\n", - "* [From molecular hydrogen bonds to SAFT-VR Mie association and NeqSim](thermodynamics/saft_vr_mie_association_from_fundamental_models.ipynb): derive water 4C site topology, hydrogen-bond energy, and NeqSim kernel volume from counterpoise-corrected molecular calculations; benchmark Dufal association with teqp; verify NeqSim mass action and Helmholtz identities; and propagate the mapped parameters to water properties and heater duty.\n", - "* [Phase equilibrium](thermodynamics/PhaseEquilibrium.ipynb)\n", - "* [Flash Calculations and Rachford-Rice](thermodynamics/RachRice.ipynb)\n", - "* [Advanced TPflash algorithms](thermodynamics/c1_co2_h2s_flash_test.ipynb)\n", - "* [Thermodynamic property charts and connected process paths](thermodynamics/ThermoPropertyCharts.ipynb)\n", - "* [Physical porperty charts](thermodynamics/physiclaPropertyChart.ipynb)\n", - "* [Thermodynamc Cycles](thermodynamics/ThermodynamicCycles.ipynb)\n", - "* [Exergy analysis](thermodynamics/ExergyAnalysis.ipynb)\n", - "* [Chemical Equilibrium](thermodynamics/ChemicalEquilibrium.ipynb)\n", - "* [Water-ammonia thermodynamics and generator screening](thermodynamics/water_ammonia_properties.ipynb): calculate pure-fluid references, binary equilibrium, composition and pressure sensitivities, EOS uncertainty, and a connected heater-separator generator workflow with recovery, carryover, and closure checks.\n", - "\n", - "## Fluid mechanics\n", - "* [Fluid mechanics](fluidflow/FluidMechanics.ipynb)\n", - "* [Natural-gas pipeline linepack and operational flexibility](fluidflow/natural_gas_pipeline_linepack.ipynb): combine NeqSim real-gas properties and native PipeBeggsAndBrills pressure profiles with inventory integration, pack/unpack transients, deliverability limits, and operating-margin checks.\n", - "* **Advanced** - [\u00c5sgard Transport open route data to terrain-following NeqSim](fluidflow/asgard_transport_open_data_to_neqsim.ipynb): retrieve or replay public SODIR and EMODnet data, retain raw and 720 km engineering KP, create normalized five-point cross-sections and a transparent synthetic C5P candidate, and run a source-built segmented pressure/temperature model with flow sensitivity and refinement checks.\n", - "* **Advanced** - [Dynamic CO\u2082 and flow tracing from \u00c5sgard and Kristin to K\u00e5rst\u00f8](fluidflow/asgard_transport_dynamic_co2_tracing.ipynb): build NeqSim Java from an exact `master` commit, mix two gas sources, solve the inlet pressure required for a fixed K\u00e5rst\u00f8 boundary, compare first-order and TVD component transport with an explicit physical-dispersion sensitivity, refine grid/time resolution, and finish with a component-resolved gas/oil `TwoFluidPipe` conservation gate.\n", - "* [Norwegian NCS rich/dry gas network optimization](process/norwegian_ncs_gas_network_optimization.ipynb) \u2014 Gassco 2026 point-specific quality, NeqSim phase envelopes and ISO 6976, Beggs\u2013Brill capacity, onshore NGL recovery, a looped gas-network solve, reported export-rate validation, and future tie-in value-chain optimization.\n", - "* [NeqSim + OpenFOAM CFD with inline flow graphics](fluidflow/neqsim_openfoam_cfd.ipynb)\n", - "* [Tonal valve and piping noise: evidence-gated PEPR workflow](fluidflow/tonal_aeroacoustic_root_cause_gate.ipynb): combine a synthetic NeqSim gas letdown, valve-noise and vibration screens, a transient compressible finite-volume CFD/CAA verification, spectra, geometry and modal-data requirements, multi-hypothesis evidence synthesis, and an executable stop gate that prevents a tonal root-cause claim when installed evidence is missing.\n", - "* [Parametric CAD-to-CFD workflow with NeqSim, CadQuery, Gmsh, and OpenFOAM](fluidflow/neqsim_cadquery_gmsh_openfoam_workflow.ipynb): generate an exact STEP internal fluid volume and named STL boundaries, mesh CadQuery geometry with Gmsh physical groups, transfer NeqSim gas properties and flow into a three-dimensional OpenFOAM RANS case, render the actual mesh and CFD fields inline, validate geometry identity, mesh quality, convergence, and flow closure, and package reusable CAD, mesh, and case artifacts.\n", - "* [P&ID and mechanical datasheet to CAD and CFD with NeqSim](fluidflow/pid_datasheet_to_cad_cfd_neqsim.ipynb): normalize reviewed equipment, stream, nozzle, and instrument tags; verify dimensions with a calibrated-image QA record; calculate a three-phase CPA inlet with NeqSim; generate an exact separator gas-space STEP model with CadQuery; retain inlet, outlet, walls, and liquid-interface groups through Gmsh; run a real OpenFOAM RANS hydraulic screen; render the solved fields; and package the traceable design basis, CAD, mesh, case, and results.\n", - "* [Wet-gas centrifugal-compressor inlet with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_compressor_inlet_wet_gas.ipynb): generate a 3D suction CAD, solve the carrier gas, and screen liquid impaction and hot-wall evaporation.\n", - "* [Liquid-valve bubble formation with NeqSim and OpenFOAM](fluidflow/neqsim_openfoam_flashing_valve.ipynb): screen single-phase flow, local cavitation with pressure recovery, and sustained flashing.\n", - "* [Finite-rate pipeline evaporation and gas dissolution](fluidflow/pipeline_evaporation_and_gas_dissolution.ipynb): calculate droplet and film evaporation, gas dissolution into oil and water, heat and Maxwell\u2013Stefan mass transfer, slip, completion length, and incomplete phase transfer.\n", - "* [Single phase pipe flow](fluidflow/singlephaseflow.ipynb)\n", - "* [Multi phase pipe flow](fluidflow/multiphaseflow.ipynb)\n", - "* [Minimum-flow analysis for a long oil\u2013gas\u2013water flowline](fluidflow/minimum_flow_long_multiphase_flowline.ipynb): use the native `TwoFluidPipe` to locate terrain liquid accumulation, screen a multi-criterion minimum rate, test low-flow inventory growth and restart recovery, and quantify mesh sensitivity.\n", - "* **Advanced** - [Three-phase wellstream shutdown cooldown and executable OpenFOAM dead-leg screening](flowassurance/wellstream_shutdown_cooldown_to_openfoam_deadleg.ipynb): close a terrain-following gas\u2013oil\u2013water `TwoFluidPipe`, calculate axial no-touch time to an SRK-CPA hydrate-management boundary, then run an OpenFOAM 14 tee/dead-leg phase-settling and conjugate-cooldown screen with mesh, time-step, phase-volume, energy, cold-spot, hydrate-risk-volume, and heat-loss-feedback checks.\n", - "* [Flow induced vibrations (FIV)](fluidflow/FIVcalc.ipynb)\n", - "\n", - "## Heat and mass transfer\n", - "* [Non-equilibrium thermodynamics](thermodynamics/Nonequilibriumthermodynamics.ipynb)\n", - "* [Heat transfer](thermodynamics/heatTransfer.ipynb)\n", - "* [Mass transfer](thermodynamics/massTransfer.ipynb)\n", - "\n", - "## Thermodynamics of gas processing\n", - "* [PVT/density of gases](thermodynamics/density_of_gas.ipynb)\n", - "* [Phase envelopes of oil and gas](thermodynamics/Phase_envelopes_of_oil_and_gas.ipynb)\n", - "* [Water dew point calculation](thermodynamics/water_dew_point_claculations.ipynb)\n", - "* [Solubility of gases in water](thermodynamics/solubility_of_gases_in_water.ipynb)\n", - "* [Freezing point in LNG](thermodynamics/freezing_in_LNG.ipynb)\n", - "* [Phase behaviour of CO2](thermodynamics/PhaseBEhaviourCO2.ipynb)\n", - "* [Mercury in natural gas](thermodynamics/mercury_in_gas.ipynb)\n", - "* [H2S distribution in oil and gas processing](thermodynamics/H2Sdistribution.ipynb)\n", - "* [Simulation of fluids with water](thermodynamics/flash_with_salt_water.ipynb)\n", - "\n", - "\n", - "## Thermodynamic and Physical Properties\n", - "* [Thermodynamic properties](howtouseneqsim.ipynb)\n", - "* [Viscosty of fluids](thermodynamics/ViscosityOfFluids.ipynb)\n", - "* [Thermal conductivity of fluids](thermodynamics/ThermalConductivityOfFluids.ipynb)\n", - "* [Interfacial tension](thermodynamics/interfacialtension.ipynb)\n", - "* [Interface adsorption](thermodynamics/Interfacialadsorption.ipynb)\n", - "* [Diffusion coefficient](thermodynamics/diffusioncoefficients.ipynb)\n", - "\n", - "## Characterization of reservoir fluids\n", - "* [PVT of reservoir fluids](PVT/OilProperties.ipynb)\n", - "* [Characterization of a well fluid](PVT/fluidcharacterization.ipynb)\n", - "* [PVT experiments](PVT/PVTexperiments.ipynb)\n", - "* [Auditable PVT laboratory reports and fluid characterization](PVT/PVTreports.ipynb)\n", - "* [Complete PVT workflow: laboratory data, regression, separator optimization, and simulator export](PVT/pvt_workflow_from_lab_to_simulator.ipynb): characterize a reservoir oil, validate CCE and DLE, tune viscosity, optimize staged separation, and export reports, black-oil, and E300 models.