๐ฌ OPM Flow Complete Showcaseยถ
A complete end-to-end reservoir simulation workflow in Google Colab
This notebook demonstrates the full OPM Flow pipeline:
โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ
โ 1.SETUP โโโโถโ 2.BUILD โโโโถโ 3. RUN โโโโถโ4.ANALYZEโโโโถโ 5. 3D โโโโถโ6.SENSIT.โโโโถโ7.EXPORT โ
โ Install โ โ Model โ โ OPM โ โ Results โ โ VIZ ๐ โ โ Sweeps โ โ Report โ
โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ
Features:
- ๐๏ธ Generate ECLIPSE-format simulation decks
- ๐ Run OPM Flow simulations
- ๐ Production analysis & visualization
- ๐ฒ Interactive 3D reservoir views ๐
- ๐ Animated saturation evolution ๐
- ๐ฌ Sensitivity analysis (permeability sweeps)
- ๐พ Export results & reports
๐ Table of Contentsยถ
๐ก Colab users: Use the built-in ToC in the left sidebar (๐ icon) for navigation!
- Environment Setup - Install OPM Flow
- Build the Model - Create ECLIPSE deck
- Run Simulation - Execute OPM Flow
- Analyze Results - Parse & visualize
- 3D Visualization - Interactive 3D views ๐
- Sensitivity Analysis - Parameter sweeps
- Export & Report - Generate outputs
1. Environment Setupยถ
First, we install OPM Flow and set up our working environment.
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# ๐ฆ Install OPM Flow (runs automatically in Colab)
import subprocess
import sys
import os
from pathlib import Path
def install_opm_flow():
"""Install OPM Flow via apt (Ubuntu/Colab)."""
# Check if already installed
try:
result = subprocess.run(['flow', '--version'], capture_output=True, text=True, timeout=5)
if result.returncode == 0:
version = result.stdout.strip().split('\n')[0]
print(f"โ
OPM Flow already installed: {version}")
return True
except:
pass
print("๐ฆ Installing OPM Flow...")
print(" This takes about 2-3 minutes in Colab.\n")
steps = [
("Updating package list", "apt-get update -qq"),
("Installing prerequisites", "apt-get install -y -qq software-properties-common"),
("Adding OPM repository", "apt-add-repository -y ppa:opm/ppa"),
("Updating with OPM repo", "apt-get update -qq"),
("Installing OPM Flow", "apt-get install -y libopm-simulators-bin"),
]
for i, (desc, cmd) in enumerate(steps, 1):
print(f" [{i}/{len(steps)}] {desc}...", end=" ", flush=True)
result = subprocess.run(f"sudo {cmd}", shell=True, capture_output=True, text=True)
if result.returncode == 0:
print("โ")
else:
print("โ")
print(f" Error: {result.stderr[:300]}")
return False
# Verify installation
result = subprocess.run(['flow', '--version'], capture_output=True, text=True)
if result.returncode == 0:
print(f"\nโ
Installation complete!")
version_line = result.stdout.strip().split('\n')[0]
print(f" {version_line}")
return True
return False
# Install OPM Flow
OPM_AVAILABLE = install_opm_flow()
# ๐ฆ Install OPM Flow (runs automatically in Colab)
import subprocess
import sys
import os
from pathlib import Path
def install_opm_flow():
"""Install OPM Flow via apt (Ubuntu/Colab)."""
# Check if already installed
try:
result = subprocess.run(['flow', '--version'], capture_output=True, text=True, timeout=5)
if result.returncode == 0:
version = result.stdout.strip().split('\n')[0]
print(f"โ
OPM Flow already installed: {version}")
return True
except:
pass
print("๐ฆ Installing OPM Flow...")
print(" This takes about 2-3 minutes in Colab.\n")
steps = [
("Updating package list", "apt-get update -qq"),
("Installing prerequisites", "apt-get install -y -qq software-properties-common"),
("Adding OPM repository", "apt-add-repository -y ppa:opm/ppa"),
("Updating with OPM repo", "apt-get update -qq"),
("Installing OPM Flow", "apt-get install -y libopm-simulators-bin"),
]
for i, (desc, cmd) in enumerate(steps, 1):
print(f" [{i}/{len(steps)}] {desc}...", end=" ", flush=True)
result = subprocess.run(f"sudo {cmd}", shell=True, capture_output=True, text=True)
if result.returncode == 0:
print("โ")
else:
print("โ")
print(f" Error: {result.stderr[:300]}")
return False
# Verify installation
result = subprocess.run(['flow', '--version'], capture_output=True, text=True)
if result.returncode == 0:
print(f"\nโ
Installation complete!")
version_line = result.stdout.strip().split('\n')[0]
print(f" {version_line}")
return True
return False
# Install OPM Flow
OPM_AVAILABLE = install_opm_flow()
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# ๐ ๏ธ Setup workspace and imports
import json
import tempfile
import shutil
from datetime import datetime
from typing import Dict, List, Tuple, Optional
import re
# Create workspace
WORKSPACE = Path(tempfile.mkdtemp(prefix="opm_showcase_"))
print(f"๐ Workspace: {WORKSPACE}")
# Install visualization dependencies
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "matplotlib", "pandas", "numpy"])
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
# Plot styling
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams['figure.figsize'] = [12, 8]
plt.rcParams['font.size'] = 11
print(f"๐ Visualization ready")
print(f"๐
Session started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
# ๐ ๏ธ Setup workspace and imports
import json
import tempfile
import shutil
from datetime import datetime
from typing import Dict, List, Tuple, Optional
import re
# Create workspace
WORKSPACE = Path(tempfile.mkdtemp(prefix="opm_showcase_"))
print(f"๐ Workspace: {WORKSPACE}")
# Install visualization dependencies
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "matplotlib", "pandas", "numpy"])
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
# Plot styling
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams['figure.figsize'] = [12, 8]
plt.rcParams['font.size'] = 11
print(f"๐ Visualization ready")
print(f"๐
Session started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
2. Build the Modelยถ
We'll create a 5-spot waterflood pattern - a common field development configuration:
INJ โโโโโโโโโโโโโโโโ INJ
โ โ
โ โ
โ PROD โ
โ โ โ
โ โ
โ โ
INJ โโโโโโโโโโโโโโโโ INJ
Model Parameters:
- Grid: 21 ร 21 ร 5 cells (2,205 total)
- Cell size: 100m ร 100m ร 10m
- Porosity: 20%
- Permeability: 100 mD (with heterogeneity)
- Initial pressure: 300 bar
- 4 injectors + 1 producer (5-spot pattern)
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# ๐๏ธ ECLIPSE Deck Generator
class EclipseDeckBuilder:
"""Builder for ECLIPSE simulation decks."""
def __init__(self, name: str, nx: int, ny: int, nz: int):
self.name = name
self.nx, self.ny, self.nz = nx, ny, nz
self.ncells = nx * ny * nz
self.sections = {}
def build_runspec(self, title: str, start_date: str = "1 'JAN' 2026") -> str:
"""Build RUNSPEC section."""
return f"""-- ============================================================
-- RUNSPEC Section
-- ============================================================
RUNSPEC
TITLE
{title} /
-- Dimensions
DIMENS
{self.nx} {self.ny} {self.nz} /
-- Phases
OIL
WATER
-- Units
METRIC
-- Table dimensions
TABDIMS
1 1 40 40 /
-- Well dimensions
WELLDIMS
10 50 5 10 /
-- Start date
START
{start_date} /
-- Unified output
UNIFOUT
UNIFIN
"""
def build_grid(self, dx: float, dy: float, dz: float,
top: float, poro_mean: float, perm_mean: float,
add_heterogeneity: bool = True) -> str:
"""Build GRID section with optional heterogeneity."""
# Store cell dimensions for visualization
self.dx, self.dy, self.dz = dx, dy, dz
# Generate property arrays
np.random.seed(42) # Reproducible
if add_heterogeneity:
# Log-normal permeability distribution
perm = np.random.lognormal(np.log(perm_mean), 0.5, self.ncells)
perm = np.clip(perm, perm_mean * 0.1, perm_mean * 5) # Limit range
# Correlated porosity
poro = poro_mean + 0.05 * (np.log(perm) - np.log(perm_mean)) / 0.5
poro = np.clip(poro, 0.05, 0.35)
else:
perm = np.full(self.ncells, perm_mean)
poro = np.full(self.ncells, poro_mean)
# Store for later analysis
self.perm_array = perm
self.poro_array = poro
# Format arrays
def format_array(arr, name, per_line=10):
lines = [f"{name}"]
for i in range(0, len(arr), per_line):
chunk = arr[i:i+per_line]
lines.append(" " + " ".join(f"{v:.4f}" for v in chunk))
lines.append("/\n")
return "\n".join(lines)
return f"""-- ============================================================
-- GRID Section
-- ============================================================
GRID
-- Cell dimensions
DX
{self.ncells}*{dx} /
DY
{self.ncells}*{dy} /
DZ
{self.ncells}*{dz} /
-- Top depth (first layer)
TOPS
{self.nx * self.ny}*{top} /
-- Porosity (heterogeneous)
{format_array(poro, 'PORO')}
-- Permeability (heterogeneous, log-normal)
{format_array(perm, 'PERMX')}
-- Y-permeability = X-permeability
COPY
PERMX PERMY /
/
-- Z-permeability = 0.1 * X-permeability
{format_array(perm * 0.1, 'PERMZ')}
"""
def build_props(self) -> str:
"""Build PROPS section with fluid properties."""
return """-- ============================================================
-- PROPS Section
-- ============================================================
PROPS
-- Water-Oil relative permeability
-- Sw Krw Kro Pcow
SWOF
0.20 0.0000 1.0000 0.0
0.25 0.0050 0.8500 0.0
0.30 0.0200 0.7000 0.0
0.35 0.0450 0.5600 0.0
0.40 0.0800 0.4300 0.0
0.45 0.1250 0.3200 0.0
0.50 0.1800 0.2200 0.0
0.55 0.2450 0.1400 0.0
0.60 0.3200 0.0800 0.0
0.65 0.4050 0.0400 0.0
0.70 0.5000 0.0150 0.0
0.75 0.6050 0.0040 0.0
0.80 0.7200 0.0000 0.0
/
-- Water PVT
-- Pref Bw Cw Visc Cv
PVTW
300.0 1.010 4.0E-5 0.40 0.0 /
-- Dead oil PVT
-- P Bo Visc
PVDO
100.0 1.100 2.50
150.0 1.080 2.80
200.0 1.060 3.20
250.0 1.040 3.60
300.0 1.020 4.00
350.0 1.005 4.50
400.0 0.995 5.00
/
-- Rock compressibility
-- Pref Cr
ROCK
300.0 4.0E-5 /
-- Densities at surface
-- Oil Water Gas
DENSITY
850.0 1025.0 1.0 /
"""
def build_solution(self, datum_depth: float, datum_pressure: float,
woc_depth: float) -> str:
"""Build SOLUTION section."""
return f"""-- ============================================================
-- SOLUTION Section
-- ============================================================
SOLUTION
-- Equilibration data
-- Datum Pdat WOC Pcow GOC Pcog
EQUIL
{datum_depth} {datum_pressure} {woc_depth} 0.0 0.0 0.0 1 1 0 /
"""
def build_summary(self) -> str:
"""Build SUMMARY section with output requests."""
return """-- ============================================================
-- SUMMARY Section
-- ============================================================
SUMMARY
-- Field totals
FOPR
FWPR
FOPT
FWPT
FWIT
FWCT
FPR
FOE
-- Well rates
WOPR
/
WWPR
/
WWIR
/
WBHP
/
WWCT
/
-- Run summary
RUNSUM
-- CPU time
TCPU
"""
def build_schedule(self, wells: List[Dict], timesteps: List[float]) -> str:
"""Build SCHEDULE section with wells and timesteps."""
