GeoDFN

Geologically Consistent Discrete Fracture Network Generator

Generate ensembles of geologically plausible fracture networks from outcrop statistics - ready for flow and transport simulations.

Publication: Kamel Targhi et al. (2025) - From outcrop observations to dynamic simulations: an efficient workflow for generating ensembles of geologically plausible fracture networks and assessing their impact on flow and transport - Geoenergy

What is GeoDFN?

Fractures are ubiquitous in geological formations and can significantly influence heat and mass transfer in geothermal systems, groundwater management, and CO₂ storage. GeoDFN provides a computationally efficient workflow that bridges outcrop observations and dynamic flow simulations.

GeoDFN uses a hybrid mechanical-statistical approach: fracture lengths, orientations, and spacings are sampled from probability distributions fitted to field data, while fracture placement follows a mechanical rule - fractures are inserted sequentially (longest first), and each new fracture must respect the stress shadow (buffer zone) of existing fractures. This prevents unrealistically close spacing and produces fracture patterns that are geologically consistent without the high computational cost of full fracture growth simulations.

The result is an ensemble of equiprobable fracture networks that honour the statistical properties of the outcrop and can be fed directly into multi-purpose flow and transport simulators such as MRST, DuMux, DARTS, PorePy, and OpenCSMP.

It ships in two forms - a desktop app for interactive use, and a Python library for scripting and large-scale ensemble generation.

Get Started

Option A - Python API

For scripting, batch generation, or integration into simulation pipelines, install GeoDFN directly via pip:

pip install GeoDFN

Below is an example:

import numpy as np
from GeoDFN.Classes.DFNGenerator import DFNGenerator

gen = DFNGenerator(
    domainLengthX=300, domainLengthY=600,
    sets=[{
        'I': 0.01,
        'fractureLengthPDF': 'Log-Normal',
        'fractureLengthPDFParams': {'mu': 2.4, 'sigma': 0.73, 'Lmin': 2.59, 'Lmax': 57.48},
        'spatialDistributionPDF': 'Power-law',
        'spatialDistributionPDFParams': {'alpha': 0.51, 'min distance': 1, 'max distance': 600},
        'orientationDistributionPDF': 'Von-Mises',
        'orientationDistributionPDFParams': {'kappa': 8.55, 'loc': 1.4,
                                              'thetaMin': np.radians(30), 'thetaMax': np.radians(120)},
        'bufferZone': {'method': 'constant', 'constant': 1.4},
    }],
    apertureCalculationParameters={'method': 'subLinear', 'scalingCoefficient': 0.001, 'scalingExponent': 0.5},
    DFNName='my_dfn',
    numOfRealizations=10,
)

print(f"{len(gen.realizations)} realizations generated")

Option B - Desktop App (no Python required)

The easiest way to get started without any installation:

1. Download the application
2. Unzip the file
3. Double-click GeoDFN.exe - your browser opens with the interface

Option C - Run the GUI locally

If you have Python installed, you can run the interface directly:

pip install GeoDFN
streamlit run app.py

Your browser will open automatically with the full interface.

GUI or Python API?

For uncertainty quantification studies and generating training data for machine-learning emulators, we recommend the Python API. It allows full automation over the parameter space that defines the fracture network ensemble.

Capabilities

Output

Each run writes results to DFNs/<name>/:

DFNs/<name>/
├── fractureCoordinates/       # Start/end (x, y) of each fracture
├── aperture/                  # Aperture values
├── fractureSet/               # Full fracture list per set
├── orientationStereographic/  # Stereonet plots
├── outputPropertiesTotal/     # Network statistics
└── pics/                      # DFN visualizations

For Developers

pip install -e ".[dev]"

pytest tests/

pip install pyinstaller
python -m PyInstaller geodfn.spec --noconfirm

GeoDFN/
├── Classes/
│   ├── DFNGenerator.py                      # Random-seed generator
│   ├── DFNGeneratorWithSeed.py              # Fixed seed-point generator
│   ├── DFNGeneratorWithSeedAndExclusion.py  # Generator with exclusion zones
│   ├── _validation.py                       # Input validation
│   ├── fractureLengthPDFs.py
│   ├── orientationPDFs.py
│   ├── spatialDistributionPDFs.py
│   ├── apertureCalculator.py
│   └── bufferZoneCalculator.py
├── Example-BrazilRandomSeeds.py
├── Example-BrazilFixedSeeds.py
├── Example-BrazilFixedSeedsAndExclusion.py
├── Example-BrazilAperture.py
├── Examples.ipynb
└── PercolationAnalysis.ipynb
app.py          # Streamlit GUI
launcher.py     # Desktop app entry point
geodfn.spec     # PyInstaller build spec

Citation

If you use GeoDFN in your research, please cite:

Kamel Targhi, E., et al. "From outcrop observations to dynamic simulations: an efficient workflow for generating ensembles of geologically plausible fracture networks and assessing their impact on flow and transport." Geoenergy 3.1 (2025): geoenergy2025-028.

License

MIT License - © 2025 Elahe Kamel Targhi. See LICENSE for details.