\n", - "* [Black oil vs. computational simulation](PVT/blackoilvscomp.ipynb)\n", - "* [Eclipse-style PVT input and depletion-aware fluid recombination](PVT/eclipseFluidCharNeqSim.ipynb)\n", - "* [Characterization and plus fraction disitribution](PVT/GammaModel.ipynb)\n", - "* [Oil-assay cuts, pseudo-components, and fluid characterization](PVT/oilassay.ipynb)\n", - "\n", - "## Exploration and licence access\n", - "* [From opening an NCS area to an exploration discovery](fielddevelopment/ncs_area_opening_licensing_exploration_to_discovery.ipynb): follow the Norwegian opening and licensing framework through pre-qualification, licence groups and work obligations; screen synthetic public and MCS-style map layers; quantify play and prospect chance, probabilistic volumes and drilling value; characterize a hypothetical discovery sample in NeqSim; and hand an auditable discovery package to the reservoir-to-OPM Flow workflow.\n", - "* [NCS spatial history, discoveries, and future potential](fielddevelopment/ncs_spatial_history_discoveries_and_future_potential.ipynb): retrieve live SODIR discoveries, fields, wildcats, licences, plays, facilities, pipelines, and resource tables; reconstruct the staged northward development of the shelf; compare sea-area learning and opportunity archetypes; rank undeveloped discoveries with explicit limitations; and screen selected gas tie-backs with a source-built NeqSim Java master.\n", - "\n", - "## Reservoir simulations\n", - "* [Open SPE9 subsurface data with XTGeo, XTGeoViz, and NeqSim](reservoir/xtgeo_spe9_subsurface_to_neqsim.ipynb): load checksum-pinned synthetic SPE9 grid and restart properties with XTGeo; audit field-to-SI units; render maps and spaced candidate columns with XTGeoViz; parse the public PVTO deck; quantify STOIIP uncertainty; and pass rate scenarios into a composable NeqSim separation, cooling, and compression model with explicit conservation and capacity checks.\n", - "* [Introduction to reservoir simulations](reservoir/reservoirsimulation.ipynb)\n", - "* [Rock and reservoir flow from pore space to field development](reservoir/rock_flow_pore_to_field_neqsim.ipynb): use PoreSpy, OpenPNM, GSTools, SciPy, and current NeqSim Java master to connect digital rock, capillary invasion, pore-network permeability, SCAL, heterogeneous finite-volume waterflooding, well productivity, injection and water-handling constraints, surface separation, and OPM Flow handoffs.\n", - "* [A simplified reservoir simulation model](reservoir/simplereservoir.ipynb)\n", - "* [Composition gradient in a gas reservoir](reservoir/compositiongrad.ipynb)\n", - "* [NeqSim black-oil tables, OPM Flow reservoir simulation, and characterized process feeds](reservoir/neqsim_opm_flow_blackoil_coupling.ipynb): characterize a C20+ reservoir oil, generate Flow-compatible PVTO/PVDG/PVTW tables, run a 10 \u00d7 10 \u00d7 3 depletion and water-injection case, visualize the active-cell mesh and restart pressure/saturation states, reconstruct time-varying NeqSim well streams, and connect the maximum-load case to separation, compression, cooling, and oil letdown.\n", - "* [From seismic, samples, and petrophysics to an OPM Flow production forecast](reservoir/subsurface_data_to_opm_flow_production_forecast.ipynb): generate synthetic SEG-Y, LAS, RCAL/SCAL, pressure, and fluid-sample evidence; interpret petrophysics; build and upscale a seismic-guided geological model; generate all NeqSim PVT, grid, saturation-function, well, control, and schedule inputs; run low/base/high OPM Flow cases; and validate files, volumes, pressure, rates, and forecast results.\n", - "* **Advanced** - [FMU on the Norwegian Continental Shelf: Norne data to reservoir simulation, ERT and NeqSim](reservoir/fmu_norne_subsurface_to_facilities_workflow.ipynb): download a pinned public Norne model revision; parse and quality-control grid, petrophysical and well data; upscale to a transparent two-phase finite-volume reservoir model; run a base forecast; update uncertain reservoir parameters with ES-MDA and an ERT-ready case; propagate Q10/Q50/Q90 rates through a real NeqSim separation and compression process; apply facility constraints; and close the feedback loop to the reservoir controls.\n", - "* [ERT + NeqSim integrated field-development uncertainty](reservoir/ert_neqsim_integrated_field_development.ipynb): run 24 coupled reservoir, well, SURF, NeqSim process, and economic realizations with ERT; quantify P10/P50/P90 value, Q10/Q50/Q90 facility loads, capacity-exceedance probabilities, and dominant uncertainty drivers; and select internally consistent representative cases.\n", - "* [Stochastic reservoir-to-market optimization](reservoir/stochastic_reservoir_to_market_optimization.ipynb): preserve twelve internally consistent ERT, OPM Flow, NeqSim Java master, process, market, and economic realizations while optimizing well chokes, compressor configuration, host holdback, and common upgrade timing with Pyomo/HiGHS; rank complete P90/P50/P10 outcomes and quantify VSS and EVPI.\n", - "* [Discovery 1 NCS tie-in decision with OPM Flow and NeqSim master](reservoir/ncs_discovery_1_tie_in_opm_neqsim_master.ipynb): a guided neutral-labelled, composite open-data teaching case with learning objectives, 52 equations, interpretation prompts, 12 exercises, and a glossary. It uses public SODIR/FactMaps information, a public E300 analogue, OPM Flow, and NeqSim to explain reservoir-to-host coupling, holdback, utilization, and uncertainty. It contains no internal project data and is not a basis for real development decisions.\n", - "\n", - "## Wells\n", - "* [Introductin to oil and gas wells](well/wellcalcs.ipynb)\n", - "* [Rock-derived well productivity and injectivity with OPM Flow and NeqSim](well/rock_derived_productivity_injectivity_opm_neqsim.ipynb): derive permeability-thickness and skin from well-test evidence, generate SCAL with pyscal, run OPM Flow producer, injector, and bubble-point depletion cases, extract guarded pressure-to-rate curves with resdata, check voidage and fracture margin, and hand the inflow relationship to NeqSim wellbore and separation models.\n", - "* [Well nodal analysis and ECLIPSE VFPPROD lift curves](well/well_nodal_analysis_and_eclipse_vfp.ipynb): calculate gas-well IPR and VLP curves with NeqSim, solve and independently verify their operating-point crossing, screen tubing-head pressure, tubing size, and reservoir depletion, generate a rate\u2013THP lift-curve family, and export a validated five-dimensional VFPPROD include.\n", - "* [Integrated wells and production technology](well/integrated_wells_drilling_completion_lift_injection_intervention.ipynb): connect minimum-curvature drilling geometry, NeqSim well construction and cost, completion sensitivity, artificial-lift screening, water injection, well-integrity evidence, and acid intervention as one governed lifecycle.\n", - "\n", - "## Production technology\n", - "* [Production technology with NeqSim](production/production_technology.ipynb)\n", - "* [Well chemistry and water management with NeqSim](production/well_chemistry_and_water_management.ipynb)\n", - "* [Produced-water, sand, and production-chemicals management](production/produced_water_sand_chemicals_management.ipynb): connect three-phase separation, water quality, hydrocyclone/deoiling, discharge constraints, scale, corrosion, hydrate and wax inhibitor boundaries, MEG/TEG recovery, sand erosion, chemical consumption, and emissions.\n", - "\n", - "## Subsea facilities\n", - "* [Subsea production equipment and system screening with NeqSim](subseaequipment/subseaequipment.ipynb)\n", - "* [Simulation of subsea processes with NeqSim](subseaequipment/subsea_process_simulation.ipynb)\n", - "* [Multiphase flow of reservoir fluids with NeqSim](subseaequipment/multiphase_reservoir_fluid_flow.ipynb)\n", - "\n", - "## Unit Operations\n", - "* [Three-phase production separators with NeqSim](process/Separators.ipynb)\n", - "* [Heat Exchangers](process/heatexchangerDescription.ipynb)\n", - "* [Compressors and expanders](process/GasCompressors.ipynb)\n", - "* [Turboexpander-compressors](process/TurboExpanderCompressor_Example.ipynb)\n", - "* [Compressor curves and operating-envelope calculations](process/compressor_curves_and_compressor_calculations.ipynb)\n", - "* [Pumps](process/pumps.ipynb)\n", - "* [Valves](process/valves.ipynb)\n", - "* [Manifolds and pipes](process/manifoldsandpipes.ipynb)\n", - "* [Absoprtion](process/absorption.ipynb)\n", - "* [Adsorption](process/adsorption.ipynb)\n", - "* [Distillation](process/distillationoilgas.ipynb)\n", - "* [Mass transfer unit operations](process/masstransferMeOH.ipynb)\n", - "\n", - "## Process simulation\n", - "* [Reservoir-fluid tuning to a reference-process GOR with NeqSim](process/reservoirGORprocesstuning.ipynb): tune a bounded gas endmember against a four-stage separation train, close mass and energy balances, and quantify target and process-definition sensitivities.