# Well specifications
welspecs = "WELSPECS\n"
compdat = "COMPDAT\n"
wconprod = "WCONPROD\n"
wconinje = "WCONINJE\n"
for w in wells:
welspecs += f"'{w['name']}' 'G1' {w['i']} {w['j']} 1* '{w['type']}' /\n"
compdat += f"'{w['name']}' {w['i']} {w['j']} 1 {self.nz} 'OPEN' 2* 0.15 /\n"
if w['type'] == 'OIL':
wconprod += f"'{w['name']}' 'OPEN' 'ORAT' {w['rate']} 4* {w['bhp_min']} /\n"
else:
wconinje += f"'{w['name']}' 'WATER' 'OPEN' 'RATE' {w['rate']} 1* {w['bhp_max']} /\n"
welspecs += "/\n\n"
compdat += "/\n\n"
wconprod += "/\n\n"
wconinje += "/\n\n"
# Restart output configuration (write SWAT, PRESSURE every 5 steps)
rptrst = """-- Restart output for saturation visualization
RPTRST
BASIC=3 FREQ=5 /
"""
# Timesteps
tstep = "TSTEP\n" + " ".join(str(t) for t in timesteps) + " /\n"
return f"""-- ============================================================
-- SCHEDULE Section
-- ============================================================
SCHEDULE
{rptrst}-- Well specifications
{welspecs}
-- Completions
{compdat}
-- Producer controls
{wconprod}
-- Injector controls
{wconinje}
-- Simulation timesteps
{tstep}
END
"""
def build_complete_deck(self, **params) -> str:
"""Build complete ECLIPSE deck."""
deck = ""
deck += self.build_runspec(params.get('title', self.name))
deck += self.build_grid(
dx=params.get('dx', 100),
dy=params.get('dy', 100),
dz=params.get('dz', 10),
top=params.get('top', 2500),
poro_mean=params.get('poro', 0.20),
perm_mean=params.get('perm', 100),
add_heterogeneity=params.get('heterogeneous', True)
)
deck += self.build_props()
deck += self.build_solution(
datum_depth=params.get('datum_depth', 2525),
datum_pressure=params.get('pressure', 300),
woc_depth=params.get('woc', 2600)
)
deck += self.build_summary()
deck += self.build_schedule(
wells=params.get('wells', []),
timesteps=params.get('timesteps', [30]*12)
)
return deck
print("โ
EclipseDeckBuilder ready")
# ๐๏ธ ECLIPSE Deck Generator
class EclipseDeckBuilder:
"""Builder for ECLIPSE simulation decks."""
def __init__(self, name: str, nx: int, ny: int, nz: int):
self.name = name
self.nx, self.ny, self.nz = nx, ny, nz
self.ncells = nx * ny * nz
self.sections = {}
def build_runspec(self, title: str, start_date: str = "1 'JAN' 2026") -> str:
"""Build RUNSPEC section."""
return f"""-- ============================================================
-- RUNSPEC Section
-- ============================================================
RUNSPEC
TITLE
{title} /
-- Dimensions
DIMENS
{self.nx} {self.ny} {self.nz} /
-- Phases
OIL
WATER
-- Units
METRIC
-- Table dimensions
TABDIMS
1 1 40 40 /
-- Well dimensions
WELLDIMS
10 50 5 10 /
-- Start date
START
{start_date} /
-- Unified output
UNIFOUT
UNIFIN
"""
def build_grid(self, dx: float, dy: float, dz: float,
top: float, poro_mean: float, perm_mean: float,
add_heterogeneity: bool = True) -> str:
"""Build GRID section with optional heterogeneity."""
# Store cell dimensions for visualization
self.dx, self.dy, self.dz = dx, dy, dz
# Generate property arrays
np.random.seed(42) # Reproducible
if add_heterogeneity:
# Log-normal permeability distribution
perm = np.random.lognormal(np.log(perm_mean), 0.5, self.ncells)
perm = np.clip(perm, perm_mean * 0.1, perm_mean * 5) # Limit range
# Correlated porosity
poro = poro_mean + 0.05 * (np.log(perm) - np.log(perm_mean)) / 0.5
poro = np.clip(poro, 0.05, 0.35)
else:
perm = np.full(self.ncells, perm_mean)
poro = np.full(self.ncells, poro_mean)
# Store for later analysis
self.perm_array = perm
self.poro_array = poro
# Format arrays
def format_array(arr, name, per_line=10):
lines = [f"{name}"]
for i in range(0, len(arr), per_line):
chunk = arr[i:i+per_line]
lines.append(" " + " ".join(f"{v:.4f}" for v in chunk))
lines.append("/\n")
return "\n".join(lines)
return f"""-- ============================================================
-- GRID Section
-- ============================================================
GRID
-- Cell dimensions
DX
{self.ncells}*{dx} /
DY
{self.ncells}*{dy} /
DZ
{self.ncells}*{dz} /
-- Top depth (first layer)
TOPS
{self.nx * self.ny}*{top} /
-- Porosity (heterogeneous)
{format_array(poro, 'PORO')}
-- Permeability (heterogeneous, log-normal)
{format_array(perm, 'PERMX')}
-- Y-permeability = X-permeability
COPY
PERMX PERMY /
/
-- Z-permeability = 0.1 * X-permeability
{format_array(perm * 0.1, 'PERMZ')}
"""
def build_props(self) -> str:
"""Build PROPS section with fluid properties."""
return """-- ============================================================
-- PROPS Section
-- ============================================================
PROPS
-- Water-Oil relative permeability
-- Sw Krw Kro Pcow
SWOF
0.20 0.0000 1.0000 0.0
0.25 0.0050 0.8500 0.0
0.30 0.0200 0.7000 0.0
0.35 0.0450 0.5600 0.0
0.40 0.0800 0.4300 0.0
0.45 0.1250 0.3200 0.0
0.50 0.1800 0.2200 0.0
0.55 0.2450 0.1400 0.0
0.60 0.3200 0.0800 0.0
0.65 0.4050 0.0400 0.0
0.70 0.5000 0.0150 0.0
0.75 0.6050 0.0040 0.0
0.80 0.7200 0.0000 0.0
/
-- Water PVT
-- Pref Bw Cw Visc Cv
PVTW
300.0 1.010 4.0E-5 0.40 0.0 /
-- Dead oil PVT
-- P Bo Visc
PVDO
100.0 1.100 2.50
150.0 1.080 2.80
200.0 1.060 3.20
250.0 1.040 3.60
300.0 1.020 4.00
350.0 1.005 4.50
400.0 0.995 5.00
/
-- Rock compressibility
-- Pref Cr
ROCK
300.0 4.0E-5 /
-- Densities at surface
-- Oil Water Gas
DENSITY
850.0 1025.0 1.0 /
"""
def build_solution(self, datum_depth: float, datum_pressure: float,
woc_depth: float) -> str:
"""Build SOLUTION section."""
return f"""-- ============================================================
-- SOLUTION Section
-- ============================================================
SOLUTION
-- Equilibration data
-- Datum Pdat WOC Pcow GOC Pcog
EQUIL
{datum_depth} {datum_pressure} {woc_depth} 0.0 0.0 0.0 1 1 0 /
"""
def build_summary(self) -> str:
"""Build SUMMARY section with output requests."""
return """-- ============================================================
-- SUMMARY Section
-- ============================================================
SUMMARY
-- Field totals
FOPR
FWPR
FOPT
FWPT
FWIT
FWCT
FPR
FOE
-- Well rates
WOPR
/
WWPR
/
WWIR
/
WBHP
/
WWCT
/
-- Run summary
RUNSUM
-- CPU time
TCPU
"""
def build_schedule(self, wells: List[Dict], timesteps: List[float]) -> str:
"""Build SCHEDULE section with wells and timesteps."""
# Well specifications
welspecs = "WELSPECS\n"
compdat = "COMPDAT\n"
wconprod = "WCONPROD\n"
wconinje = "WCONINJE\n"
for w in wells:
welspecs += f"'{w['name']}' 'G1' {w['i']} {w['j']} 1* '{w['type']}' /\n"
compdat += f"'{w['name']}' {w['i']} {w['j']} 1 {self.nz} 'OPEN' 2* 0.15 /\n"
if w['type'] == 'OIL':
wconprod += f"'{w['name']}' 'OPEN' 'ORAT' {w['rate']} 4* {w['bhp_min']} /\n"
else:
wconinje += f"'{w['name']}' 'WATER' 'OPEN' 'RATE' {w['rate']} 1* {w['bhp_max']} /\n"
welspecs += "/\n\n"
compdat += "/\n\n"
wconprod += "/\n\n"
wconinje += "/\n\n"
# Restart output configuration (write SWAT, PRESSURE every 5 steps)
rptrst = """-- Restart output for saturation visualization
RPTRST
BASIC=3 FREQ=5 /
"""
# Timesteps
tstep = "TSTEP\n" + " ".join(str(t) for t in timesteps) + " /\n"
return f"""-- ============================================================
-- SCHEDULE Section
-- ============================================================
SCHEDULE
{rptrst}-- Well specifications
{welspecs}
-- Completions
{compdat}
-- Producer controls
{wconprod}
-- Injector controls
{wconinje}
-- Simulation timesteps
{tstep}
END
"""
def build_complete_deck(self, **params) -> str:
"""Build complete ECLIPSE deck."""
deck = ""
deck += self.build_runspec(params.get('title', self.name))
deck += self.build_grid(
dx=params.get('dx', 100),
dy=params.get('dy', 100),
dz=params.get('dz', 10),
top=params.get('top', 2500),
poro_mean=params.get('poro', 0.20),
perm_mean=params.get('perm', 100),
add_heterogeneity=params.get('heterogeneous', True)
)
deck += self.build_props()
deck += self.build_solution(
datum_depth=params.get('datum_depth', 2525),
datum_pressure=params.get('pressure', 300),
woc_depth=params.get('woc', 2600)
)
deck += self.build_summary()
deck += self.build_schedule(
wells=params.get('wells', []),
timesteps=params.get('timesteps', [30]*12)
)
return deck
print("โ
EclipseDeckBuilder ready")
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# ๐๏ธ Build the 5-Spot Waterflood Model
# Grid dimensions
NX, NY, NZ = 21, 21, 5
# Well positions (5-spot pattern)
center = (NX // 2 + 1, NY // 2 + 1) # Producer in center
corners = [
(2, 2), (2, NY-1), (NX-1, 2), (NX-1, NY-1) # Injectors at corners
]
wells = [
# Central producer
{'name': 'PROD1', 'i': center[0], 'j': center[1], 'type': 'OIL', 'rate': 500, 'bhp_min': 150},
# Corner injectors
{'name': 'INJ1', 'i': corners[0][0], 'j': corners[0][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
{'name': 'INJ2', 'i': corners[1][0], 'j': corners[1][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
{'name': 'INJ3', 'i': corners[2][0], 'j': corners[2][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
{'name': 'INJ4', 'i': corners[3][0], 'j': corners[3][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
]
# Timesteps: 5 years with monthly steps
timesteps = [30] * 60 # 60 months
# Build deck
builder = EclipseDeckBuilder("FIVESPOT", NX, NY, NZ)
deck = builder.build_complete_deck(
title="5-Spot Waterflood Pattern - OPM Showcase",
dx=100, dy=100, dz=10,
top=2500,
poro=0.20,
perm=100,
heterogeneous=True,
datum_depth=2525,
pressure=300,
woc=2600,
wells=wells,
timesteps=timesteps
)
# Save deck
deck_path = WORKSPACE / "FIVESPOT.DATA"
deck_path.write_text(deck)
print(f"โ
Model built: {deck_path.name}")
print(f" Grid: {NX} ร {NY} ร {NZ} = {NX*NY*NZ:,} cells")
print(f" Wells: 1 producer + 4 injectors")
print(f" Duration: {sum(timesteps)} days ({sum(timesteps)/365:.1f} years)")
print(f" Deck size: {len(deck):,} characters")
# ๐๏ธ Build the 5-Spot Waterflood Model
# Grid dimensions
NX, NY, NZ = 21, 21, 5
# Well positions (5-spot pattern)
center = (NX // 2 + 1, NY // 2 + 1) # Producer in center
corners = [
(2, 2), (2, NY-1), (NX-1, 2), (NX-1, NY-1) # Injectors at corners
]
wells = [
# Central producer
{'name': 'PROD1', 'i': center[0], 'j': center[1], 'type': 'OIL', 'rate': 500, 'bhp_min': 150},
# Corner injectors
{'name': 'INJ1', 'i': corners[0][0], 'j': corners[0][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
{'name': 'INJ2', 'i': corners[1][0], 'j': corners[1][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
{'name': 'INJ3', 'i': corners[2][0], 'j': corners[2][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
{'name': 'INJ4', 'i': corners[3][0], 'j': corners[3][1], 'type': 'WATER', 'rate': 200, 'bhp_max': 400},
]
# Timesteps: 5 years with monthly steps
timesteps = [30] * 60 # 60 months
# Build deck
builder = EclipseDeckBuilder("FIVESPOT", NX, NY, NZ)
deck = builder.build_complete_deck(
title="5-Spot Waterflood Pattern - OPM Showcase",
dx=100, dy=100, dz=10,
top=2500,
poro=0.20,
perm=100,
heterogeneous=True,
datum_depth=2525,
pressure=300,
woc=2600,
wells=wells,
timesteps=timesteps
)
# Save deck
deck_path = WORKSPACE / "FIVESPOT.DATA"
deck_path.write_text(deck)
print(f"โ
Model built: {deck_path.name}")
print(f" Grid: {NX} ร {NY} ร {NZ} = {NX*NY*NZ:,} cells")
print(f" Wells: 1 producer + 4 injectors")
print(f" Duration: {sum(timesteps)} days ({sum(timesteps)/365:.1f} years)")
print(f" Deck size: {len(deck):,} characters")
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# ๐ Visualize the model
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Reshape arrays to grid
perm_grid = builder.perm_array[:NX*NY].reshape(NY, NX)
