Reproducible material for A Wasserstein GAN with gradient penalty for 3D porous media generation.
Corrales M., Izzatullah M., Hoteit H., and Ravasi M.
Submitted to Second EAGE Subsurface Intelligence Workshop, 28-31 October 2022, Manama, Bahrain

Project structure

This repository is organized as follows:

• 📂 checkpoints: folder containing the trained generator every two epochs for RockGAN and CRockGAN.
• 📂 data: folder containing data and instructions on how to retrieve the data.
• 📂 figures: folder containing the 3D figures of the results obtained.
• 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (REV, Training for RockGAN and CRockGAN, and metrics by epochs).
• 📂 rockgan: package of the project.

Notebooks

The following notebooks are provided:

• 📙 01_Representative_Elementary_Volume_REV.ipynb: notebook performing Representative Elementary Volume for porosity and permeability to determine sub-volume size for data augmentation.
• 📙 02_Training_RockGAN.ipynb: notebook performing Training of RockGAN.
• 📙 03_Training_CRockGAN.ipynb: notebook performing Training of CRockGAN.
• 📙 04_Results_Minkowski_by_epochs.ipynb: notebook performing comparison results of the Minkowski functionales by epochs (RockGAN VS CRockGAN).
• 📙 05_Results_TwoPointStat_by_epochs.ipynb: notebook performing comparison results of the Two-point statistics by epochs (RockGAN VS CRockGAN).
• 📙 06_Results_Permeability_by_epochs.ipynb: notebook performing comparison results of permeability by epochs (RockGAN VS CRockGAN).
• 📙 07_Results_3D_Visualization.ipynb: notebook performing 3D visualization of the samples after training.

Getting started 👾 🤖

To ensure reproducibility of the results, we suggest using the environment.yml file when creating an environment.

Simply run:

./install_env.sh

It will take some time, if at the end you see the word Done! on your terminal you are ready to go. After that you can simply install your package:

pip install .

or in developer mode:

pip install -e .

Remember to always activate the environment by typing:

conda activate rockgan

Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA GEForce RTX 3090 GPU. Different environment configurations may be required for different combinations of workstation and GPU.

References

• Mosser, L., Dubrule, O., & Blunt, M. J. (2017). Reconstruction of three-dimensional porous media using generative adversarial neural networks [https://github.com/LukasMosser/PorousMediaGan]
• Gostick J, Khan ZA, Tranter TG, Kok MDR, Agnaou M, Sadeghi MA, Jervis R. PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images. Journal of Open Source Software, 2019. doi:10.21105/joss.01296 [https://github.com/PMEAL/porespy]
• Gostick et al. "OpenPNM: a pore network modeling package." Computing in Science & Engineering 18, no. 4 (2016): 60-74. doi:10.1109/MCSE.2016.49 [https://github.com/PMEAL/OpenPNM]
• Arnout M.P. Boelens, and Hamdi A. Tchelepi, QuantImPy: Minkowski functionals and functions with Python, SoftwareX, Volume 16, 2021, 100823, ISSN 2352-7110, doi: 10.1016/j.softx.2021.100823 [https://github.com/boeleman/quantimpy]

Did you find this repository useful? Please cite us

@article{eage:/content/papers/10.3997/2214-4609.2022616005,
   author = "Corrales, M. and Izzatullah, M. and Hoteit, H. and Ravasi, M.",
   title = "A Wasserstein GAN with Gradient Penalty for 3D Porous Media Generation.", 
   journal= "",
   year = "2022",
   volume = "2022",
   number = "1",
   pages = "1-5",
   doi = "https://doi.org/10.3997/2214-4609.2022616005",
   url = "https://www.earthdoc.org/content/papers/10.3997/2214-4609.2022616005",
   publisher = "European Association of Geoscientists & Engineers",
   issn = "2214-4609",
   type = "",
  }