\n", - "* [Multistage oil stabilization with NeqSim](process/Simulationofanoilstabilizationprocess.ipynb): characterize a synthetic reservoir fluid, solve three equilibrium flash stages, close total/component/energy balances, screen separator capacity, and quantify volatility\u2013recovery trade-offs.\n", - "* [Simulation of a TEG dehydration process](process/simulationTEG.ipynb)\n", - "* [Reporting simulation results and report using field/SI units](process/processreportsandunits.ipynb)\n", - "* [Process simulation using neqsim](process/comparesimulations.ipynb)\n", - "* [Comparsion of process simulation using neqsim, UNISIM and DWSIM](process/comparesimulations2.ipynb)\n", - "* [Integrated oil stabilization, gas recompression, TEX, and NGL stabilization](process/oil_and_gas_Process_with_ngl_stabilizer_and_tex_process.ipynb): build a synthetic rich-fluid case through four-stage stabilization, three-stage flash-gas recompression, hydrocarbon-dew-point cooling, turboexpansion, a MESH-residual NGL stabilizer, product-quality checks, balances, and independent operating scenarios.\n", - "* [MEG regeneration and reclamation](process/MEGprocess.ipynb)\n", - "* [Development of large process models - use of sub-models](process/demo_field_process_model.ipynb)\n", - "\n", - "## Dynamic process simulations\n", - "* [Simulations of dynamic operation of a separator](process/dynamicsimul.ipynb)\n", - "* [Dynamic compressor calculations: speed response, pressure control, anti-surge, combined control, and startup](process/dynamiccompressor.ipynb): executable five-scenario workbook using current NeqSim thermodynamics, explicit two-volume mass balances, bounded PI and recycle controls, and engineering audits.\n", - "* [Dynamic compressor discharge-volume transient and PI flow control with NeqSim](process/dynamic_compressor_discharge_volume_control.ipynb): current complementary real-gas transient and controller example.\n", - "* [Dynamic compressor maps and anti-surge control](process/dynamiccompressor_dyn.ipynb): generate a five-speed chart with surge and stone-wall boundaries, compare native `AntiSurge` strategies, run a closed-loop turndown and recovery study, and preserve the five original connected-process transient workflows.\n", - "* [Integrated dynamic SURF\u2013topside\u2013control simulation](process/integrated_dynamic_surf_topside_control.ipynb): couple the corrected native `TwoFluidPipe` transient outlet process stream, terrain accumulation, and closed-ledger Lagrangian slug tracking to a dynamic receiving separator, compressor map, anti-surge recycle, stonewall arbitration, filtered pressure control, and fail-closed continuity and multiphase-flux audits using NeqSim Java built from `master`.\n", - "* [Custom external unit operation in dynamic simulation](process/dynamic_external_unit_operation.ipynb): implement a Python heater with thermal inertia, integrate it with NeqSim streams and equipment, and validate its energy balance and analytical response.\n", - "* [Pure component operations](process/singlecomponent.ipynb)\n", - "* [Pure component dynamic operations](process/singlecomponent_dyn.ipynb)\n", - "* [Dynamic control of an oil and gas separator](process/dynsep.ipynb)\n", - "* [Dynamic process simulation using reinforcement learning](process/RL_Process_Control.ipynb)\n", - "\n", - "## Gas processing design\n", - "* [Design of a gas-liquid separator](process/gas_oil_separation.ipynb)\n", - "* [Design of a TEG-dehydration process](process/TEGdehydration.ipynb)\n", - "* [Design of a shell and tube heat exchanger](process/heatexchanger.ipynb)\n", - "* [Offshore topside gas-processing train screening with NeqSim](process/topsideprocess.ipynb): cool and separate a synthetic well fluid, stabilize liquid, compress and cool export gas, close balances, and screen equipment capacity and operating sensitivities.\n", - "* [NGL extraction and fractionation with NeqSim](process/NGLextractionprocess.ipynb): model a characterized feed through cooling, expansion, scrubbing, heat recovery, MESH-residual fractionation, export compression, balance checks, product-quality calculations, and operating sensitivity.\n", - "* [Natural-gas compressor trains with NeqSim](process/GasCompressorTrain.ipynb): compare shortcut, detailed-EoS, Schultz, SRK, GERG-2008, and map-based methods; build two-stage compression, generate lift curves, and evaluate recycle-based antisurge control.\n", - "* [Automated process design in NeqSim](process/automatedprocessdesign.ipynb): build and audit a valve-cooler-separator-compressor flowsheet, screen 42 design points, apply constraints, trace a Pareto frontier, and export an automation snapshot.\n", - "* [Engineering calculations using the Python Fluids package](https://fluids.readthedocs.io/)\n", - "* [Engineering calculations using the Python heat transfer package](https://ht.readthedocs.io/)\n", - "* [Calculation of flow in fittings, valves, orifice plates etc.](process/fluidsandneqsim.ipynb)\n", - "* [NeqSim-connected separator sizing in SI units](process/sepsioze.ipynb): build a four-stage production process, extract phase properties, preserve and refresh seven vertical and horizontal two- and three-phase sizing cases, validate balances, and screen throughput and temperature sensitivities.\n", - "* [Gibbs reactor](reactions/GibbsReactor1.ipynb)\n", - "\n", - "## Downstream processing\n", - "* [How crude oil is sold to Europe](gasvaluechain/european_crude_oil_sales_foundation.ipynb): follow a synthetic North Sea cargo through quality, net standard quantity, quotation-period pricing, terminal and tanker logistics, buyer value, producer netback, and cash reconciliation.\n", - "* [Oil-refinery simulation foundation with NeqSim](process/neqsim_oil_refinery_simulation_foundation.ipynb): connect a synthetic TBP assay to a current-source NeqSim refinery front end, component and energy closure, cut recoveries, heat recovery, direct emissions, temperature sensitivity, margin, and crude-slate optimisation.\n", - "* [Crude-oil preflash and atmospheric flash-zone screening with NeqSim](process/oilrefineries.ipynb): characterize a synthetic TBP assay, model staged flash separation, close mass and energy balances, and screen operating sensitivities.\n", - "\n", - "## Refrigeration and heat pumps\n", - "* [Air and water cooling with NeqSim](process/air_and_water_cooling.ipynb)\n", - "* [Propane mechanical refrigeration with NeqSim](process/MechanicalCooling.ipynb): build and validate a vapour-compression cycle, balances, COP, scale-up, and operating sensitivities.\n", - "* [Propane heat-pump performance and operating limits with NeqSim](process/heatpumps.ipynb): calculate heating COP, source and sink sensitivities, seasonal performance, and preliminary 1 MW utility sizing.\n", - "\n", - "## LNG (liquified natural gas)\n", - "* [LNG process efficiency: SMR, C3MR, DMR, and nitrogen expansion](process/LNG_Liquefaction_Processes.ipynb)\n", - "* [Ship transport and LNG (LNG ageing)](process/lngageing.ipynb)\n", - "\n", - "## Hydrogen\n", - "* [Thermodynamic and physical properties of hydrogen](thermodynamics/ThermodynamicsOfHydrogen.ipynb)\n", - "* [Hydrogen production by reforming, shift, cooling, separation, and compression](thermodynamics/productionOfHydrogen.ipynb): preserve the original pre-reforming, thermochemistry, oxidation, and partial-oxidation cases; solve current NeqSim Gibbs equilibrium; audit balances and sensitivities; and connect syngas to a composable downstream process.\n", - "* [Hydrogen pipeline transport and compression with NeqSim](hydrogen/transportOfHydrogen.ipynb): preserve the original pure-H\u2082 property, two-stage compression, 500 km pipeline, profile, and ISO 6976 examples; add property-model comparison, mass and energy audits, diameter and throughput sensitivities, and seven inspected figures.