poro_grid = builder.poro_array[:NX*NY].reshape(NY, NX)
# 1. Permeability map
im1 = axes[0].imshow(perm_grid, cmap='viridis', origin='lower')
axes[0].set_title('Permeability (mD) - Layer 1')
plt.colorbar(im1, ax=axes[0])
# Mark wells
for w in wells:
marker = 'ro' if w['type'] == 'OIL' else 'b^'
axes[0].plot(w['i']-1, w['j']-1, marker, markersize=12, markeredgecolor='white', markeredgewidth=2)
# 2. Porosity map
im2 = axes[1].imshow(poro_grid, cmap='YlOrRd', origin='lower')
axes[1].set_title('Porosity - Layer 1')
plt.colorbar(im2, ax=axes[1])
for w in wells:
marker = 'ro' if w['type'] == 'OIL' else 'b^'
axes[1].plot(w['i']-1, w['j']-1, marker, markersize=12, markeredgecolor='white', markeredgewidth=2)
# 3. Well pattern schematic
axes[2].set_xlim(0, NX+1)
axes[2].set_ylim(0, NY+1)
axes[2].set_aspect('equal')
axes[2].set_title('5-Spot Well Pattern')
# Draw grid outline
axes[2].plot([1, NX, NX, 1, 1], [1, 1, NY, NY, 1], 'k-', linewidth=2)
# Draw wells and connections
for w in wells:
if w['type'] == 'OIL':
axes[2].plot(w['i'], w['j'], 'ro', markersize=20, label='Producer')
axes[2].annotate(w['name'], (w['i'], w['j']), textcoords="offset points",
xytext=(0, 15), ha='center', fontweight='bold')
else:
axes[2].plot(w['i'], w['j'], 'b^', markersize=15)
axes[2].annotate(w['name'], (w['i'], w['j']), textcoords="offset points",
xytext=(0, 12), ha='center', fontsize=9)
# Draw flow lines to producer
axes[2].annotate('', xy=(center[0], center[1]), xytext=(w['i'], w['j']),
arrowprops=dict(arrowstyle='->', color='blue', alpha=0.5, lw=1.5))
axes[2].legend(['Grid', 'Producer', 'Injector'], loc='upper right')
axes[2].set_xlabel('I index')
axes[2].set_ylabel('J index')
plt.tight_layout()
plt.savefig(WORKSPACE / 'model_overview.png', dpi=150)
plt.show()
print(f"\n๐ Permeability: min={perm_grid.min():.1f}, max={perm_grid.max():.1f}, mean={perm_grid.mean():.1f} mD")
print(f"๐ Porosity: min={poro_grid.min():.3f}, max={poro_grid.max():.3f}, mean={poro_grid.mean():.3f}")
# ๐ Visualize the model
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Reshape arrays to grid
perm_grid = builder.perm_array[:NX*NY].reshape(NY, NX)
poro_grid = builder.poro_array[:NX*NY].reshape(NY, NX)
# 1. Permeability map
im1 = axes[0].imshow(perm_grid, cmap='viridis', origin='lower')
axes[0].set_title('Permeability (mD) - Layer 1')
plt.colorbar(im1, ax=axes[0])
# Mark wells
for w in wells:
marker = 'ro' if w['type'] == 'OIL' else 'b^'
axes[0].plot(w['i']-1, w['j']-1, marker, markersize=12, markeredgecolor='white', markeredgewidth=2)
# 2. Porosity map
im2 = axes[1].imshow(poro_grid, cmap='YlOrRd', origin='lower')
axes[1].set_title('Porosity - Layer 1')
plt.colorbar(im2, ax=axes[1])
for w in wells:
marker = 'ro' if w['type'] == 'OIL' else 'b^'
axes[1].plot(w['i']-1, w['j']-1, marker, markersize=12, markeredgecolor='white', markeredgewidth=2)
# 3. Well pattern schematic
axes[2].set_xlim(0, NX+1)
axes[2].set_ylim(0, NY+1)
axes[2].set_aspect('equal')
axes[2].set_title('5-Spot Well Pattern')
# Draw grid outline
axes[2].plot([1, NX, NX, 1, 1], [1, 1, NY, NY, 1], 'k-', linewidth=2)
# Draw wells and connections
for w in wells:
if w['type'] == 'OIL':
axes[2].plot(w['i'], w['j'], 'ro', markersize=20, label='Producer')
axes[2].annotate(w['name'], (w['i'], w['j']), textcoords="offset points",
xytext=(0, 15), ha='center', fontweight='bold')
else:
axes[2].plot(w['i'], w['j'], 'b^', markersize=15)
axes[2].annotate(w['name'], (w['i'], w['j']), textcoords="offset points",
xytext=(0, 12), ha='center', fontsize=9)
# Draw flow lines to producer
axes[2].annotate('', xy=(center[0], center[1]), xytext=(w['i'], w['j']),
arrowprops=dict(arrowstyle='->', color='blue', alpha=0.5, lw=1.5))
axes[2].legend(['Grid', 'Producer', 'Injector'], loc='upper right')
axes[2].set_xlabel('I index')
axes[2].set_ylabel('J index')
plt.tight_layout()
plt.savefig(WORKSPACE / 'model_overview.png', dpi=150)
plt.show()
print(f"\n๐ Permeability: min={perm_grid.min():.1f}, max={perm_grid.max():.1f}, mean={perm_grid.mean():.1f} mD")
print(f"๐ Porosity: min={poro_grid.min():.3f}, max={poro_grid.max():.3f}, mean={poro_grid.mean():.3f}")
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# ๐ Run OPM Flow Simulation
def run_opm_flow(deck_path: Path, verbose: bool = True) -> Dict:
"""Run OPM Flow simulation."""
if not OPM_AVAILABLE:
print("โ OPM Flow not available. Please run installation cell first.")
return {'success': False, 'error': 'OPM Flow not installed'}
print(f"๐ Running simulation: {deck_path.name}")
print(f" Working directory: {deck_path.parent}")
print()
start_time = datetime.now()
# Run flow
result = subprocess.run(
['flow', str(deck_path)],
capture_output=True,
text=True,
cwd=deck_path.parent,
timeout=600 # 10 minute timeout
)
elapsed = (datetime.now() - start_time).total_seconds()
# Parse output for progress
if verbose and result.stdout:
lines = result.stdout.strip().split('\n')
# Show last few lines
print(" Output (last 10 lines):")
for line in lines[-10:]:
print(f" {line}")
success = result.returncode == 0
print()
if success:
print(f"โ
Simulation completed in {elapsed:.1f} seconds")
else:
print(f"โ Simulation failed (return code: {result.returncode})")
if result.stderr:
print(f" Error: {result.stderr[:500]}")
return {
'success': success,
'elapsed': elapsed,
'returncode': result.returncode,
'stdout': result.stdout,
'stderr': result.stderr
}
# Run the simulation
sim_result = run_opm_flow(deck_path)
# ๐ Run OPM Flow Simulation
def run_opm_flow(deck_path: Path, verbose: bool = True) -> Dict:
"""Run OPM Flow simulation."""
if not OPM_AVAILABLE:
print("โ OPM Flow not available. Please run installation cell first.")
return {'success': False, 'error': 'OPM Flow not installed'}
print(f"๐ Running simulation: {deck_path.name}")
print(f" Working directory: {deck_path.parent}")
print()
start_time = datetime.now()
# Run flow
result = subprocess.run(
['flow', str(deck_path)],
capture_output=True,
text=True,
cwd=deck_path.parent,
timeout=600 # 10 minute timeout
)
elapsed = (datetime.now() - start_time).total_seconds()
# Parse output for progress
if verbose and result.stdout:
lines = result.stdout.strip().split('\n')
# Show last few lines
print(" Output (last 10 lines):")
for line in lines[-10:]:
print(f" {line}")
success = result.returncode == 0
print()
if success:
print(f"โ
Simulation completed in {elapsed:.1f} seconds")
else:
print(f"โ Simulation failed (return code: {result.returncode})")
if result.stderr:
print(f" Error: {result.stderr[:500]}")
return {
'success': success,
'elapsed': elapsed,
'returncode': result.returncode,
'stdout': result.stdout,
'stderr': result.stderr
}
# Run the simulation
sim_result = run_opm_flow(deck_path)
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# ๐ Check output files
print("๐ Simulation output files:")
output_files = list(WORKSPACE.glob("FIVESPOT.*"))
for f in sorted(output_files):
size = f.stat().st_size
if size > 1024*1024:
size_str = f"{size/1024/1024:.1f} MB"
elif size > 1024:
size_str = f"{size/1024:.1f} KB"
else:
size_str = f"{size} B"
print(f" {f.name:30s} {size_str:>10s}")
# ๐ Check output files
print("๐ Simulation output files:")
output_files = list(WORKSPACE.glob("FIVESPOT.*"))
for f in sorted(output_files):
size = f.stat().st_size
if size > 1024*1024:
size_str = f"{size/1024/1024:.1f} MB"
elif size > 1024:
size_str = f"{size/1024:.1f} KB"
else:
size_str = f"{size} B"
print(f" {f.name:30s} {size_str:>10s}")
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# ๐ฆ Install opm Python bindings (for reading binary result files)
import subprocess
import sys
print("Installing opm Python bindings...")
result = subprocess.run(
[sys.executable, "-m", "pip", "install", "-q", "opm"],
capture_output=True, text=True
)
if result.returncode == 0:
print("โ
opm installed")
try:
from opm.io.ecl import ESmry
print(" Can read UNSMRY files!")
except ImportError as e:
print(f" โ ๏ธ Import issue: {e}")
else:
print(f"โ ๏ธ opm install failed (will use mock data)")
print(f" {result.stderr[:200]}")
# ๐ฆ Install opm Python bindings (for reading binary result files)
import subprocess
import sys
print("Installing opm Python bindings...")
result = subprocess.run(
[sys.executable, "-m", "pip", "install", "-q", "opm"],
capture_output=True, text=True
)
if result.returncode == 0:
print("โ
opm installed")
try:
from opm.io.ecl import ESmry
print(" Can read UNSMRY files!")
except ImportError as e:
print(f" โ ๏ธ Import issue: {e}")
else:
print(f"โ ๏ธ opm install failed (will use mock data)")
print(f" {result.stderr[:200]}")
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# ๐ Result Parser
class OPMResultParser:
"""Parser for OPM Flow output files."""
def __init__(self, workspace: Path, case_name: str):
self.workspace = workspace
self.case_name = case_name
self.data = None
def parse(self) -> pd.DataFrame:
"""Parse OPM results - tries multiple methods."""
# List available output files
print("๐ Looking for output files...")
output_files = list(self.workspace.glob(f"{self.case_name}*"))
for f in sorted(output_files)[:10]:
size_kb = f.stat().st_size / 1024
print(f" {f.name:30s} {size_kb:>8.1f} KB")
print()
# Method 1: Try opm.io Python bindings (best)
try:
from opm.io.ecl import ESmry
smspec_file = self.workspace / f"{self.case_name}.SMSPEC"
if smspec_file.exists():
print("๐ Reading results with opm.io...")
smry = ESmry(str(self.workspace / self.case_name))
keys = smry.keys()
print(f" Available vectors: {len(keys)}")
# Build dataframe from available keys
data_dict = {'TIME': smry['TIME']}
for key in ['FOPR', 'FWPR', 'FWCT', 'FPR', 'FOPT', 'FWPT', 'FWIT']:
if key in keys:
data_dict[key] = smry[key]
else:
data_dict[key] = np.zeros(len(smry['TIME']))
self.data = pd.DataFrame(data_dict)
print(f" โ
Loaded {len(self.data)} timesteps")
return self.data
except ImportError:
print("โ ๏ธ opm.io not installed, using mock data...")
except Exception as e:
print(f"โ ๏ธ opm.io error: {e}")
# Fallback: Generate mock data for visualization
print("๐ Generating representative results for visualization...")
return self._generate_mock_results()
def _generate_mock_results(self) -> pd.DataFrame:
"""Generate realistic mock results."""
time = np.arange(0, 1830, 30) # 5 years, monthly
n = len(time)
# Realistic waterflood decline curves
fopr = 500 * np.exp(-time / 800) + 100 * np.exp(-time / 2000)
fopr = np.clip(fopr, 50, 500)
# Water breakthrough
breakthrough_time = 300
fwpr = 600 * (1 - np.exp(-(time - breakthrough_time) / 400))
fwpr = np.clip(fwpr, 0, 800)
fwpr[time < breakthrough_time] = 0
# Water cut
total_liquid = fopr + fwpr
fwct = np.where(total_liquid > 0, fwpr / total_liquid, 0)
# Pressure
fpr = 300 - 50 * (1 - np.exp(-time / 500)) + 20 * (1 - np.exp(-time / 1000))
fpr = np.clip(fpr, 250, 300)
# Cumulative
fopt = np.cumsum(fopr * 30)
fwpt = np.cumsum(fwpr * 30)
fwit = np.cumsum(np.ones(n) * 800 * 30)
self.data = pd.DataFrame({
'TIME': time,
'FOPR': fopr,
'FWPR': fwpr,
'FWCT': fwct,
'FPR': fpr,
'FOPT': fopt,
'FWPT': fwpt,
'FWIT': fwit
})
print(f" โ
Generated {len(self.data)} timesteps")
return self.data
# Parse results
parser = OPMResultParser(WORKSPACE, "FIVESPOT")
results = parser.parse()
print(f"\n๐ Results summary:")
print(results.describe().round(2))
# ๐ Result Parser
class OPMResultParser:
"""Parser for OPM Flow output files."""
def __init__(self, workspace: Path, case_name: str):
self.workspace = workspace
self.case_name = case_name
self.data = None
def parse(self) -> pd.DataFrame:
"""Parse OPM results - tries multiple methods."""
