1887

Abstract

Summary

Linking the pore-scale and reservoir-scale subsurface fluid flow remains an open challenge in areas such as oil recovery and Carbon Capture and Storage (CCS). One of the main factors hindering our knowledge of such a process is the scarcity of physical samples from geological areas of interest. One way to tackle this issue is by creating accurate, digital representations of the available rock samples to perform numerical fluid flow simulations. Recent advancements in Machine Learning and Deep Generative Modeling open up a new promising avenue for generating realistic digital rock samples at low cost. This is particularly the case for Generative Adversarial Networks (GANs) due to their ability to learn complex high-dimensional distributions and produce high-quality samples. The present study introduces a novel Wasserstein GAN with gradient penalty (WGAN-GP) to generate 3D high-quality porous media samples. Moreover, a comprehensive set of evaluation metrics inspired by the geometry and topology of the structure and the fluid flow properties is established to assess the quality of the generative process.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2022616005
2022-10-28
2024-06-15
Loading full text...

Full text loading...

References

  1. Arjovsky, M., Chintala, S. and Bottou, L. [2017] Wasserstein generative adversarial networks. In: International conference on machine learning. PMLR, 214–223.
    [Google Scholar]
  2. Boelens, A.M. and Tchelepi, H.A. [2021] QuantImPy: Minkowski functionals and functions with Python.Soft-wareX, 16, 100823.
    [Google Scholar]
  3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. [2014] Generative adversarial nets.Advances in neural information processing systems, 27.
    [Google Scholar]
  4. Gostick, J.T., Khan, Z.A., Tranter, T.G., Kok, M.D., Agnaou, M., Sadeghi, M. and Jervis, R. [2019] PoreSpy: A python toolkit for quantitative analysis of porous media images.Journal of Open Source Software, 4(37), 1296.
    [Google Scholar]
  5. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V. and Courville, A.C. [2017] Improved training of wasserstein gans.Advances in neural information processing systems, 30.
    [Google Scholar]
  6. Mosser, L., Dubrule, O. and Blunt, M.J. [2017] Reconstruction of three-dimensional porous media using generative adversarial neural networks.Physical Review E, 96(4), 043309.
    [Google Scholar]
  7. Okabe, H. and Blunt, M.J. [2005] Multiple-point statistics to generate pore space images. In: Geostatistics Banff 2004, Springer, 763–768.
    [Google Scholar]
  8. Wang, Y. and Sun, S. [2016] Direct calculation of permeability by high-accurate finite difference and numerical integration methods.Communications in Computational Physics, 20(2), 405–440.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2022616005
Loading
/content/papers/10.3997/2214-4609.2022616005
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error