\n", - "* [Liquefaction of hydrogen](hydrogen/liquefaction_of_hydrogen.ipynb)\n", - "\n", - "## Methanol\n", - "* [Thermodynamics of methanol](thermodynamics/ThermodynamicsOfMethanol.ipynb)\n", - "* [Physical properties of methanol](thermodynamics/PhysicalPropertiesOfMethanol.ipynb)\n", - "* [Production of methanol](reactions/Methanol_Production.ipynb)\n", - "\n", - "## Ammonia\n", - "* [Thermodynamics of ammonia](thermodynamics/ThermodynamicsOfAmmonia.ipynb): pure-ammonia phase equilibrium, public-property validation, model sensitivity, and process heating.\n", - "* [Physical properties of ammonia](thermodynamics/PhysicalPropertiesOfAmmonia.ipynb): phase-aware caloric and transport properties, validation, sensitivities, heat-transfer screening, and process integration.\n", - "* [Production of ammonia from natural gas](reactions/blue_ammonia_production.ipynb): steam reforming, water-gas shift, CO2 capture, compression, synthesis, and product recovery.\n", - "* [Ammonia as a refrigerant](process/ammonia_refrigeration.ipynb): saturation properties, vapour-compression cycle, composable process model, balances, and operating sensitivities.\n", - "* [Power production from ammonia](power/ammonia_power_generation.ipynb): atom-balanced combustion products, Brayton-cycle streams, power and efficiency balances, sensitivities, and emissions limitations.\n", - "\n", - "## Process safety\n", - "* Simulation of process blow down\n", - "* [Facilitator-ready HAZOP workshop with NeqSim](process/hazop_workshop_with_neqsim.ipynb): build a calculated inlet-separation and export-compression process; discover native nodes; apply facilitator-reviewed IEC 61882 deviations; quantify blocked outlet, compressor temperature, and gas blow-by; rank risk and produce an action register.\n", - "* [Loss-of-containment consequence analysis with NeqSim](process/loss_of_containment_consequence_analysis.ipynb): calculate transient source terms, choked-flow validation, 0.5 LFL and H2S endpoints, jet-fire radiation, VCE overpressure, and effect envelopes.\n", - "* [Barrier performance and ESD response-time verification with NeqSim](process/barrier_performance_and_esd_response_time.ipynb): derive process safety time; verify native detection, logic, and final-element timing; build cause-and-effect, evidence-linked performance standards, SCEs, and a barrier register; screen impairments.\n", - "* [DEXPI 2.0 to safety-study workflow with NeqSim](process/dexpi_safety_study_workflow.ipynb): export a calculated process to DEXPI 2.0; audit equipment, piping, instrumentation, design conditions, and safety semantics; build HAZOP nodes, C&E records, and a cross-study readiness matrix; retain missing SIS metadata in a controlled register.\n", - "* [Integrated SIL, ESD, HIPPS, and depressurization safety study](process/integrated_sil_esd_hipps_depressurization_study.ipynb): connect LOPA and native SIL verification to 2oo3 HIPPS voting, ESD timing, real-gas pressure rise, cold and fire blowdown, simultaneous flare load, and header-Mach sensitivity.\n", - "* [ESD, PSV, and controlled gas depressurization with NeqSim](process/ESD_PSV_Process_Safety_Demo.ipynb): calculate SRK gas properties, native PSV hysteresis and API 520 screening, blocked-outlet protection, manual and PSHH-triggered ESD blowdown, ISO 5167 orifice flow, balances, and blowdown-orifice trade-offs.\n", - "* [ESD system, alarm and flare](process/ESD_Fire_Alarm_System_Tutorial.ipynb)\n", - "* [Alarm handling in NeqSim](process/Alarm_Handling_NeqSim.ipynb)\n", - "* [Use of process simulators and NeqSim for process safety design](process/neqsim_process_safety_design.ipynb)\n", - "* [High Integrity Pressure Protection System (HIPPS) and process simulation](process/HIPPS_Safety_Simulations.ipynb)\n", - "* [Bow-tie, LOPA, and safety-instrumented risk analysis with NeqSim](process/bowtie_lopa_sif_risk_analysis.ipynb): connect an SRK inlet-separation process to native bow-tie generation, SIF PFD/SIL verification, LOPA, proof-test sensitivity, conservation checks, and a combined risk-and-capacity screen.\n", - "\n", - "## Power production\n", - "* [Electrical engineering fundamentals for oil and gas operations](power/electrical_engineering_fundamentals_oil_gas.ipynb): connect NeqSim compressor and water-injection pump duties to three-phase power, motors and VFDs, load lists, transformers, cables, fault current, starting, protection, harmonics, reliability, and operating constraints.\n", - "* [NCS wind, battery, and gas-turbine electrification with NeqSim](power/ncs_wind_battery_gas_electrification.ipynb): build NeqSim from a pinned repository commit, derive an offshore process load, dispatch Hywind-scale synthetic wind and balancing gas, evaluate native battery storage, and screen direct CO\u2082, curtailment, ramps, reserve, and field-maturity sensitivities.\n", - "* [Process-coupled offshore electrification with NeqSim](power/process_coupled_offshore_electrification_study.ipynb): extend a real separation and recompression process with 70-to-160 bara export compression, derive flow-dependent electrical demand from solved equipment duties, and compare gas turbines, shore power, wind, battery storage, and hybrid operation.\n", - "* [Natural-gas combustion, burner devices, and NeqSim integration with Cantera](reactions/natural_gas_combustion_with_cantera.ipynb): compare fuel and oxidizer blends; boiler, furnace, low-NOx, gas-turbine, duct-burner, and flare cases; validate composition and mass closure; model staged combustion; and run a Cantera custom unit inside a NeqSim process.\n", - "* [Gas-fired power plants with NeqSim](power/Gas_fired_power_plants.ipynb): fuel quality, stoichiometric combustion, Brayton-cycle states, heat recovery, balances, direct CO2 intensity, and operating sensitivities.\n", - "* [Natural-gas combined-cycle power plant with NeqSim](power/combined_cycle_power_plant.ipynb): Brayton gas turbine, single-pressure HRSG, CPA water/steam Rankine cycle, balances, quality checks, and operating sensitivities.\n", - "\n", - "## CO2 removal and handling\n", - "* [Thermodynamic and physical properties of CO2 \u2014 model selection, phase envelope, depressurization, and export conditioning](thermodynamics/ThermodynamicandphysicalpropertiesofCO2.ipynb)\n", - "* [EOS-CG and GERG-2008 for CO2](thermodynamics/EOSCG_vs_GERG2008.ipynb)\n", - "* [CO\u2082-rich phase envelopes, solids, hydrates, and conditioning](thermodynamics/phaseenvelopesofCO2richmixtures.ipynb): compare pure CO\u2082, CO\u2082\u2013CH\u2084, and CO\u2082\u2013CH\u2084\u2013H\u2082S phase behavior; screen solid and hydrate boundaries; map properties; and connect the results to compression, cooling, throttling, separation, balances, and operating sensitivity.\n", - "* [Thermodynamics of CO2 rich gases and water](thermodynamics/CO2richandwater.ipynb)\n", - "* [CO\u2082 solubility, speciation, and MDEA gas treating](thermodynamics/CO2alkonolamines.ipynb): compare Electrolyte-CPA and Electrolyte-ScRK equilibrium, inspect aqueous speciation, screen loading, temperature, model, and solvent-rate sensitivities, and run a balanced stream\u2013mixer\u2013separator contact process.\n", - "* CO2 removal from natural gas\n", - "* CO2 removal from gas fired power plants\n", - "* [CO\u2082 compression, intercooling, and dense-phase pumping with NeqSim](process/CO2_compression.ipynb): build a three-stage PR-EOS compression train with intercooling, dense-phase pumping, injection conditioning, phase-envelope diagnostics, stage-count and intercooler sensitivities, mass and energy closure, and operating-limit checks.\n", - "* [CO\u2082 dehydration for pipeline transport with NeqSim](process/CO2_Dehydration_Pipeline.ipynb): map saturated water content, impurity phase behaviour, transport density, TEG absorption, circulation and purity sensitivities, hydraulic screening, and mass balances.\n", - "* CO2 depressurization\n", - "* [CO2 injection pipelines](fluidflow/CO2pipeline.ipynb)\n", - "* [CO2 chain from compression to pipeline](process/co2_chain_from_compression_to_pipeline.ipynb): build and validate staged compression, intercooling, dense-phase diagnostics, and an 80 km NeqSim pipeline design study.\n", - "* [CO\u2082 trace-acid formation, partitioning, and compression conditioning with NeqSim](thermodynamics/CO2reactions.ipynb): preserve the original acid, sulfur, and pH lessons; apply an oxygen-limited element-balanced formation screen; refresh Peng\u2013Robinson and Electrolyte-CPA results; and run a two-stage compression, intercooling, and separation sensitivity workflow.