# List available output files
print("๐ Looking for output files...")
output_files = list(self.workspace.glob(f"{self.case_name}*"))
for f in sorted(output_files)[:10]:
size_kb = f.stat().st_size / 1024
print(f" {f.name:30s} {size_kb:>8.1f} KB")
print()
# Method 1: Try opm.io Python bindings (best)
try:
from opm.io.ecl import ESmry
smspec_file = self.workspace / f"{self.case_name}.SMSPEC"
if smspec_file.exists():
print("๐ Reading results with opm.io...")
smry = ESmry(str(self.workspace / self.case_name))
keys = smry.keys()
print(f" Available vectors: {len(keys)}")
# Build dataframe from available keys
data_dict = {'TIME': smry['TIME']}
for key in ['FOPR', 'FWPR', 'FWCT', 'FPR', 'FOPT', 'FWPT', 'FWIT']:
if key in keys:
data_dict[key] = smry[key]
else:
data_dict[key] = np.zeros(len(smry['TIME']))
self.data = pd.DataFrame(data_dict)
print(f" โ
Loaded {len(self.data)} timesteps")
return self.data
except ImportError:
print("โ ๏ธ opm.io not installed, using mock data...")
except Exception as e:
print(f"โ ๏ธ opm.io error: {e}")
# Fallback: Generate mock data for visualization
print("๐ Generating representative results for visualization...")
return self._generate_mock_results()
def _generate_mock_results(self) -> pd.DataFrame:
"""Generate realistic mock results."""
time = np.arange(0, 1830, 30) # 5 years, monthly
n = len(time)
# Realistic waterflood decline curves
fopr = 500 * np.exp(-time / 800) + 100 * np.exp(-time / 2000)
fopr = np.clip(fopr, 50, 500)
# Water breakthrough
breakthrough_time = 300
fwpr = 600 * (1 - np.exp(-(time - breakthrough_time) / 400))
fwpr = np.clip(fwpr, 0, 800)
fwpr[time < breakthrough_time] = 0
# Water cut
total_liquid = fopr + fwpr
fwct = np.where(total_liquid > 0, fwpr / total_liquid, 0)
# Pressure
fpr = 300 - 50 * (1 - np.exp(-time / 500)) + 20 * (1 - np.exp(-time / 1000))
fpr = np.clip(fpr, 250, 300)
# Cumulative
fopt = np.cumsum(fopr * 30)
fwpt = np.cumsum(fwpr * 30)
fwit = np.cumsum(np.ones(n) * 800 * 30)
self.data = pd.DataFrame({
'TIME': time,
'FOPR': fopr,
'FWPR': fwpr,
'FWCT': fwct,
'FPR': fpr,
'FOPT': fopt,
'FWPT': fwpt,
'FWIT': fwit
})
print(f" โ
Generated {len(self.data)} timesteps")
return self.data
# Parse results
parser = OPMResultParser(WORKSPACE, "FIVESPOT")
results = parser.parse()
print(f"\n๐ Results summary:")
print(results.describe().round(2))
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# ๐ Production Plots
if not results.empty:
fig = plt.figure(figsize=(16, 12))
gs = GridSpec(3, 2, figure=fig, hspace=0.3, wspace=0.25)
# Convert time to years for readability
time_years = results['TIME'] / 365
# 1. Oil and Water Production Rates
ax1 = fig.add_subplot(gs[0, 0])
ax1.plot(time_years, results['FOPR'], 'g-', linewidth=2, label='Oil Rate (FOPR)')
ax1.plot(time_years, results['FWPR'], 'b-', linewidth=2, label='Water Rate (FWPR)')
ax1.set_xlabel('Time (years)')
ax1.set_ylabel('Rate (smยณ/day)')
ax1.set_title('Field Production Rates')
ax1.legend()
ax1.grid(True, alpha=0.3)
ax1.set_xlim(0, time_years.max())
# 2. Water Cut
ax2 = fig.add_subplot(gs[0, 1])
ax2.plot(time_years, results['FWCT'] * 100, 'r-', linewidth=2)
ax2.fill_between(time_years, 0, results['FWCT'] * 100, alpha=0.3, color='red')
ax2.set_xlabel('Time (years)')
ax2.set_ylabel('Water Cut (%)')
ax2.set_title('Field Water Cut')
ax2.set_ylim(0, 100)
ax2.grid(True, alpha=0.3)
ax2.axhline(y=90, color='red', linestyle='--', alpha=0.5, label='Economic limit (90%)')
ax2.legend()
# 3. Reservoir Pressure
ax3 = fig.add_subplot(gs[1, 0])
ax3.plot(time_years, results['FPR'], 'm-', linewidth=2)
ax3.fill_between(time_years, results['FPR'].min()-10, results['FPR'], alpha=0.3, color='purple')
ax3.set_xlabel('Time (years)')
ax3.set_ylabel('Pressure (bar)')
ax3.set_title('Field Average Pressure')
ax3.grid(True, alpha=0.3)
ax3.axhline(y=250, color='red', linestyle='--', alpha=0.5, label='Min pressure')
ax3.legend()
# 4. Cumulative Production
ax4 = fig.add_subplot(gs[1, 1])
ax4.plot(time_years, results['FOPT'] / 1e6, 'g-', linewidth=2, label='Oil Produced')
ax4.plot(time_years, results['FWPT'] / 1e6, 'b-', linewidth=2, label='Water Produced')
ax4.plot(time_years, results['FWIT'] / 1e6, 'c--', linewidth=2, label='Water Injected')
ax4.set_xlabel('Time (years)')
ax4.set_ylabel('Cumulative Volume (million smยณ)')
ax4.set_title('Cumulative Volumes')
ax4.legend()
ax4.grid(True, alpha=0.3)
# 5. Oil Rate vs Water Cut (diagnostic plot)
ax5 = fig.add_subplot(gs[2, 0])
scatter = ax5.scatter(results['FWCT'] * 100, results['FOPR'],
c=time_years, cmap='viridis', s=30, alpha=0.7)
ax5.set_xlabel('Water Cut (%)')
ax5.set_ylabel('Oil Rate (smยณ/day)')
ax5.set_title('Oil Rate vs Water Cut')
plt.colorbar(scatter, ax=ax5, label='Time (years)')
ax5.grid(True, alpha=0.3)
# 6. Key Performance Indicators
ax6 = fig.add_subplot(gs[2, 1])
ax6.axis('off')
# Calculate KPIs
total_oil = results['FOPT'].iloc[-1] / 1e6
total_water_prod = results['FWPT'].iloc[-1] / 1e6
total_water_inj = results['FWIT'].iloc[-1] / 1e6
final_wct = results['FWCT'].iloc[-1] * 100
voidage_ratio = total_water_inj / (total_oil + total_water_prod) if (total_oil + total_water_prod) > 0 else 0
# Original oil in place (rough estimate)
pv = NX * NY * NZ * 100 * 100 * 10 * 0.20 # mยณ
ooip = pv * (1 - 0.20) / 1.02 # smยณ (initial Sw=0.2, Bo=1.02)
recovery_factor = total_oil * 1e6 / ooip * 100 if ooip > 0 else 0
kpi_text = f"""
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ KEY PERFORMANCE INDICATORS โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฃ
โ โ
โ Simulation Period: {time_years.max():.1f} years โ
โ โ
โ PRODUCTION โ
โ โโโโโโโโโโโโโโโโโโโโโ โ
โ Total Oil: {total_oil:>8.2f} million smยณ โ
โ Total Water: {total_water_prod:>8.2f} million smยณ โ
โ Final Water Cut: {final_wct:>8.1f} % โ
โ โ
โ INJECTION โ
โ โโโโโโโโโโโโโโโโโโโโโ โ
โ Total Injected: {total_water_inj:>8.2f} million smยณ โ
โ Voidage Ratio: {voidage_ratio:>8.2f} โ
โ โ
โ RECOVERY โ
โ โโโโโโโโโโโโโโโโโโโโโ โ
โ Est. OOIP: {ooip/1e6:>8.2f} million smยณ โ
โ Recovery Factor: {recovery_factor:>8.1f} % โ
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
"""
ax6.text(0.1, 0.5, kpi_text, transform=ax6.transAxes, fontsize=11,
verticalalignment='center', fontfamily='monospace',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
plt.suptitle('5-Spot Waterflood Simulation Results', fontsize=14, fontweight='bold', y=1.02)
plt.savefig(WORKSPACE / 'production_results.png', dpi=150, bbox_inches='tight')
plt.show()
else:
print("โ No results to plot")
# ๐ Production Plots
if not results.empty:
fig = plt.figure(figsize=(16, 12))
gs = GridSpec(3, 2, figure=fig, hspace=0.3, wspace=0.25)
# Convert time to years for readability
time_years = results['TIME'] / 365
# 1. Oil and Water Production Rates
ax1 = fig.add_subplot(gs[0, 0])
ax1.plot(time_years, results['FOPR'], 'g-', linewidth=2, label='Oil Rate (FOPR)')
ax1.plot(time_years, results['FWPR'], 'b-', linewidth=2, label='Water Rate (FWPR)')
ax1.set_xlabel('Time (years)')
ax1.set_ylabel('Rate (smยณ/day)')
ax1.set_title('Field Production Rates')
ax1.legend()
ax1.grid(True, alpha=0.3)
ax1.set_xlim(0, time_years.max())
# 2. Water Cut
ax2 = fig.add_subplot(gs[0, 1])
ax2.plot(time_years, results['FWCT'] * 100, 'r-', linewidth=2)
ax2.fill_between(time_years, 0, results['FWCT'] * 100, alpha=0.3, color='red')
ax2.set_xlabel('Time (years)')
ax2.set_ylabel('Water Cut (%)')
ax2.set_title('Field Water Cut')
ax2.set_ylim(0, 100)
ax2.grid(True, alpha=0.3)
ax2.axhline(y=90, color='red', linestyle='--', alpha=0.5, label='Economic limit (90%)')
ax2.legend()
# 3. Reservoir Pressure
ax3 = fig.add_subplot(gs[1, 0])
ax3.plot(time_years, results['FPR'], 'm-', linewidth=2)
ax3.fill_between(time_years, results['FPR'].min()-10, results['FPR'], alpha=0.3, color='purple')
ax3.set_xlabel('Time (years)')
ax3.set_ylabel('Pressure (bar)')
ax3.set_title('Field Average Pressure')
ax3.grid(True, alpha=0.3)
ax3.axhline(y=250, color='red', linestyle='--', alpha=0.5, label='Min pressure')
ax3.legend()
# 4. Cumulative Production
ax4 = fig.add_subplot(gs[1, 1])
ax4.plot(time_years, results['FOPT'] / 1e6, 'g-', linewidth=2, label='Oil Produced')
ax4.plot(time_years, results['FWPT'] / 1e6, 'b-', linewidth=2, label='Water Produced')
ax4.plot(time_years, results['FWIT'] / 1e6, 'c--', linewidth=2, label='Water Injected')
ax4.set_xlabel('Time (years)')
ax4.set_ylabel('Cumulative Volume (million smยณ)')
ax4.set_title('Cumulative Volumes')
ax4.legend()
ax4.grid(True, alpha=0.3)
# 5. Oil Rate vs Water Cut (diagnostic plot)
ax5 = fig.add_subplot(gs[2, 0])
scatter = ax5.scatter(results['FWCT'] * 100, results['FOPR'],
c=time_years, cmap='viridis', s=30, alpha=0.7)
ax5.set_xlabel('Water Cut (%)')
ax5.set_ylabel('Oil Rate (smยณ/day)')
ax5.set_title('Oil Rate vs Water Cut')
plt.colorbar(scatter, ax=ax5, label='Time (years)')
ax5.grid(True, alpha=0.3)
# 6. Key Performance Indicators
ax6 = fig.add_subplot(gs[2, 1])
ax6.axis('off')
# Calculate KPIs
total_oil = results['FOPT'].iloc[-1] / 1e6
total_water_prod = results['FWPT'].iloc[-1] / 1e6
total_water_inj = results['FWIT'].iloc[-1] / 1e6
final_wct = results['FWCT'].iloc[-1] * 100
voidage_ratio = total_water_inj / (total_oil + total_water_prod) if (total_oil + total_water_prod) > 0 else 0
# Original oil in place (rough estimate)
pv = NX * NY * NZ * 100 * 100 * 10 * 0.20 # mยณ
ooip = pv * (1 - 0.20) / 1.02 # smยณ (initial Sw=0.2, Bo=1.02)
recovery_factor = total_oil * 1e6 / ooip * 100 if ooip > 0 else 0
kpi_text = f"""
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ KEY PERFORMANCE INDICATORS โ
โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฃ
โ โ
โ Simulation Period: {time_years.max():.1f} years โ
โ โ
โ PRODUCTION โ
โ โโโโโโโโโโโโโโโโโโโโโ โ
โ Total Oil: {total_oil:>8.2f} million smยณ โ
โ Total Water: {total_water_prod:>8.2f} million smยณ โ
โ Final Water Cut: {final_wct:>8.1f} % โ
โ โ
โ INJECTION โ
โ โโโโโโโโโโโโโโโโโโโโโ โ
โ Total Injected: {total_water_inj:>8.2f} million smยณ โ
โ Voidage Ratio: {voidage_ratio:>8.2f} โ
โ โ
โ RECOVERY โ
โ โโโโโโโโโโโโโโโโโโโโโ โ
โ Est. OOIP: {ooip/1e6:>8.2f} million smยณ โ
โ Recovery Factor: {recovery_factor:>8.1f} % โ
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
"""
ax6.text(0.1, 0.5, kpi_text, transform=ax6.transAxes, fontsize=11,
verticalalignment='center', fontfamily='monospace',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
plt.suptitle('5-Spot Waterflood Simulation Results', fontsize=14, fontweight='bold', y=1.02)
plt.savefig(WORKSPACE / 'production_results.png', dpi=150, bbox_inches='tight')
plt.show()
else:
print("โ No results to plot")
5. ๐ฒ 3D Reservoir Visualizationยถ
Now let's visualize the reservoir in 3D:
- Static: Permeability distribution
- Dynamic: Water saturation evolution over time
Inย [ย ]:
Copied!
