References
- Allen, M.B., 2021. The Mathematics of Fluid Flow in Porous Media, John
Wiley & Sons, Hoboken, NJ.
- Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M.,
Keehm, Y., Krzikalla, F., Lee, M., Madonna, C. and Marsh, M., 2013a.
Digital rock physics benchmarks—Part I: Imaging and segmentation.
Computers & Geosciences, 50, pp.25-32.
- Andrä, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M.,
Keehm, Y., Krzikalla, F., Lee, M., Madonna, C. and Marsh, M., 2013b.
Digital rock physics benchmarks—Part II: Computing effective
properties. Computers & Geosciences, 50, pp.33-43.
- Blunt, M.J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S.,
Mostaghimi, P., Paluszny, A. and Pentland, C., 2013. Pore-scale
imaging and modelling. Advances in Water resources, 51, pp.197-216.
- Da Wang, Y., Armstrong, R.T. and Mostaghimi, P., 2019. Enhancing
resolution of digital rock images with super resolution convolutional
neural networks. Journal of Petroleum Science and Engineering, 182,
p.106261.
- Da Wang, Y., Armstrong, R.T. and Mostaghimi, P., 2020. Boosting
resolution and recovering texture of 2D and 3D micro‐CT images with
deep learning. Water Resources Research, 56(1), p.e2019WR026052.
- Karras, T., Laine, S. and Aila, T., 2019. A style-based generator
architecture for generative adversarial networks. In Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition
(pp. 4401-4410).
- Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J. and
Aila, T., 2020a. Analyzing and improving the image quality of
stylegan. In Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition (pp. 8110-8119).
- Karras,
T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J. and Aila, T.,
2020b. Training generative adversarial networks with limited data.
arXiv preprint arXiv:2006.06676.
- Keehm, Y., Mukerji, T. and Nur, A., 2001. Computational rock physics
at the pore scale: Transport properties and diagenesis in realistic
pore geometries. The Leading Edge, 20(2), pp.180-183.
- Keehm, Y., Mukerji, T. and Nur, A., 2004. Permeability prediction from
thin sections: 3D reconstruction and Lattice‐Boltzmann flow
simulation. Geophysical Research Letters, 31(4).
- Linden, S., Wiegmann, A. and Hagen, H., 2015. The LIR space
partitioning system applied to the Stokes equations. Graphical Models,
82, pp.58-66.
- Mohebi, A., Fieguth, P. and Ioannidis, M.A., 2009. Statistical fusion
of two-scale images of porous media. Advances in water resources,
32(11), pp.1567-1579.
- Niu, Y., Wang, Y.D., Mostaghimi, P., Swietojanski, P. and Armstrong,
R.T., 2020. An innovative application of generative adversarial
networks for physically accurate rock images with an unprecedented
field of view. Geophysical Research Letters, 47(23), p.e2020GL089029.
- Jiao, Y., Stillinger, F.H. and Torquato, S., 2007. Modeling
heterogeneous materials via two-point correlation functions: Basic
principles. Physical review E, 76(3), p.031110.
- Okabe, H. and Blunt, M.J., 2007. Pore space reconstruction of vuggy
carbonates using microtomography and multiple‐point statistics. Water
Resources Research, 43(12).
- Saxena, N., Hofmann, R., Alpak, F.O., Berg, S., Dietderich, J.,
Agarwal, U., Tandon, K., Hunter, S., Freeman, J. and Wilson, O.B.,
2017. References and benchmarks for pore-scale flow simulated using
micro-CT images of porous media and digital rocks. Advances in Water
Resources, 109, pp.211-235.
- Tahmasebi, P., Javadpour, F. and Sahimi, M., 2015. Multiscale and
multiresolution modeling of shales and their flow and morphological
properties. Scientific reports, 5(1), pp.1-11.
- Wang, Y., Teng, Q., He, X., Feng, J. and Zhang, T., 2019. CT-image of
rock samples super resolution using 3D convolutional neural network.
Computers & Geosciences, 133, p.104314.
- Wildenschild, D., Vaz, C.M.P., Rivers, M.L., Rikard, D. and
Christensen, B.S.B., 2002. Using X-ray computed tomography in
hydrology: systems, resolutions, and limitations. Journal of
Hydrology, 267(3-4), pp.285-297.
- Wildenschild, D. and Sheppard, A.P., 2013. X-ray imaging and analysis
techniques for quantifying pore-scale structure and processes in
subsurface porous medium systems. Advances in Water resources, 51,
pp.217-246.
- You, N., Li, Y.E. and Cheng, A., 2021. 3D Carbonate Digital Rock
Reconstruction Using Progressive Growing GAN. Journal of Geophysical
Research: Solid Earth, 126(5), p.e2021JB021687.
- Zhu, J.Y., Park, T., Isola, P. and Efros, A.A., 2017. Unpaired
image-to-image translation using cycle-consistent adversarial
networks. In Proceedings of the IEEE international conference on
computer vision (pp. 2223-2232).