\n", - "* [CO2 and reactions in a recompressor train](thermodynamics/Co2_recompression.ipynb)\n", - "\n", - "## Gas and oil transport\n", - "* [Design of a gas pipeline](fluidflow/gaspipeline.ipynb)\n", - "* [Design of a multi-phase pipeline](fluidflow/twophasepipeline.ipynb)\n", - "* [Two-fluid transient, slug-flow, and S-riser modelling](fluidflow/two_fluid_transient_slug_flow.ipynb): compare `TwoFluidPipe` with Beggs\u2013Brill, map flow regimes, simulate pressure and liquid-inventory transients, and study an oil-and-gas wellstream in an S-riser.\n", - "* [Flexible multiphase flowlines and risers](fluidflow/flixiblepipes.ipynb): use current `PipeBeggsAndBrills` profiles, thermal modes, connected lazy-wave-riser legs, API RP 14E velocity screening, and diameter/rate design scenarios.\n", - "* [Transient multiphase flow with NeqSim's drift-flux model](process/transient_multiphase_flow_tutorial.ipynb): build an SRK gas-condensate fluid, simulate horizontal and terrain flow, inspect drift-flux closure and accumulation, exercise controlled slug diagnostics, validate a vertical-riser initial state, test mesh sensitivity, and connect a flow-step case to a receiving separator.\n", - "* [Water-hammer simulation with NeqSim](process/water_hammer_simulation_tutorial.ipynb): use the released `WaterHammerPipe` MOC solver for valve closure, pressure histories and envelopes, closure-time sensitivity, cavitation screening, elevation, material effects, and ESD design.\n", - "\n", - "## Flow assurance\n", - "* [Thermodynamics of natural gas hydrates \u2014 CPA equilibrium, MEG and salt inhibition, and arrival-process screening](thermodynamics/thermodynamics_of_natural_gas_hydrates.ipynb)\n", - "* [Wax appearance, model tuning, and flowline operability with NeqSim](thermodynamics/thermodynamicsOfWax.ipynb): characterize a reservoir fluid, calculate and tune wax precipitation, map WAT and wax fraction, and screen cooling and insulation scenarios along a segmented flowline.\n", - "* [Mineral-scale thermodynamics and flowline control with NeqSim](thermodynamics/ThermodynamicsOfMineralScale.ipynb): calculate Electrolyte-CPA aqueous speciation and saturation indices, screen water chemistry and incompatible-brine mixing, profile flowline risk, assess inhibitor dose, and couple multiphase hydraulics to deposition.\n", - "* [PVT property tables for multiphase flow simulators](thermodynamics/PVTtableGeneration.ipynb): generate and audit OLGA-style tables for wet gas, characterized condensate, and an Eclipse-described well fluid; inspect phase/property grids, qualify interpolation resolution, and retain engineering checks.\n", - "* [Top-of-line condensation and MEG carryover with NeqSim](process/topoflinecondensation.ipynb): use CPA fugacity equilibrium to saturate gas with MEG-water liquid, quantify cold-wall condensate, close flash balances, screen a cooling flowline profile, and compare inhibitor strengths.\n", - "* [MEG injection and evaporation of water](thermodynamics/MEG_injection_and_evaporation.ipynb): model SRK-CPA water/MEG equilibrium, hydrate inhibition, evaporation, and a validated saturation\u2013injection\u2013separation workflow.\n", - "* [Asphaltenes in oil and gas production](thermodynamics/Asphaltene_Modeling_Tutorial.ipynb)\n", - "\n", - "## Process control\n", - "* [Dynamic process control with NeqSim](process/process_control_with_neqsim_v2.ipynb): build native separator pressure and level loops, connect two-stage separation and flash-gas recompression, implement cascade and ratio control, compare PI tuning, and filter transmitter noise.\n", - "* [Reinforcement Learning integration](process/reinforcement_learning_integration.ipynb)\n", - "* [Data-driven separator pressure control with NeqSim](process/data_driven_control_strategies.ipynb): build an SRK wellstream, equilibrium separator, gas valve, and pressure transmitter; derive real-gas inventory and valve-capacity tables; apply deterministic Kalman estimation and constrained MPC; and validate disturbance rejection, control bounds, and mass closure.\n", - "* [Model predictive control](process/model_predictive_controller_examples.ipynb)\n", - "* [DEXPI standard](process/dexpi_demo.ipynb)\n", - "\n", - "# Process automation and logics imlementation\n", - "* [Process logic and safeguarding with NeqSim](process/NeqSim_Process_Logic_Demo.ipynb): connect an SRK-CPA wellstream and heated three-phase separator to pressure detectors, 2oo3 HIPPS voting, sequenced ESD actions, fire-and-gas detection, response scenarios, and engineering checks.\n", - "* [Process alarms, interlocks, and sequential logic around a NeqSim model](process/ProcessLogicIntegrated.ipynb): build a current NeqSim valve-and-separator flowsheet, implement priority, deadband, latching, delayed blowdown, reset permissives, and validate a real-gas vessel balance.\n", - "\n", - "## Oil and Gas metering and analysis\n", - "* [Multi phase measurments](process/MultiphaseflowMeasurement.ipynb)\n", - "* [Allocation of production](process/allocationoilandgas2.ipynb)\n", - "\n", - "## Gas and Oil specifications\n", - "* [Calorific value of natural gas (ISO6976)](gasquality/CalorificValueNaturalGas.ipynb)\n", - "* [Natural-gas blending and quality optimization](gasquality/gas_blending_quality_optimization.ipynb)\n", - "* [Oil quality specifications: assay characterization, API gravity, viscosity, vapor pressure, and stabilization](gasquality/oilqualityspecifications.ipynb): executable SRK workflow with ASTM D6377 screening, six technical figures, and a connected valve-conditioner-separator application.\n", - "* [Hydrocarbon dew point](gasquality/hydrocarbon_dew_point_of_natural_gas.ipynb)\n", - "* [Oil vapour pressure: TVP, VPCR4, and RVP](process/TVP_RVP_Study.ipynb): calculate method-specific vapour pressure, composition and temperature sensitivity, and stabilization-pressure trade-offs.\n", - "\n", - "## Process simulation using NeqSim\n", - "* [Simulation of oil stabilization](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/oilstabilizationprocess.ipynb)\n", - "* [Produced-water treatment with NeqSim](process/producedwatertreatment.ipynb): preserve the two legacy CPA separation studies and hydrocyclone video; quantify flare gas, dissolved hydrocarbons, and BTEX over pressure and temperature; use the released Electrolyte-CPA water builder and native hydrocyclone sizing, PDR, droplet-distribution, capacity, reject-flow, and oil-in-water screening APIs; and close the connected process mass balance.\n", - "* [Simulation of TEG dehydration process](https://colab.research.google.com/github/equinor/neqsimprocess/blob/master/example/TEGprocessHX.ipynb)\n", - "* [Simulation of MEG hydrate inhibition and regeneration](process/MEGwaterprocess.ipynb): CPA hydrate envelopes, cold liquid dropout, and rich-MEG regeneration balances.\n", - "* [Exergy analysis of oil and gas processes](process/exergyanalysisofoilprocess.ipynb): preserve the legacy offshore process example while building a self-contained three-stage stabilization and export train, closing mass, component, and energy balances, auditing entropy and exergy, ranking irreversibility, and screening separator-pressure trade-offs.\n", - "* [Process equipment weight screening with NeqSim](process/weightofoilprocess.ipynb): rebuild the original offshore HP/MP/LP stabilization process with current APIs, close gas/oil/water balances, calculate native equipment and discipline weights, inspect separator dimensions, and rerun capacity sensitivities.\n", - "* [Optimization of a field producing from both gas and oil reservoir](reservoir/optimizationofoilandgasproduction.ipynb)\n", - "* [Process flow diagrams driven by NeqSim results](process/process_flow_diagram.ipynb): characterize a rich wellstream, connect separation, gas compression, and liquid throttling, verify balances, render a calculated pyflowsheet SVG with embedded tables, and screen export pressure.\n", - "* [Integration of third party tools into neqsim process simulations](process/neqsimreaktoro.ipynb)\n", - "* [Adding a new unit operation using python](process/newunitoperation.ipynb)\n", - "* [Adding a ML based unit operation](process/heat_exchanger_ml_external_unit.ipynb)\n", - "\n", - "\n", - "##Integrated modelling reservoir, well, transport and process\n", - "* [Reservoir-to-market system curves with a capacity-designed topside](reservoir/reservoir_well_topside_market_system_curves.ipynb): connect one fluid from reservoir inflow through well and flowline lift, HP/MP/LP separation, flash-gas recompression, two-stage export compression, separator and scrubber sizing, complete compressor maps, reservoir-pressure nodal matrices, debottlenecking, depletion, and capacity-bounded ECLIPSE VFPPROD export.