# ๐ฆ Install 3D visualization libraries
!pip install -q plotly kaleido Pillow
print("โ
Plotly + Kaleido + Pillow installed (for 3D viz & GIF export)")
# ๐ฆ Install 3D visualization libraries
!pip install -q plotly kaleido Pillow
print("โ
Plotly + Kaleido + Pillow installed (for 3D viz & GIF export)")
Inย [ย ]:
Copied!
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
class ReservoirVisualizer3D:
"""3D visualization of reservoir properties."""
def __init__(self, nx, ny, nz, dx=100, dy=100, dz=10):
self.nx, self.ny, self.nz = nx, ny, nz
self.dx, self.dy, self.dz = dx, dy, dz
# Create grid coordinates
self.x = np.arange(nx) * dx
self.y = np.arange(ny) * dy
self.z = np.arange(nz) * dz
def plot_property_3d(self, prop_3d, title, colorscale='Viridis',
opacity=0.6, show_wells=True):
"""Plot a 3D property (e.g., permeability, saturation)."""
# Create meshgrid for cell centers
X, Y, Z = np.meshgrid(
self.x + self.dx/2,
self.y + self.dy/2,
self.z + self.dz/2,
indexing='ij'
)
# Flatten for scatter plot
x_flat = X.flatten()
y_flat = Y.flatten()
z_flat = Z.flatten()
values = prop_3d.flatten()
# Filter out very low values for better visualization
mask = values > np.percentile(values, 5)
fig = go.Figure()
# Add property as 3D scatter (volumetric effect)
fig.add_trace(go.Scatter3d(
x=x_flat[mask],
y=y_flat[mask],
z=-z_flat[mask], # Negative Z for depth
mode='markers',
marker=dict(
size=4,
color=values[mask],
colorscale=colorscale,
opacity=opacity,
colorbar=dict(title=title.split('(')[-1].replace(')', '') if '(' in title else ''),
),
hovertemplate=f'X: %{{x:.0f}}m<br>Y: %{{y:.0f}}m<br>Depth: %{{z:.0f}}m<br>{title}: %{{marker.color:.1f}}<extra></extra>'
))
# Add wells
if show_wells:
well_positions = [
(self.dx/2, self.dy/2, 'INJ1', 'blue'), # Corner injectors
(self.x[-1]+self.dx/2, self.dy/2, 'INJ2', 'blue'),
(self.dx/2, self.y[-1]+self.dy/2, 'INJ3', 'blue'),
(self.x[-1]+self.dx/2, self.y[-1]+self.dy/2, 'INJ4', 'blue'),
(self.x[self.nx//2]+self.dx/2, self.y[self.ny//2]+self.dy/2, 'PROD', 'red'), # Center producer
]
for wx, wy, name, color in well_positions:
# Well trajectory (vertical line)
fig.add_trace(go.Scatter3d(
x=[wx, wx],
y=[wy, wy],
z=[0, -self.z[-1]-self.dz],
mode='lines+text',
line=dict(color=color, width=6),
text=[name, ''],
textposition='top center',
showlegend=False,
hoverinfo='text',
hovertext=name
))
fig.update_layout(
title=dict(text=f'๐ฒ {title}', font=dict(size=20)),
scene=dict(
xaxis_title='X (m)',
yaxis_title='Y (m)',
zaxis_title='Depth (m)',
aspectmode='data',
camera=dict(
eye=dict(x=1.5, y=1.5, z=0.8)
)
),
width=800,
height=600,
margin=dict(l=0, r=0, t=40, b=0)
)
return fig
def create_saturation_animation(self, saturation_over_time, times):
"""Create animated 3D visualization of saturation."""
X, Y, Z = np.meshgrid(
self.x + self.dx/2,
self.y + self.dy/2,
self.z + self.dz/2,
indexing='ij'
)
x_flat = X.flatten()
y_flat = Y.flatten()
z_flat = Z.flatten()
# Create frames
frames = []
for i, (sat, time) in enumerate(zip(saturation_over_time, times)):
values = sat.flatten()
frames.append(go.Frame(
data=[go.Scatter3d(
x=x_flat,
y=y_flat,
z=-z_flat,
mode='markers',
marker=dict(
size=4,
color=values,
colorscale='RdYlBu_r',
cmin=0, cmax=1,
opacity=0.7,
),
)],
name=str(i),
layout=go.Layout(title=f'Water Saturation at {time:.0f} days')
))
# Initial frame
initial_values = saturation_over_time[0].flatten()
fig = go.Figure(
data=[go.Scatter3d(
x=x_flat,
y=y_flat,
z=-z_flat,
mode='markers',
marker=dict(
size=4,
color=initial_values,
colorscale='RdYlBu_r',
cmin=0, cmax=1,
opacity=0.7,
colorbar=dict(title='Sw'),
),
)],
frames=frames
)
# Add animation controls
fig.update_layout(
title='๐ Water Saturation Evolution',
scene=dict(
xaxis_title='X (m)',
yaxis_title='Y (m)',
zaxis_title='Depth (m)',
aspectmode='data',
camera=dict(eye=dict(x=1.5, y=1.5, z=0.8))
),
updatemenus=[dict(
type='buttons',
showactive=False,
y=0,
x=0.1,
xanchor='right',
yanchor='top',
buttons=[
dict(label='โถ๏ธ Play',
method='animate',
args=[None, dict(frame=dict(duration=500, redraw=True),
fromcurrent=True)]),
dict(label='โธ๏ธ Pause',
method='animate',
args=[[None], dict(frame=dict(duration=0, redraw=False),
mode='immediate')])
]
)],
sliders=[dict(
active=0,
yanchor='top',
xanchor='left',
currentvalue=dict(
prefix='Time: ',
suffix=' days',
visible=True,
xanchor='right'
),
steps=[dict(args=[[str(i)], dict(frame=dict(duration=0, redraw=True),
mode='immediate')],
label=f'{t:.0f}',
method='animate')
for i, t in enumerate(times)]
)],
width=800,
height=650,
)
return fig
def plot_cross_section_xy(self, prop_3d, layer_k, title, colorscale='Viridis'):
"""Plot horizontal cross-section at layer k (X-Y plane)."""
import plotly.express as px
# Extract layer
layer_data = prop_3d[:, :, layer_k]
depth = self.z[layer_k] + self.dz/2
fig = px.imshow(
layer_data.T, # Transpose for correct orientation
x=self.x + self.dx/2,
y=self.y + self.dy/2,
origin='lower',
color_continuous_scale=colorscale,
labels={'x': 'X (m)', 'y': 'Y (m)', 'color': title.split('(')[-1].replace(')', '')},
aspect='equal'
)
# Add well markers
well_positions = [
(self.dx/2, self.dy/2, 'INJ1', 'blue', 'โฒ'),
(self.x[-1]+self.dx/2, self.dy/2, 'INJ2', 'blue', 'โฒ'),
(self.dx/2, self.y[-1]+self.dy/2, 'INJ3', 'blue', 'โฒ'),
(self.x[-1]+self.dx/2, self.y[-1]+self.dy/2, 'INJ4', 'blue', 'โฒ'),
(self.x[self.nx//2]+self.dx/2, self.y[self.ny//2]+self.dy/2, 'PROD', 'red', 'โ'),
]
for wx, wy, name, color, marker in well_positions:
fig.add_scatter(
x=[wx], y=[wy],
mode='markers+text',
marker=dict(size=12, color=color, symbol='circle'),
text=[name],
textposition='top center',
showlegend=False
)
fig.update_layout(
title=f'๐ {title} - Layer {layer_k+1} (Depth: {depth:.0f}m)',
width=700, height=600
)
return fig
def plot_cross_section_xz(self, prop_3d, j_slice, title, colorscale='Viridis'):
"""Plot vertical cross-section at j (X-Z plane)."""
import plotly.express as px
# Extract slice
slice_data = prop_3d[:, j_slice, :]
y_pos = self.y[j_slice] + self.dy/2
fig = px.imshow(
slice_data.T, # Transpose: rows=depth, cols=X
x=self.x + self.dx/2,
y=-(self.z + self.dz/2), # Negative for depth
origin='upper',
color_continuous_scale=colorscale,
labels={'x': 'X (m)', 'y': 'Depth (m)', 'color': title.split('(')[-1].replace(')', '')},
aspect='auto'
)
fig.update_layout(
title=f'๐ {title} - Y={y_pos:.0f}m (X-Z Cross-Section)',
width=800, height=400,
yaxis=dict(autorange='reversed') # Depth increases downward
)
return fig
def plot_cross_section_yz(self, prop_3d, i_slice, title, colorscale='Viridis'):
"""Plot vertical cross-section at i (Y-Z plane)."""
import plotly.express as px
# Extract slice
slice_data = prop_3d[i_slice, :, :]
x_pos = self.x[i_slice] + self.dx/2
fig = px.imshow(
slice_data.T,
x=self.y + self.dy/2,
y=-(self.z + self.dz/2),
origin='upper',
color_continuous_scale=colorscale,
labels={'x': 'Y (m)', 'y': 'Depth (m)', 'color': title.split('(')[-1].replace(')', '')},
aspect='auto'
)
fig.update_layout(
title=f'๐ {title} - X={x_pos:.0f}m (Y-Z Cross-Section)',
width=800, height=400,
yaxis=dict(autorange='reversed')
)
return fig
def export_animation_gif(self, saturation_over_time, times, filename='saturation_animation.gif',
fps=2, camera_angle=None):
"""Export saturation animation as GIF.
Args:
saturation_over_time: List of 3D saturation arrays
times: List of time values
filename: Output filename
fps: Frames per second
camera_angle: Optional dict with 'eye' position
"""
import io
from PIL import Image
# Use kaleido for rendering
frames_images = []
print(f"๐ฌ Rendering {len(times)} frames...")
for idx, (sat, time) in enumerate(zip(saturation_over_time, times)):
# Create single frame figure
X, Y, Z = np.meshgrid(
self.x + self.dx/2,
self.y + self.dy/2,
self.z + self.dz/2,
indexing='ij'
)
values = sat.flatten()
fig = go.Figure(data=[go.Scatter3d(
x=X.flatten(),
y=Y.flatten(),
z=-Z.flatten(),
mode='markers',
marker=dict(
size=4,
color=values,
colorscale='RdYlBu_r',
cmin=0, cmax=1,
opacity=0.7,
colorbar=dict(title='Sw'),
),
)])
camera = camera_angle or dict(eye=dict(x=1.5, y=1.5, z=0.8))
fig.update_layout(
title=dict(text=f'๐ Water Saturation - Day {time:.0f}', font=dict(size=18)),
scene=dict(
xaxis_title='X (m)',
yaxis_title='Y (m)',
zaxis_title='Depth (m)',
aspectmode='data',
camera=camera
),
width=600,
height=500,
margin=dict(l=0, r=0, t=50, b=0)
)
# Render to bytes
img_bytes = fig.to_image(format='png', scale=1.5)
img = Image.open(io.BytesIO(img_bytes))
frames_images.append(img)
print(f" Frame {idx+1}/{len(times)}: Day {time:.0f}", end='\r')
print(f"\n๐พ Saving GIF to {filename}...")
# Save as GIF
frames_images[0].save(
filename,
save_all=True,
append_images=frames_images[1:],
duration=int(1000/fps), # milliseconds per frame
loop=0 # Infinite loop
)
print(f"โ
Animation saved: {filename} ({len(frames_images)} frames)")
return filename
print("โ
ReservoirVisualizer3D class ready")
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
class ReservoirVisualizer3D:
"""3D visualization of reservoir properties."""
def __init__(self, nx, ny, nz, dx=100, dy=100, dz=10):
self.nx, self.ny, self.nz = nx, ny, nz
self.dx, self.dy, self.dz = dx, dy, dz
# Create grid coordinates
self.x = np.arange(nx) * dx
self.y = np.arange(ny) * dy
self.z = np.arange(nz) * dz
def plot_property_3d(self, prop_3d, title, colorscale='Viridis',
opacity=0.6, show_wells=True):
"""Plot a 3D property (e.g., permeability, saturation)."""
# Create meshgrid for cell centers
X, Y, Z = np.meshgrid(
self.x + self.dx/2,
self.y + self.dy/2,
self.z + self.dz/2,
indexing='ij'
)
# Flatten for scatter plot
x_flat = X.flatten()
y_flat = Y.flatten()
z_flat = Z.flatten()
values = prop_3d.flatten()
# Filter out very low values for better visualization
mask = values > np.percentile(values, 5)
fig = go.Figure()
# Add property as 3D scatter (volumetric effect)
fig.add_trace(go.Scatter3d(
x=x_flat[mask],
y=y_flat[mask],
z=-z_flat[mask], # Negative Z for depth
mode='markers',
marker=dict(
size=4,
color=values[mask],
colorscale=colorscale,
opacity=opacity,
colorbar=dict(title=title.split('(')[-1].replace(')', '') if '(' in title else ''),
),
hovertemplate=f'X: %{{x:.0f}}m<br>Y: %{{y:.0f}}m<br>Depth: %{{z:.0f}}m<br>{title}: %{{marker.color:.1f}}<extra></extra>'
))
# Add wells
if show_wells:
well_positions = [
(self.dx/2, self.dy/2, 'INJ1', 'blue'), # Corner injectors
(self.x[-1]+self.dx/2, self.dy/2, 'INJ2', 'blue'),
(self.dx/2, self.y[-1]+self.dy/2, 'INJ3', 'blue'),
(self.x[-1]+self.dx/2, self.y[-1]+self.dy/2, 'INJ4', 'blue'),
(self.x[self.nx//2]+self.dx/2, self.y[self.ny//2]+self.dy/2, 'PROD', 'red'), # Center producer
]
for wx, wy, name, color in well_positions:
# Well trajectory (vertical line)
fig.add_trace(go.Scatter3d(
x=[wx, wx],
y=[wy, wy],
z=[0, -self.z[-1]-self.dz],
mode='lines+text',
line=dict(color=color, width=6),
text=[name, ''],
textposition='top center',
showlegend=False,
hoverinfo='text',
hovertext=name
))
fig.update_layout(
title=dict(text=f'๐ฒ {title}', font=dict(size=20)),
scene=dict(
xaxis_title='X (m)',
yaxis_title='Y (m)',
zaxis_title='Depth (m)',
aspectmode='data',
camera=dict(
eye=dict(x=1.5, y=1.5, z=0.8)
)
),
width=800,
height=600,
margin=dict(l=0, r=0, t=40, b=0)
)
return fig
def create_saturation_animation(self, saturation_over_time, times):
"""Create animated 3D visualization of saturation."""
X, Y, Z = np.meshgrid(
self.x + self.dx/2,
self.y + self.dy/2,
self.z + self.dz/2,
indexing='ij'
)
x_flat = X.flatten()
y_flat = Y.flatten()
z_flat = Z.flatten()
# Create frames
frames = []
for i, (sat, time) in enumerate(zip(saturation_over_time, times)):
values = sat.flatten()
frames.append(go.Frame(
data=[go.Scatter3d(
x=x_flat,
y=y_flat,
z=-z_flat,
mode='markers',
marker=dict(
size=4,
color=values,
colorscale='RdYlBu_r',
cmin=0, cmax=1,
opacity=0.7,
),
)],
name=str(i),
layout=go.Layout(title=f'Water Saturation at {time:.0f} days')
))
# Initial frame
initial_values = saturation_over_time[0].flatten()
fig = go.Figure(
data=[go.Scatter3d(
x=x_flat,
y=y_flat,
z=-z_flat,
mode='markers',
marker=dict(
size=4,
color=initial_values,
colorscale='RdYlBu_r',
cmin=0, cmax=1,
opacity=0.7,
colorbar=dict(title='Sw'),
),
)],
frames=frames
)
# Add animation controls
fig.update_layout(
title='๐ Water Saturation Evolution',
scene=dict(
xaxis_title='X (m)',
yaxis_title='Y (m)',
zaxis_title='Depth (m)',
aspectmode='data',
camera=dict(eye=dict(x=1.5, y=1.5, z=0.8))
),
updatemenus=[dict(
type='buttons',
showactive=False,
y=0,
x=0.1,
xanchor='right',
yanchor='top',
buttons=[
dict(label='โถ๏ธ Play',
method='animate',
args=[None, dict(frame=dict(duration=500, redraw=True),
fromcurrent=True)]),
dict(label='โธ๏ธ Pause',
method='animate',
args=[[None], dict(frame=dict(duration=0, redraw=False),
mode='immediate')])
]
)],
sliders=[dict(
active=0,
yanchor='top',
xanchor='left',
currentvalue=dict(
prefix='Time: ',
suffix=' days',
visible=True,
xanchor='right'
),
steps=[dict(args=[[str(i)], dict(frame=dict(duration=0, redraw=True),
mode='immediate')],
label=f'{t:.0f}',
method='animate')
for i, t in enumerate(times)]
)],
width=800,
height=650,
)
return fig
def plot_cross_section_xy(self, prop_3d, layer_k, title, colorscale='Viridis'):
"""Plot horizontal cross-section at layer k (X-Y plane)."""
import plotly.express as px
# Extract layer
layer_data = prop_3d[:, :, layer_k]
depth = self.z[layer_k] + self.dz/2
fig = px.imshow(
layer_data.T, # Transpose for correct orientation
x=self.x + self.dx/2,
y=self.y + self.dy/2,
origin='lower',
color_continuous_scale=colorscale,
labels={'x': 'X (m)', 'y': 'Y (m)', 'color': title.split('(')[-1].replace(')', '')},
aspect='equal'
)
# Add well markers
well_positions = [
(self.dx/2, self.dy/2, 'INJ1', 'blue', 'โฒ'),
(self.x[-1]+self.dx/2, self.dy/2, 'INJ2', 'blue', 'โฒ'),
(self.dx/2, self.y[-1]+self.dy/2, 'INJ3', 'blue', 'โฒ'),
(self.x[-1]+self.dx/2, self.y[-1]+self.dy/2, 'INJ4', 'blue', 'โฒ'),
(self.x[self.nx//2]+self.dx/2, self.y[self.ny//2]+self.dy/2, 'PROD', 'red', 'โ'),
]
for wx, wy, name, color, marker in well_positions:
fig.add_scatter(
x=[wx], y=[wy],
mode='markers+text',
marker=dict(size=12, color=color, symbol='circle'),
text=[name],
textposition='top center',
showlegend=False
)
fig.update_layout(
title=f'๐ {title} - Layer {layer_k+1} (Depth: {depth:.0f}m)',
width=700, height=600
)
return fig
def plot_cross_section_xz(self, prop_3d, j_slice, title, colorscale='Viridis'):
"""Plot vertical cross-section at j (X-Z plane)."""
import plotly.express as px
# Extract slice
slice_data = prop_3d[:, j_slice, :]
y_pos = self.y[j_slice] + self.dy/2
fig = px.imshow(
slice_data.T, # Transpose: rows=depth, cols=X
x=self.x + self.dx/2,
y=-(self.z + self.dz/2), # Negative for depth
origin='upper',
color_continuous_scale=colorscale,
labels={'x': 'X (m)', 'y': 'Depth (m)', 'color': title.split('(')[-1].replace(')', '')},
aspect='auto'
)
fig.update_layout(
title=f'๐ {title} - Y={y_pos:.0f}m (X-Z Cross-Section)',
width=800, height=400,
yaxis=dict(autorange='reversed') # Depth increases downward
)
return fig
def plot_cross_section_yz(self, prop_3d, i_slice, title, colorscale='Viridis'):
"""Plot vertical cross-section at i (Y-Z plane)."""
import plotly.express as px
# Extract slice
slice_data = prop_3d[i_slice, :, :]
x_pos = self.x[i_slice] + self.dx/2
fig = px.imshow(
slice_data.T,
x=self.y + self.dy/2,
y=-(self.z + self.dz/2),
origin='upper',
color_continuous_scale=colorscale,
labels={'x': 'Y (m)', 'y': 'Depth (m)', 'color': title.split('(')[-1].replace(')', '')},
aspect='auto'
)
fig.update_layout(
title=f'๐ {title} - X={x_pos:.0f}m (Y-Z Cross-Section)',
width=800, height=400,
yaxis=dict(autorange='reversed')
)
return fig
def export_animation_gif(self, saturation_over_time, times, filename='saturation_animation.gif',
fps=2, camera_angle=None):
"""Export saturation animation as GIF.
Args:
saturation_over_time: List of 3D saturation arrays
times: List of time values
filename: Output filename
fps: Frames per second
camera_angle: Optional dict with 'eye' position
"""
import io
from PIL import Image
# Use kaleido for rendering
frames_images = []
print(f"๐ฌ Rendering {len(times)} frames...")
for idx, (sat, time) in enumerate(zip(saturation_over_time, times)):
# Create single frame figure
X, Y, Z = np.meshgrid(
self.x + self.dx/2,
self.y + self.dy/2,
self.z + self.dz/2,
indexing='ij'
)
values = sat.flatten()
fig = go.Figure(data=[go.Scatter3d(
x=X.flatten(),
y=Y.flatten(),
z=-Z.flatten(),
mode='markers',
marker=dict(
size=4,
color=values,
colorscale='RdYlBu_r',
cmin=0, cmax=1,
opacity=0.7,
colorbar=dict(title='Sw'),
),
)])
camera = camera_angle or dict(eye=dict(x=1.5, y=1.5, z=0.8))
fig.update_layout(
title=dict(text=f'๐ Water Saturation - Day {time:.0f}', font=dict(size=18)),
scene=dict(
xaxis_title='X (m)',
yaxis_title='Y (m)',
zaxis_title='Depth (m)',
aspectmode='data',
camera=camera
),
width=600,
height=500,
margin=dict(l=0, r=0, t=50, b=0)
)
# Render to bytes
img_bytes = fig.to_image(format='png', scale=1.5)
img = Image.open(io.BytesIO(img_bytes))
frames_images.append(img)
print(f" Frame {idx+1}/{len(times)}: Day {time:.0f}", end='\r')
print(f"\n๐พ Saving GIF to {filename}...")
# Save as GIF
frames_images[0].save(
filename,
save_all=True,
append_images=frames_images[1:],
duration=int(1000/fps), # milliseconds per frame
loop=0 # Infinite loop
)
print(f"โ
Animation saved: {filename} ({len(frames_images)} frames)")
return filename
print("โ
ReservoirVisualizer3D class ready")
Inย [ย ]:
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# ๐ฒ Static: 3D Permeability Distribution
# Get grid dimensions from builder
dx, dy, dz = builder.dx, builder.dy, builder.dz
viz = ReservoirVisualizer3D(NX, NY, NZ, dx, dy, dz)
# Get permeability from builder and reshape to 3D
perm = builder.perm_array
perm_3d = perm.reshape((NX, NY, NZ), order='F') # Fortran order for Eclipse
fig_perm = viz.plot_property_3d(
perm_3d,
'Permeability (mD)',
colorscale='Plasma',
opacity=0.5
)
fig_perm.show()
# ๐ฒ Static: 3D Permeability Distribution
# Get grid dimensions from builder
dx, dy, dz = builder.dx, builder.dy, builder.dz
viz = ReservoirVisualizer3D(NX, NY, NZ, dx, dy, dz)
# Get permeability from builder and reshape to 3D
perm = builder.perm_array
perm_3d = perm.reshape((NX, NY, NZ), order='F') # Fortran order for Eclipse
fig_perm = viz.plot_property_3d(
perm_3d,
'Permeability (mD)',
colorscale='Plasma',
opacity=0.5
)
fig_perm.show()
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# ๐ Cross-Sections: Slice through the reservoir
print("๐ Creating cross-section views...")