\\n* [Integrating reservoir and process simulations](reservoir/reservoirandprocess.ipynb)\n", - "* [Ensemble-based integrated reservoir and process modelling](reservoir/ensamble_based_modelling.ipynb): update a reproducible reservoir ensemble with production history, transfer posterior rates through an explicit ERT-compatible contract, run a NeqSim choke, cooler, three-phase separator, and compressor, and quantify bottleneck probability and constrained production.\n", - "\n", - "## NeqSim PVT and Process Simulation API\n", - "* [How to create an API using NeqSim and Python](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/python)\n", - "* [How to create an API using NeqSim and Java](https://github.com/EvenSol/NeqSim-Colab/tree/master/API/java)\n", - "* [NeqSim PVT API: executable property workflows](API/NeqSimPVTAPI.ipynb): build SRK, GERG-2008, and CPA fluids; run unit-aware TP flashes; characterize heavy fractions; retrieve named phase properties; compare gas models; sweep phase behavior; and validate closure and property trends.\n", - "* [NeqSim Process API: validated engineering calculation services](API/NeqSimProcessAPI.ipynb): build unit-aware request contracts for SRK-CPA TEG dehydration and a staged offshore process, return finite JSON-compatible results, reject invalid inputs, and screen independent operating scenarios.\n", - "\n", - "# Online process simulation\n", - "* [Online oil-stabilization simulation with NeqSim](process/onlineprocesssimulation.ipynb): build a PR well fluid, solve three-stage stabilization, recompress flash gas, update live inputs, run historian scenarios, and validate mass closure, product quality, power, and recycle convergence.\n", - "* [Implementing effective online process simulations](process/online_process_simulation_demo1.ipynb): build a four-stage stabilization, gas recompression, dew-point control, recycle, oil export, and produced-water process; compare cold, warm, and single-step online strategies over 30 points; inspect structured reports; and validate conservation, runtime, and provisional-result quality.\n", - "\n", - "## Process plant operation\n", - "* [Weather-aware gas processing and power generation with NeqSim](process/weatherandprocess.ipynb): preserve daily, hourly, station, and forecast workflows; calculate SRK air properties, export-compressor demand, turbine capacity, fuel, and direct emissions with deterministic weather fallbacks.\n", - "* Condition based monitoring\n", - "* [Condition-based monitoring of heat exchangers with NeqSim](process/heat_exchanger_condition_monitoring.ipynb): normalize historian data with native SRK and SRK-CPA streams, invert the two-stream HeatExchanger for effective UA, audit hot/cold duty consistency, detect fouling and sensor bias, verify cleaning recovery, and return reusable equipment-health snapshots.\n", - "\n", - "## Operations and asset management\n", - "\n", - "* [Facility lifecycle from commissioning to decommissioning](operations/facility_lifecycle_commissioning_to_decommissioning.ipynb): connect nitrogen properties, drying and inerting, hydrocarbon introduction, process ramp-up, shutdown inventory and low-temperature screening, late-life economic limits, cessation, P&A, removal schedules, waste and recycling, cost uncertainty, and emissions using public-PyPI NeqSim with Python.\n", - "* [Operations and asset management with NeqSim and Python](operations/operations_asset_management_with_neqsim.ipynb): connect source-built NeqSim process and failure consequences to censored reliability fitting, condition trends, maintenance-policy and shared-crew RAM simulation, availability, and uncertainty-weighted production accounting.\n", - "\n", - "## Materials in gas processing\n", - "* Material selection in gas processing\n", - "* [CO\u2082 corrosion, inhibition, and pipeline integrity](material/corrosionandthermo.ipynb): Electrolyte-CPA speciation, NORSOK M-506 screening, mechanistic inhibition, material selection, and coupled flowline integrity.\n", - "* [Elemental sulfur (S8) in natural gas processing](material/elemental_sulfur.ipynb)\n", - "* [Acid partitioning between gas, oil, and water](thermodynamics/formicacid_calculation.ipynb)\n", - "\n", - "## Emissions\n", - "* [CO2 emissions (scope 1, 2 and 3)](https://colab.research.google.com/drive/1Iwja5bKiP-fC2tS9O97-06j5XhqE0lJK#scrollTo=TgILfzXSzDAP)\n", - "* [Hydrocarbon emissions from processing and transport](emissions/Hydrocarbon_emissions.ipynb): use CPA phase equilibrium, high-pressure separation, isenthalpic letdown, low-pressure flash-gas accounting, methane/VOC breakdown, operating maps, and mitigation scenarios.\n", - "* [Chemicals consumption (glycols/etc)](process/cemicalsconsumption.ipynb): use CPA phase equilibrium and a connected SimpleTEGAbsorber to quantify equilibrium vapor loss, water removal, packing capacity, circulation sensitivity, and annual TEG make-up.\n", - "* [CO\u2082 emissions across the natural-gas value chain with NeqSim](emissions/CO2emsissionsinthevaluechain.ipynb): connect SRK separation, offshore and onshore compression, cooling, ISO 6976 gas quality, composition-derived carbon factors, Scope 1/2/3 boundaries, electrification scenarios, avoided-cost screening, and pressure\u2013power sensitivity.\n", - "\n", - "##Pipeline network optimization\n", - "* [Capacity-aware compressor optimization and field-life debottlenecking](process/neqsim_compressor_capacity_bottleneck_optimization.ipynb): generate all speed, head, and efficiency curves with surge and stonewall boundaries; use the same speed, power, map, and custom capacity constraints in typed operating-point results, bottleneck analysis, and maximum-throughput pressure-boundary optimization; quantify driver-upgrade value and production gains from declining inlet pressure.\n", - "* [Dry gas parallel-pipeline network optimization](process/PipeNetworkOptim.ipynb): balance unequal branches, screen capacity and diameters, map throughput, and connect compression, cooling, splitting, pipe hydraulics, and delivery-node mixing.\n", - "* [Multiphase network modeling and optimization](process/MultiphaseOptim.ipynb): balance two three-phase well branches at a shared manifold, screen export bore and backpressure, model thermal export hydraulics, and close a receiving-separator mass balance.\n", - "\n", - "\n", - "## Machine learning techniques and artificial intelligence (AI) in gas processing simulation\n", - "* Use of Languge Models and NeqSim\n", - "* Machine learning and PVT\n", - "* [Machine learning and process simulation](process/Machine_learning_and_process_simulation.ipynb)\n", - "* [AI-assisted gas-processing simulation and design with NeqSim](process/AIgasprocessing.ipynb): generate audited choke\u2013cooler\u2013three-phase-separator\u2013compressor cases, fit and validate a transparent quadratic surrogate, screen constrained operation, verify AI recommendations in NeqSim, and demonstrate synthetic residual monitoring.\n", - "* [Machine-learning-driven heat exchanger unit operations](process/heat_exchanger_ml_external_unit.ipynb)\n", - "\n", - "## Innovative technologies combining AI technologies and NeqSim\n", - "* [Real-Time process monitoring for operational safety and compliance](AI/Real_Time_process_monitoring_for_operational_safety_and_compliance.ipynb)\n", - "* [NeqSim and Data Analytics using Seeq](AI/NeqSim_and_Seeq.ipynb)\n", - "* Optimization of process parameters to maximize efficiency and production\n", - "* Anomaly detection to detect unusual patterns or deviations in process data\n", - "* Integration of different data sources to analyze data from different sources (sensor data, simulation data, weather data and external environmental data) to provide a more holistic understanding of processes\n", - "* Digital twins used to simulate and optimize processes in a virtual setting before implementation in the real world\n", - "* Self-adjusting control systems that adjust themselves based on continuous feedback and changes in process data\n", - "* Predicting oil spills or gas leaks for environmental monitoring\n", - "* Virtual measurements in oprocess plants using neqsim and AI technologies\n", - "* [IoT and Industry 4.0](AI/IoT_and_Industry4.0_with_NeqSim.ipynb)\n", - "\n", - "\n", - "## Statistics\n", - "* [Statistical Sites on the World Wide Web](statistics/WorldStats.ipynb)\n", - "* [World oil and natural-gas production statistics connected to NeqSim](statistics/worldOilandGasProduction.ipynb): audit an embedded 2024 Energy Institute/Our World in Data snapshot, close world totals, calculate producer shares and grouped concentration, apply SRK and ISO 6976 gas quality, bridge energy to standard volume, and screen export compression and direct-combustion scenarios.\n", - "* [Oil and Gas Price Statistics and Analysis](statistics/OilandGasPriceStatistics.ipynb)\n", - "* [Norwegian Continental Shelf production statistics and NeqSim export-process capacity screening](statistics/ProductionfromNorwegianContinentalShelf.ipynb)\n", - "* [CO2 emissions from oil and gas production](statistics/CO2emissionsNorwegianContinentalShelf.ipynb)\n", - "* [Statistics of CO2 in atmosphere](statistics/CO2inatm.ipynb)\n", - "\n", - "## Energy System Modelling\n", - "* [Energy system modelling](https://oemof.org/)\n", - "* [Thermal Engineering Systems](https://tespy.readthedocs.io/en/main/index.html)\n", - "* [A Sector-Coupled Open Optimisation Model of the European Energy System](https://pypsa-eur.readthedocs.io/en/latest/)\n", - "* [Examples of NeqSim in Energy System Modelling](energyopt/ThermalEnergyAndNeqSim.ipynb#scrollTo=C7-qUy51VbFs)\n", - "* [PyPSA-Earth example for Norway](energyopt/PyPSA-Earth_Norway.ipynb)\n", - "\n", - "## Economic analysis\n", - "* [NeqSim-connected NCS gas-field economy analysis](fielddevelopment/economy.ipynb): connect SRK separation and export compression, native mechanical-design cost estimation, declining field pressure and production, corrected tax-rate algebra, cash flow, NPV, IRR, payback, break-even, and commercial sensitivities.\n", - "* [Integrated process cost estimation and economic screening](fielddevelopment/process_cost_estimation_and_economics.ipynb): connect a gas-process simulation to mechanical design, CAPEX, OPEX, location/material/CEPCI sensitivities, and financial metrics.\n", - "\n", - "## Earth Tools and Metocean Data\n", - "* [Metocean and field development with NeqSim](fielddevelopment/metocean_and_field_development.ipynb): use a checksum-controlled public NORA10 teaching series with metocean statistics, weather windows, extremes, joint contours, spectra, tides, and NeqSim seasonal tie-back cases.\n", - "* [Earth tools and field development with NeqSim](fielddevelopment/earth_tools_and_field_development.ipynb): combine CRS-safe GeoPandas vectors, raster bathymetry, exclusion-aware least-cost routes, network analysis, interactive mapping, NeqSim tie-back hydraulics, and host economics.\n", - "\n", - "## Integrated energy and emission calculations\n", - "* [Integrated NeqSim + eCalc field-life, compressor-map, and offshore-energy study](power/neqsim_ecalc_integrated_energy_emissions.ipynb): combine three-pressure separation and recompression, complete NeqSim-generated compressor maps with surge and stonewall boundaries, declining inlet-pressure field life, native wind and battery dispatch, and eCalc fuel, CO\u2082, capacity, recirculation, and intensity accounting.\n", - "\n", - "## Northern Lights CCS open-data calculations\n", - "The numbered series uses the Equinor Northern Lights Databricks Marketplace listing as the authoritative full-data boundary and small hash-checked Eos snapshots for credential-free execution. NeqSim owns thermodynamics, wells, pipelines, facilities, and operations; OPM Flow owns dynamic storage-reservoir simulation.\n", - "* [01 - Northern Lights open-data foundation and NeqSim model basis](fielddevelopment/northern_lights/01_northern_lights_open_data_foundation.ipynb): audit licensed Eos trajectory, interpreted formation, UCS, and public well-test evidence; build an exact source-master NeqSim runtime; calculate EOS-CG CO\u2082 and CPA water anchors, a frictionless hydrostatic injection screen, and Phase 1/2 capacity translations; then issue explicit NeqSim and OPM Flow handoffs.\n", - "\n", - "## Volve field calculations\n", - "This principal eight-part field-lifecycle collection uses the Equinor Volve Data Village listing as its authoritative measured-data boundary and carries versioned handoff contracts from one discipline to the next. It follows the [NeqSim Field Development and Operations book](https://equinor.github.io/neqsimhome/doc/field_development_and_operations/book_standalone.html) from SEG-Y and development framing through OPM Flow, production technology, facilities, constrained operations, late life, shutdown, and decommissioning. A clean teaching fallback is stored for reproducibility, but it is explicitly labelled and is not represented as measured Volve data. Run the numbered notebooks in order.\n", - "* [01 - Volve seismic, wells, and static model](fielddevelopment/volve/01_volve_seismic_wells_static_model.ipynb): inventory and select Marketplace SEG-Y and well assets; perform geometry, amplitude, horizon, depth-conversion, log, petrophysical, fluid-density, volumetric, and uncertainty screens; then write the geoscience-to-reservoir handoff.\n", - "* [02 - Volve PVT, black oil, and reservoir](fielddevelopment/volve/02_volve_pvt_blackoil_reservoir.ipynb): select PVT, Eclipse/OPM, and production assets; characterize the fluid with NeqSim SRK; generate black-oil/DLE properties; screen history, material balance, depletion, and OPM Flow acceptance; then write the reservoir forecast handoff.\n", - "* [03 - Volve wells, SURF, and flow assurance](fielddevelopment/volve/03_volve_wells_surf_flow_assurance.ipynb): connect trajectories and completions to productivity, injectivity, IPR/VLP, NeqSim wellstreams, pipeline hydraulics, thermal response, hydrate margins, cooldown, and the facility-inlet envelope.\n", - "* [04 - Volve facilities and processing](fielddevelopment/volve/04_volve_facilities_processing.ipynb): model inlet conditioning, three-phase separation, oil stabilization, gas compression and cooling with NeqSim; locate bottlenecks; and screen produced water, chemicals, utilities, emissions, availability, and safeguarding.\n", - "* [05 - Volve integrated field twin and decisions](fielddevelopment/volve/05_volve_integrated_field_twin.ipynb): assemble all discipline contracts; reconcile history and capacity ownership; test interventions, uncertainty, economics, emissions, and decision gates; and issue a traceable integrated field-evaluation handoff.\n", - "* [06 - Volve reservoir simulation and production history matching](fielddevelopment/volve/06_volve_reservoir_history_matching.ipynb): obtain and audit the Marketplace production workbook; calculate NeqSim PVT anchors; run and fit a communicating two-tank reservoir simulator to pressure, oil, gas, and water; test a holdout period; diagnose identifiability; propagate uncertainty; and expose an optional controlled OPM Flow acceptance path.\n", - "* [07 - Volve all wells, nodal analysis, gathering, and full SURF](fielddevelopment/volve/07_volve_all_wells_gathering_surf.ipynb): simulate every active producer with IPR, NeqSim-calibrated tubing VLP, choke and branch loss; solve two manifolds, trunks, a common riser, and host backpressure; verify the network in a composable NeqSim ProcessSystem; and evaluate integrity, late-life, outage, shutdown, cooldown, hydrate, and restart cases.\n", - "* [08 - Volve closed-loop full-field development and operations](fielddevelopment/volve/08_volve_closed_loop_field_development_operations.ipynb): convert the static handoff into development scenarios and producer/injector placement; generate OPM well schedules and an OPM-primary history-match ensemble; feed NeqSim well, SURF, separation, compression, water, export, and injection limits back into monthly reservoir controls; and calculate the economic limit, shutdown handoff, plugging inventory, removal sequence, and decommissioning uncertainty.\n", - "* [Compact Volve full-field precursor with OPM Flow and NeqSim](fielddevelopment/volve_full_field_development_opm_neqsim.ipynb): compare producer/injector layouts in a reduced teaching sector and connect reservoir states to PVT, wells, SURF, topsides, schedule, economics, and uncertainty.\n", - "\n", - "## Field development\n", - "* **Advanced** - [NCS resource classification and project maturation with NeqSim](fielddevelopment/ncs_resource_classification_with_neqsim.ipynb): explain RC0-RC9, F/A project categories, BOI/BOK/BOV/BOG/PDO transitions, low/base/high uncertainty and discovery chance; build a governed project register with pandas and NetworkX; derive marketable gas and condensate yields with a source-traceable NeqSim process; connect volumes to capacity-limited profiles, synthetic economics, public SODIR data contracts, and reusable CSV/JSON handoffs; and link the exploration-to-market notebook chain.