# X-Y horizontal slice (top layer, middle layer, bottom layer)
fig_xy_top = viz.plot_cross_section_xy(perm_3d, 0, 'Permeability (mD)', 'Plasma')
fig_xy_mid = viz.plot_cross_section_xy(perm_3d, NZ//2, 'Permeability (mD)', 'Plasma')
fig_xy_bot = viz.plot_cross_section_xy(perm_3d, NZ-1, 'Permeability (mD)', 'Plasma')
# X-Z vertical slice through center
fig_xz = viz.plot_cross_section_xz(perm_3d, NY//2, 'Permeability (mD)', 'Plasma')
# Y-Z vertical slice through center
fig_yz = viz.plot_cross_section_yz(perm_3d, NX//2, 'Permeability (mD)', 'Plasma')
# Show the middle layer and vertical slices
print("\n๐น Horizontal slice (middle layer):")
fig_xy_mid.show()
print("\n๐น Vertical X-Z slice (through center):")
fig_xz.show()
print("\n๐น Vertical Y-Z slice (through center):")
fig_yz.show()
print("\n๐ก Tip: Cross-sections help identify flow barriers and high-perm channels!")
# ๐ Cross-Sections: Slice through the reservoir
print("๐ Creating cross-section views...")
# X-Y horizontal slice (top layer, middle layer, bottom layer)
fig_xy_top = viz.plot_cross_section_xy(perm_3d, 0, 'Permeability (mD)', 'Plasma')
fig_xy_mid = viz.plot_cross_section_xy(perm_3d, NZ//2, 'Permeability (mD)', 'Plasma')
fig_xy_bot = viz.plot_cross_section_xy(perm_3d, NZ-1, 'Permeability (mD)', 'Plasma')
# X-Z vertical slice through center
fig_xz = viz.plot_cross_section_xz(perm_3d, NY//2, 'Permeability (mD)', 'Plasma')
# Y-Z vertical slice through center
fig_yz = viz.plot_cross_section_yz(perm_3d, NX//2, 'Permeability (mD)', 'Plasma')
# Show the middle layer and vertical slices
print("\n๐น Horizontal slice (middle layer):")
fig_xy_mid.show()
print("\n๐น Vertical X-Z slice (through center):")
fig_xz.show()
print("\n๐น Vertical Y-Z slice (through center):")
fig_yz.show()
print("\n๐ก Tip: Cross-sections help identify flow barriers and high-perm channels!")
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# ๐ Dynamic: Water Saturation Animation
def read_saturation_from_unrst(workspace, case_name, nx, ny, nz):
"""Read water saturation from OPM UNRST file.
Returns:
tuple: (list of 3D saturation arrays, list of times) or (None, None) if failed
"""
unrst_path = workspace / f"{case_name}.UNRST"
if not unrst_path.exists():
print(f" โ ๏ธ UNRST file not found: {unrst_path.name}")
return None, None
try:
from opm.io.ecl import EclFile
print(f" ๐ Reading {unrst_path.name}...")
rst = EclFile(str(unrst_path))
# Get available report steps
report_steps = rst.report_steps
print(f" Found {len(report_steps)} report steps")
saturations = []
times = []
# Read SWAT at each report step
for step in report_steps:
try:
# Get water saturation
swat = np.array(rst['SWAT', step])
# Reshape to 3D (Eclipse uses Fortran ordering)
swat_3d = swat.reshape((nx, ny, nz), order='F')
saturations.append(swat_3d)
# Get time (days)
# Note: opm.io might have time in INTEHEAD or we estimate
times.append(step * 30) # Approximate: step * typical timestep
except Exception as e:
print(f" โ ๏ธ Could not read step {step}: {e}")
continue
if saturations:
print(f" โ
Read {len(saturations)} saturation snapshots")
return saturations, np.array(times)
else:
return None, None
except ImportError:
print(" โ ๏ธ opm.io not available")
return None, None
except Exception as e:
print(f" โ ๏ธ Error reading UNRST: {e}")
return None, None
def generate_synthetic_saturation(nx, ny, nz, n_timesteps=12):
"""Generate synthetic waterflood saturation evolution (fallback)."""
swc = 0.2 # Connate water
# Well positions
injectors = [(0, 0), (nx-1, 0), (0, ny-1), (nx-1, ny-1)]
producer = (nx//2, ny//2)
times = np.linspace(0, 1800, n_timesteps) # 5 years
saturations = []
for t in times:
sat = np.ones((nx, ny, nz)) * swc
# Water front propagates from injectors
for inj_x, inj_y in injectors:
for i in range(nx):
for j in range(ny):
dist = np.sqrt((i - inj_x)**2 + (j - inj_y)**2)
front_radius = 0.015 * t * (1 + 0.1 * np.random.randn())
if dist < front_radius:
sat_increase = (1 - swc - 0.2) * (1 - dist / max(front_radius, 1))
for k in range(nz):
gravity_factor = 1 + 0.1 * (k / nz)
sat[i, j, k] = min(0.8, swc + sat_increase * gravity_factor)
saturations.append(sat)
return saturations, times
# Try to read real simulation results first
print("๐ Loading water saturation data...")
sat_evolution, times = read_saturation_from_unrst(WORKSPACE, 'FIVESPOT', NX, NY, NZ)
if sat_evolution is None:
print("\n๐ Using synthetic saturation (real data not available)")
print(" This generates a realistic waterflood pattern for visualization demo")
sat_evolution, times = generate_synthetic_saturation(NX, NY, NZ, n_timesteps=12)
else:
print("\nโ
Using REAL simulation results!")
print(f" {len(times)} timesteps loaded")
# Create animation
fig_sat = viz.create_saturation_animation(sat_evolution, times)
fig_sat.show()
print("\n๐ก Tip: Click โถ๏ธ Play or drag the slider to see water advancing!")
# ๐ Dynamic: Water Saturation Animation
def read_saturation_from_unrst(workspace, case_name, nx, ny, nz):
"""Read water saturation from OPM UNRST file.
Returns:
tuple: (list of 3D saturation arrays, list of times) or (None, None) if failed
"""
unrst_path = workspace / f"{case_name}.UNRST"
if not unrst_path.exists():
print(f" โ ๏ธ UNRST file not found: {unrst_path.name}")
return None, None
try:
from opm.io.ecl import EclFile
print(f" ๐ Reading {unrst_path.name}...")
rst = EclFile(str(unrst_path))
# Get available report steps
report_steps = rst.report_steps
print(f" Found {len(report_steps)} report steps")
saturations = []
times = []
# Read SWAT at each report step
for step in report_steps:
try:
# Get water saturation
swat = np.array(rst['SWAT', step])
# Reshape to 3D (Eclipse uses Fortran ordering)
swat_3d = swat.reshape((nx, ny, nz), order='F')
saturations.append(swat_3d)
# Get time (days)
# Note: opm.io might have time in INTEHEAD or we estimate
times.append(step * 30) # Approximate: step * typical timestep
except Exception as e:
print(f" โ ๏ธ Could not read step {step}: {e}")
continue
if saturations:
print(f" โ
Read {len(saturations)} saturation snapshots")
return saturations, np.array(times)
else:
return None, None
except ImportError:
print(" โ ๏ธ opm.io not available")
return None, None
except Exception as e:
print(f" โ ๏ธ Error reading UNRST: {e}")
return None, None
def generate_synthetic_saturation(nx, ny, nz, n_timesteps=12):
"""Generate synthetic waterflood saturation evolution (fallback)."""
swc = 0.2 # Connate water
# Well positions
injectors = [(0, 0), (nx-1, 0), (0, ny-1), (nx-1, ny-1)]
producer = (nx//2, ny//2)
times = np.linspace(0, 1800, n_timesteps) # 5 years
saturations = []
for t in times:
sat = np.ones((nx, ny, nz)) * swc
# Water front propagates from injectors
for inj_x, inj_y in injectors:
for i in range(nx):
for j in range(ny):
dist = np.sqrt((i - inj_x)**2 + (j - inj_y)**2)
front_radius = 0.015 * t * (1 + 0.1 * np.random.randn())
if dist < front_radius:
sat_increase = (1 - swc - 0.2) * (1 - dist / max(front_radius, 1))
for k in range(nz):
gravity_factor = 1 + 0.1 * (k / nz)
sat[i, j, k] = min(0.8, swc + sat_increase * gravity_factor)
saturations.append(sat)
return saturations, times
# Try to read real simulation results first
print("๐ Loading water saturation data...")
sat_evolution, times = read_saturation_from_unrst(WORKSPACE, 'FIVESPOT', NX, NY, NZ)
if sat_evolution is None:
print("\n๐ Using synthetic saturation (real data not available)")
print(" This generates a realistic waterflood pattern for visualization demo")
sat_evolution, times = generate_synthetic_saturation(NX, NY, NZ, n_timesteps=12)
else:
print("\nโ
Using REAL simulation results!")
print(f" {len(times)} timesteps loaded")
# Create animation
fig_sat = viz.create_saturation_animation(sat_evolution, times)
fig_sat.show()
print("\n๐ก Tip: Click โถ๏ธ Play or drag the slider to see water advancing!")
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# ๐ธ Save 3D visualizations
# Save as interactive HTML
fig_perm.write_html(WORKSPACE / 'permeability_3d.html')
fig_sat.write_html(WORKSPACE / 'saturation_animation.html')
print("๐พ Saved interactive 3D visualizations:")
print(f" ๐ {WORKSPACE / 'permeability_3d.html'}")
print(f" ๐ {WORKSPACE / 'saturation_animation.html'}")
# ๐ฌ Export animation as GIF
print("\n๐ฌ Creating GIF animation...")
try:
gif_path = viz.export_animation_gif(
sat_evolution,
times,
filename=str(WORKSPACE / 'saturation_animation.gif'),
fps=2
)
print(f"\n๐ก Download the GIF to view the animation offline!")
except Exception as e:
print(f"โ ๏ธ GIF export failed: {e}")
print(" (kaleido may need: !pip install -q kaleido)")
print("\n๐ก Download these files to view locally with full interactivity!")
# ๐ธ Save 3D visualizations
# Save as interactive HTML
fig_perm.write_html(WORKSPACE / 'permeability_3d.html')
fig_sat.write_html(WORKSPACE / 'saturation_animation.html')
print("๐พ Saved interactive 3D visualizations:")
print(f" ๐ {WORKSPACE / 'permeability_3d.html'}")
print(f" ๐ {WORKSPACE / 'saturation_animation.html'}")
# ๐ฌ Export animation as GIF
print("\n๐ฌ Creating GIF animation...")
try:
gif_path = viz.export_animation_gif(
sat_evolution,
times,
filename=str(WORKSPACE / 'saturation_animation.gif'),
fps=2
)
print(f"\n๐ก Download the GIF to view the animation offline!")
except Exception as e:
print(f"โ ๏ธ GIF export failed: {e}")
print(" (kaleido may need: !pip install -q kaleido)")
print("\n๐ก Download these files to view locally with full interactivity!")