\n", - "* [SEG-Y with segyio to field-development decisions with NeqSim](fielddevelopment/segyio_to_field_development_workflow.ipynb): create and round-trip a synthetic 3-D SEG-Y cube, derive similarity and fault masks, interpret top/base/thickness, propagate low/base/high STOIIP, screen wells and field-life capacity, run NeqSim three-phase separation and export compression, and map the evidence to DG0-DG4 handoffs and decisions.\n", - "* [Offshore facility concept selection with NeqSim](fielddevelopment/offshore_facility_concept_selection_with_neqsim.ipynb): compare nine facility concepts using infrastructure distance, water depth, metocean extremes, environmental loads, weather operability, NeqSim tie-back hydraulics and process simulation, hard technical gates, lifecycle economics, weighted MCDA, TOPSIS, sensitivity analysis, and Monte Carlo robustness.\n", - "* [Norwegian field and area development lifecycle screening with NeqSim](fielddevelopment/norwegian_field_and_area_development_with_neqsim.ipynb): connect synthetic depletion and well deliverability to SRK phase behavior, choke/cooler/separator/compressor process models, SURF hydraulics, capacity constraints, export-pressure concepts, brownfield tie-in holdback, direct compressor emissions, product screens, and discounted cash flow.\n", - "* **Advanced** - [NCS tie-in from reservoir to market: host holdback, quality, tariffs, and tax](fielddevelopment/ncs_tie_in_quality_tariff_tax.ipynb): integrate a source-traceable NeqSim gas-condensate process, ASTM D6377 liquid vapour pressure, ISO 6976 gas blending, boundary-specific public quality examples, dated August 2026 Gassco K/I/O tariff and booking-vintage screens, host-priority/firm-tie/pro-rata holdback allocation, accepted-production economics, simplified petroleum tax, uncertainty, and multidisciplinary decision gates.\n", - "* [Producer and injector well-count and placement workflow](fielddevelopment/well_count_and_placement_workflow.ipynb): screen counts from deliverability, injectivity, and voidage replacement; optimize robust discrete layouts with pymoo NSGA-II; verify four concepts in OPM Flow; and connect the selected concept to NeqSim PVT, wells, SURF, facilities, capacity, economics, and uncertainty.\n", - "* [Discovery 1 DG1 SURF and host-facility design with NeqSim](fielddevelopment/discovery_1_dg1_surf_facilities_design.ipynb): mature the neutral-labelled Discovery 1 reservoir-to-host handoff into a 50-deliverable educational DG1 learning package covering flow assurance, subsea architecture, pipeline sizing, host processing, utilities, safety, cost, schedule, risk, and decision gates.\n", - "* Process design basis\n", - "* Proces design in a field development scenario\n", - "* [NPV of a gas-field tie-back with NeqSim](fielddevelopment/npv.ipynb): connect SRK fluid properties, tank depletion, well and tie-back hydraulics, host-arrival control, export compression, Norwegian petroleum-tax screening, break-even, coupled decision sensitivities, and driver-emissions scenarios.\n", - "\n", - "## Field developement case studies\n", - "* [Field development case 1 (combined oil and gas field)](reservoir/fieldDevelopment1.ipynb)\n", - "* Field development case 2 (rich gas and power from shore case)\n", - "* Field development case 3 (sales gas and power from shore)\n", - "* Field development case 4 (hydrogen production with power from shore/wind)\n", - "* Offshore field and onshore NGL process\n", - "\n", - "## Bio Engineering\n", - "* [Food-waste anaerobic digestion and biomethane-to-grid with NeqSim](bioprocesses/bioandneqsim.ipynb): connect empirical digestion, thermodynamic streams, gas upgrading, compression, cooling, mass balances, design sensitivities, and sustainability metrics.\n", - "* [Biomass to sustainable aviation fuel with NeqSim](bioprocesses/biomass_to_sustainable_aviation_fuel_with_neqsim.ipynb): compare HEFA, gasification\u2013FT, ATJ, fast pyrolysis, HTL, and a biogenic-CO\u2082 hybrid with sourced yields, oil quality, NeqSim ASTM oil-quality calculations, lifecycle data, logistics, economics, uncertainty, and validation.\n", - "\n", - "## Standards\n", - "* Energy Institute - Guideline for Flow Induced Vibrations (FIV) control in Production\n", - "* [NORSOK S-001 technical-safety screening with NeqSim](standards/technicalsafetyP0001.ipynb): flash a synthetic high-pressure gas, screen secondary pressure protection, run native transient-wall vessel blowdown and API pool-fire scenarios, compare BDV bore and flare-load trade-offs, and integrate 20 engineering checks.\n", - "* [NORSOK P-002 process-system design screening with NeqSim](standards/norsokP0002.ipynb)\n", - "* NORSOK I-106, Fiscal metering systems for hydrocarbon liquid and gas,\n", - "* (ISO 13703 - Design and installation of piping systems on offshore production platforms\n", - "* [API 521 blocked-liquid thermal-expansion and relief screening with NeqSim](standards/API521_thermal_expansion.ipynb): reproduce the original PR fluid and linear pressure calculation, correct derivative units and sign, solve a nonlinear density closure, estimate heat-duty relief load, exercise native Stream and SafetyValve blowdown, and verify 16 engineering checks.\n", - "\n", - "## Development of Process Digital Twins\n", - "* [Oil Process](process/oilgasprocess1.ipynb)\n", - "* Monitoring of Cricondenbar of Natural Gas\n", - "* Monitoring of TEG dehdydration processes\n", - "\n", - "## Excercise\n", - "* Excercise 1 - [Phase behaviour of reservoir fluids](excercise/Excercise_Phase_Behaviour_of_Reservoir_Fluids%20(1).ipynb)\n", - "* Excercise 2 - [Design of a gas-oil separation process](excercise/Design_of_a_separation_process_in_HYSYS.ipynb)\n", - "* Excercise 3 - [Design of a TEG dehydration process](excercise/Design_of_a_TEG_dehydration_process.ipynb): preserve and repair the three original CPA and Campbell examples; quantify wet-gas water, dew point, hydrate temperature, lean-TEG equilibrium, circulation, stage sensitivity, and a native `SimpleTEGAbsorber` workflow with total and water balances.\n", - "* Excercise 4 - [Export-gas phase envelopes and hydrocarbon dew-point control](excercise/calculationofphaseenvelopes.ipynb): preserve three original SRK envelope cases, correct temperature units, compare pressure-specific dew points, validate a Stream dew specification, and run a connected cooler and scrubber with balances and operating sensitivities.\n", - "* Excercise 5 - [Gas processing and the gas value chain](gasvaluechain/GasProcessingChain.ipynb)\n", - "\n", - "## TEP4185 - Gas Process Technology\n", - "* [NeqSim Thermodynamics](https://colab.research.google.com/drive/1c6OY3O1xX8nmaU5JadF3CIjaj4CSYb1Q?usp=sharing)\n", - "* [Pandas + NeqSim \u2014 Data\u2011driven Thermodynamics](https://colab.research.google.com/drive/1ngKz_Ry8PkJwENdNXIkZpO8PmARiqyGr?usp=sharing)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## RMS-origin reservoir automation\n", - "\n", - "- [RMS-origin Reek to OPM Flow, ERT, and NeqSim](reservoir/rms_to_opm_flow_agent_ert.ipynb): blocking, property spreading, real simulation, uncertainty, and an RMS-agent contract.\n" - ] - } - ], - "metadata": { - "colab": { - "name": "examples of NeqSim in Colab.ipynb", - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "nbformat": 4, + "nbformat_minor": 0 } From 82da34868cbf2f298d394d24572903696866c381 Mon Sep 17 00:00:00 2001 From: Even Solbraa <41290109+EvenSol@users.noreply.github.com> Date: Mon, 31 Aug 2026 19:18:27 +0200 Subject: [PATCH 19/19] Minimize notebook ledger update --- notebooks/notebook_maintenance_ledger.json | 70 +++++++++++----------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/notebooks/notebook_maintenance_ledger.json b/notebooks/notebook_maintenance_ledger.json index 2c48dd76..7175f301 100644 --- a/notebooks/notebook_maintenance_ledger.json +++ b/notebooks/notebook_maintenance_ledger.json @@ -1,38 +1,38 @@ { - "schema_version": 3, - "updated_at": "2026-08-31T17:15:07Z", + "schema_version": 3, + "updated_at": "2026-08-31T17:15:07Z", "active_notebook_count": 295, - "shards_glob": "maintenance_ledger/*.json", - "retired_notebooks": [ - { - "path": "notebooks/process/syntheticdatageneration.ipynb", - "retired_date": "2026-08-04", - "reason": "The current Git blob is truncated at 100000 bytes and cannot be parsed; 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