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# ๐ฌ Run Permeability Sensitivity
print("๐ฌ Running permeability sensitivity analysis...\n")
perm_cases = {
'PERM_050': {'perm': 50, 'heterogeneous': False},
'PERM_100': {'perm': 100, 'heterogeneous': False}, # Base case
'PERM_200': {'perm': 200, 'heterogeneous': False},
}
sensitivity_results = {}
for name, params in perm_cases.items():
print(f" Running {name} (k={params['perm']} mD)...", end=" ")
result = run_sensitivity_case(name, **params)
if result is not None and not result.empty:
sensitivity_results[name] = result
print("โ")
else:
print("โ")
print(f"\nโ
Completed {len(sensitivity_results)} sensitivity cases")
# ๐ฌ Run Permeability Sensitivity
print("๐ฌ Running permeability sensitivity analysis...\n")
perm_cases = {
'PERM_050': {'perm': 50, 'heterogeneous': False},
'PERM_100': {'perm': 100, 'heterogeneous': False}, # Base case
'PERM_200': {'perm': 200, 'heterogeneous': False},
}
sensitivity_results = {}
for name, params in perm_cases.items():
print(f" Running {name} (k={params['perm']} mD)...", end=" ")
result = run_sensitivity_case(name, **params)
if result is not None and not result.empty:
sensitivity_results[name] = result
print("โ")
else:
print("โ")
print(f"\nโ
Completed {len(sensitivity_results)} sensitivity cases")
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# ๐ Plot Sensitivity Results
if sensitivity_results:
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
colors = {'PERM_050': 'red', 'PERM_100': 'green', 'PERM_200': 'blue'}
labels = {'PERM_050': 'k=50 mD', 'PERM_100': 'k=100 mD (base)', 'PERM_200': 'k=200 mD'}
for name, df in sensitivity_results.items():
time_years = df['TIME'] / 365
color = colors.get(name, 'gray')
label = labels.get(name, name)
axes[0, 0].plot(time_years, df['FOPR'], color=color, linewidth=2, label=label)
axes[0, 1].plot(time_years, df['FWCT'] * 100, color=color, linewidth=2, label=label)
axes[1, 0].plot(time_years, df['FOPT'] / 1e6, color=color, linewidth=2, label=label)
axes[1, 1].plot(time_years, df['FPR'], color=color, linewidth=2, label=label)
axes[0, 0].set_title('Oil Production Rate')
axes[0, 0].set_xlabel('Time (years)')
axes[0, 0].set_ylabel('FOPR (smยณ/day)')
axes[0, 0].legend()
axes[0, 0].grid(True, alpha=0.3)
axes[0, 1].set_title('Water Cut')
axes[0, 1].set_xlabel('Time (years)')
axes[0, 1].set_ylabel('Water Cut (%)')
axes[0, 1].legend()
axes[0, 1].grid(True, alpha=0.3)
axes[1, 0].set_title('Cumulative Oil Production')
axes[1, 0].set_xlabel('Time (years)')
axes[1, 0].set_ylabel('FOPT (million smยณ)')
axes[1, 0].legend()
axes[1, 0].grid(True, alpha=0.3)
axes[1, 1].set_title('Field Pressure')
axes[1, 1].set_xlabel('Time (years)')
axes[1, 1].set_ylabel('FPR (bar)')
axes[1, 1].legend()
axes[1, 1].grid(True, alpha=0.3)
plt.suptitle('Permeability Sensitivity Analysis', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(WORKSPACE / 'sensitivity_results.png', dpi=150)
plt.show()
# ๐ Plot Sensitivity Results
if sensitivity_results:
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
colors = {'PERM_050': 'red', 'PERM_100': 'green', 'PERM_200': 'blue'}
labels = {'PERM_050': 'k=50 mD', 'PERM_100': 'k=100 mD (base)', 'PERM_200': 'k=200 mD'}
for name, df in sensitivity_results.items():
time_years = df['TIME'] / 365
color = colors.get(name, 'gray')
label = labels.get(name, name)
axes[0, 0].plot(time_years, df['FOPR'], color=color, linewidth=2, label=label)
axes[0, 1].plot(time_years, df['FWCT'] * 100, color=color, linewidth=2, label=label)
axes[1, 0].plot(time_years, df['FOPT'] / 1e6, color=color, linewidth=2, label=label)
axes[1, 1].plot(time_years, df['FPR'], color=color, linewidth=2, label=label)
axes[0, 0].set_title('Oil Production Rate')
axes[0, 0].set_xlabel('Time (years)')
axes[0, 0].set_ylabel('FOPR (smยณ/day)')
axes[0, 0].legend()
axes[0, 0].grid(True, alpha=0.3)
axes[0, 1].set_title('Water Cut')
axes[0, 1].set_xlabel('Time (years)')
axes[0, 1].set_ylabel('Water Cut (%)')
axes[0, 1].legend()
axes[0, 1].grid(True, alpha=0.3)
axes[1, 0].set_title('Cumulative Oil Production')
axes[1, 0].set_xlabel('Time (years)')
axes[1, 0].set_ylabel('FOPT (million smยณ)')
axes[1, 0].legend()
axes[1, 0].grid(True, alpha=0.3)
axes[1, 1].set_title('Field Pressure')
axes[1, 1].set_xlabel('Time (years)')
axes[1, 1].set_ylabel('FPR (bar)')
axes[1, 1].legend()
axes[1, 1].grid(True, alpha=0.3)
plt.suptitle('Permeability Sensitivity Analysis', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(WORKSPACE / 'sensitivity_results.png', dpi=150)
plt.show()
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# ๐ฌ Sensitivity Analysis Setup
def run_sensitivity_case(case_name: str, **params) -> Optional[pd.DataFrame]:
"""Run a single sensitivity case."""
# Build deck with modified parameters
case_builder = EclipseDeckBuilder(case_name, NX, NY, NZ)
# Merge with base parameters
base_params = {
'title': f'Sensitivity: {case_name}',
'dx': 100, 'dy': 100, 'dz': 10,
'top': 2500,
'poro': 0.20,
'perm': 100,
'heterogeneous': True,
'datum_depth': 2525,
'pressure': 300,
'woc': 2600,
'wells': wells,
'timesteps': [30] * 24 # 2 years for faster runs
}
base_params.update(params)
deck = case_builder.build_complete_deck(**base_params)
# Save and run
case_path = WORKSPACE / f"{case_name}.DATA"
case_path.write_text(deck)
if OPM_AVAILABLE:
result = subprocess.run(
['flow', str(case_path)],
capture_output=True, text=True,
cwd=WORKSPACE, timeout=300
)
if result.returncode == 0:
parser = OPMResultParser(WORKSPACE, case_name)
return parser.parse()
# Return mock results for demo
parser = OPMResultParser(WORKSPACE, case_name)
return parser._generate_mock_results()
print("โ
Sensitivity analysis functions ready")
# ๐ฌ Sensitivity Analysis Setup
def run_sensitivity_case(case_name: str, **params) -> Optional[pd.DataFrame]:
"""Run a single sensitivity case."""
# Build deck with modified parameters
case_builder = EclipseDeckBuilder(case_name, NX, NY, NZ)
# Merge with base parameters
base_params = {
'title': f'Sensitivity: {case_name}',
'dx': 100, 'dy': 100, 'dz': 10,
'top': 2500,
'poro': 0.20,
'perm': 100,
'heterogeneous': True,
'datum_depth': 2525,
'pressure': 300,
'woc': 2600,
'wells': wells,
'timesteps': [30] * 24 # 2 years for faster runs
}
base_params.update(params)
deck = case_builder.build_complete_deck(**base_params)
# Save and run
case_path = WORKSPACE / f"{case_name}.DATA"
case_path.write_text(deck)
if OPM_AVAILABLE:
result = subprocess.run(
['flow', str(case_path)],
capture_output=True, text=True,
cwd=WORKSPACE, timeout=300
)
if result.returncode == 0:
parser = OPMResultParser(WORKSPACE, case_name)
return parser.parse()
# Return mock results for demo
parser = OPMResultParser(WORKSPACE, case_name)
return parser._generate_mock_results()
print("โ
Sensitivity analysis functions ready")
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# ๐ Export Results to CSV
if not results.empty:
csv_path = WORKSPACE / 'simulation_results.csv'
results.to_csv(csv_path, index=False)
print(f"โ
Results exported to: {csv_path.name}")
# Also export sensitivity results
for name, df in sensitivity_results.items():
df.to_csv(WORKSPACE / f'{name}_results.csv', index=False)
print(f"โ
Sensitivity results exported: {len(sensitivity_results)} files")
# ๐ Export Results to CSV
if not results.empty:
csv_path = WORKSPACE / 'simulation_results.csv'
results.to_csv(csv_path, index=False)
print(f"โ
Results exported to: {csv_path.name}")
# Also export sensitivity results
for name, df in sensitivity_results.items():
df.to_csv(WORKSPACE / f'{name}_results.csv', index=False)
print(f"โ
Sensitivity results exported: {len(sensitivity_results)} files")
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# ๐ Generate Summary Report
report = f"""
================================================================================
OPM FLOW SIMULATION REPORT
================================================================================
Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
Model: 5-Spot Waterflood Pattern
MODEL PARAMETERS
--------------------------------------------------------------------------------
Grid Dimensions: {NX} ร {NY} ร {NZ} = {NX*NY*NZ:,} cells
Cell Size: 100m ร 100m ร 10m
Porosity: 20% (mean), heterogeneous
Permeability: 100 mD (mean), log-normal distribution
Initial Pressure: 300 bar
Water-Oil Contact: 2600 m TVD
WELL CONFIGURATION
--------------------------------------------------------------------------------
Producers: 1 (center)
Injectors: 4 (corners)
Pattern: 5-spot
Producer Rate: 500 smยณ/day oil target
Injector Rate: 200 smยณ/day each (800 total)
SIMULATION RESULTS ({time_years.max():.1f} years)
--------------------------------------------------------------------------------
Cumulative Oil: {total_oil:.2f} million smยณ
Cumulative Water: {total_water_prod:.2f} million smยณ produced
{total_water_inj:.2f} million smยณ injected
Final Water Cut: {final_wct:.1f}%
Recovery Factor: {recovery_factor:.1f}% (estimated)
Voidage Ratio: {voidage_ratio:.2f}
SENSITIVITY ANALYSIS
--------------------------------------------------------------------------------
Parameter: Permeability
Cases: 50, 100 (base), 200 mD
Key Finding: Higher permeability accelerates water breakthrough
but increases early oil recovery rate.
OUTPUT FILES
--------------------------------------------------------------------------------
Deck File: {deck_path.name}
Results CSV: simulation_results.csv
Plots: model_overview.png, production_results.png,
sensitivity_results.png
================================================================================
END OF REPORT
================================================================================
"""
report_path = WORKSPACE / 'simulation_report.txt'
report_path.write_text(report)
print(report)
print(f"\n๐ Report saved to: {report_path.name}")
# ๐ Generate Summary Report
report = f"""
================================================================================
OPM FLOW SIMULATION REPORT
================================================================================
Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
Model: 5-Spot Waterflood Pattern
MODEL PARAMETERS
--------------------------------------------------------------------------------
Grid Dimensions: {NX} ร {NY} ร {NZ} = {NX*NY*NZ:,} cells
Cell Size: 100m ร 100m ร 10m
Porosity: 20% (mean), heterogeneous
Permeability: 100 mD (mean), log-normal distribution
Initial Pressure: 300 bar
Water-Oil Contact: 2600 m TVD
WELL CONFIGURATION
--------------------------------------------------------------------------------
Producers: 1 (center)
Injectors: 4 (corners)
Pattern: 5-spot
Producer Rate: 500 smยณ/day oil target
Injector Rate: 200 smยณ/day each (800 total)
SIMULATION RESULTS ({time_years.max():.1f} years)
--------------------------------------------------------------------------------
Cumulative Oil: {total_oil:.2f} million smยณ
Cumulative Water: {total_water_prod:.2f} million smยณ produced
{total_water_inj:.2f} million smยณ injected
Final Water Cut: {final_wct:.1f}%
Recovery Factor: {recovery_factor:.1f}% (estimated)
Voidage Ratio: {voidage_ratio:.2f}
SENSITIVITY ANALYSIS
--------------------------------------------------------------------------------
Parameter: Permeability
Cases: 50, 100 (base), 200 mD
Key Finding: Higher permeability accelerates water breakthrough
but increases early oil recovery rate.
OUTPUT FILES
--------------------------------------------------------------------------------
Deck File: {deck_path.name}
Results CSV: simulation_results.csv
Plots: model_overview.png, production_results.png,
sensitivity_results.png
================================================================================
END OF REPORT
================================================================================
"""
report_path = WORKSPACE / 'simulation_report.txt'
report_path.write_text(report)
print(report)
print(f"\n๐ Report saved to: {report_path.name}")
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# ๐ Final Output Summary
print("\n" + "="*60)
print(" ๐ฌ SHOWCASE COMPLETE")
print("="*60)
print(f"\n๐ Workspace: {WORKSPACE}")
print("\n๐ Generated Files:")
for f in sorted(WORKSPACE.iterdir()):
size = f.stat().st_size
if size > 1024:
size_str = f"{size/1024:.1f} KB"
else:
size_str = f"{size} B"
print(f" {f.name:40s} {size_str:>10s}")
print("\n๐ Quick Links:")
print(" โข OPM Project: https://opm-project.org")
print(" โข CLARISSA: https://gitlab.com/wolfram_laube/blauweiss_llc/irena")
print(" โข Tutorials: https://irena-40cc50.gitlab.io/tutorials/")
# ๐ Final Output Summary
print("\n" + "="*60)
print(" ๐ฌ SHOWCASE COMPLETE")
print("="*60)
print(f"\n๐ Workspace: {WORKSPACE}")
print("\n๐ Generated Files:")
for f in sorted(WORKSPACE.iterdir()):
size = f.stat().st_size
if size > 1024:
size_str = f"{size/1024:.1f} KB"
else:
size_str = f"{size} B"
print(f" {f.name:40s} {size_str:>10s}")
print("\n๐ Quick Links:")
print(" โข OPM Project: https://opm-project.org")
print(" โข CLARISSA: https://gitlab.com/wolfram_laube/blauweiss_llc/irena")
print(" โข Tutorials: https://irena-40cc50.gitlab.io/tutorials/")
๐ What You Learnedยถ
In this showcase, you've completed a full reservoir simulation workflow:
- โ Environment Setup - Installed OPM Flow in Google Colab
- โ Model Building - Created a 5-spot waterflood ECLIPSE deck programmatically
- โ Simulation Execution - Ran OPM Flow and monitored progress
- โ Result Analysis - Parsed output and created production plots
- โ Sensitivity Analysis - Evaluated parameter impacts
- โ Reporting - Exported data and generated summary
๐ Next Stepsยถ
- Try different well patterns (line drive, 9-spot)
- Add more sensitivity parameters (porosity, relative permeability)
- Implement history matching with real field data
- Explore CLARISSA's AI-assisted deck generation
CLARISSA Portal - OPM Flow Complete Showcase
ยฉ 2026 BLAUWEISS EDV LLC