From digital rock to digital wellbore: Multiscale reconstruction and simulation

Abstract

Subsurface rocks exhibit multiscale heterogeneity characteristics ranging from the microscopic to macroscopic levels. A significant challenge in geophysical exploration research is how to accurately analyze the cross-scale characterization of rock component structures and physical responses. The advancement of rock imaging equipment and computational resources has led to the emergence of digital rock physics technology as a crucial tool for addressing these challenges. This paper explores common methods and issues in three dimensional modeling and numerical simulations, spanning from micro-nano scale rocks to meter-scale wellbores, and presents relevant research insights. An initial review of the previous research and evolving trends in multiscale rock modeling and physical property simulation is firstly carried out. Subsequently, the primary methods and application range of multiscale simulation are summarized, followed by an outline of the modeling approaches and application directions for digital wellbores. The progression from digital rocks to digital wellbores signifies the successful cross-scale application of digital rock physics technology from the microscopic to macroscopic levels.

Document Type: Perspective

Cited as: Chi, P., Sun, J., Yan, W., Cui, L. From digital rock to digital wellbore: Multiscale reconstruction and simulation. Advances in Geo-Energy Research, 2024, 13(1): 1-6. https://doi.org/10.46690/ager.2024.07.01

References

Ahmad, R., Liu, M., Ortiz, M., et al. Computation of effective elastic moduli of rocks using hierarchical homogenization. Journal of the Mechanics and Physics of Solids, 2023, 174: 105268.

Andra, H., Combaret, N., Dvorkin, J., et al. Digital rock ¨ physics benchmarks-Part II: Computing effective properties. Computers & Geosciences, 2013, 50: 33-43.

Chen, H., He, X., Teng, Q., et al. Super-resolution of realworld rock microcomputed tomography images using cycle-consistent generative adversarial networks. Physical Review E, 2020, 101(2): 023305.

Chi, P., Sun, J., Luo, X., et al. Reconstruction of 3D digital rocks with controllable porosity using CVAE-GAN. Geoenergy Science and Engineering, 2023, 230: 212264.

Chi, P., Sun, J., Yan, W., et al. Multiscale fusion of tight sandstone digital rocks using attention-guided generative adversarial network. Marine and Petroleum Geology, 2024, 160: 106647.

Coelho, D., Thovert, J. F., Adler, P. M. Geometrical and transport properties of random packings of spheres and aspherical particles. Physical Review E, 1997, 55(2): 1959.

Dehghan Khalili, A., Arns, J. Y., Hussain, F., et al. Permeability upscaling for carbonates from the pore scale by use of multiscale X-ray-CT images. SPE Reservoir Evaluation & Engineering, 2013, 16(4): 353-368.

Elmorsy, M., El-Dakhakhni, W., Zhao, B. Rapid permeability upscaling of digital porous media via physicsinformed neural networks. Water Resources Research, 2023, 59(12): e2023WR035064.

Hazlett, R. D. Statistical characterization and stochastic modeling of pore networks in relation to fluid flow. Mathematical Geology. 1997, 29: 801-822.

Jiang, F., Guo, Y., Tsuji, T., et al. Upscaling permeability using multiscale X-Ray-CT images with digital rock modeling and deep learning techniques. Water Resources Research, 2023, 59(3): e2022WR033267.

Keehm, Y., Mukerji, T., Nur, A. Permeability prediction from thin sections: 3D reconstruction and Lattice-Boltzmann flow simulation. Geophysical Research Letters, 2004, 31: L04606.

Lin, W., Li, X., Yang, Z., et al. Multiscale digital porous rock reconstruction using template matching. Water Resources Research, 2019, 55(8): 6911-6922.

Liu, M., Ahmad, R., Cai, W., et al. Hierarchical homogenization with deep-learning-based surrogate model for rapid estimation of effective permeability from digital rocks. Journal of Geophysical Research: Solid Earth, 2023, 128(2): e2022JB025378.

Liu, X., Yan, J., Zhang, X., et al. Numerical upscaling of multi-mineral digital rocks: Electrical conductivities of tight sandstones. Journal of Petroleum Science and Engineering, 2021, 201: 108530.

Lucas-Oliveira, E., Araujo-Ferreira, A. G., Trevizan, W. A., et al. Sandstone surface relaxivity determined by NMR T2 distribution and digital rock simulation for permeability evaluation. Journal of Petroleum Science and Engineering, 2020, 193: 107400.

Luo, X., Sun, J., Zhang, R., et al. A multi-condition denoising diffusion probabilistic model controls the reconstruction of 3D digital rocks. Computers & Geosciences, 2024, 184: 105541.

Miarelli, M., Della Torre, A. Workflow development to scale up petrophysical properties from digital rock physics scale to laboratory scale. Transport in Porous Media, 2021, 140: 459-492.

Najafi, A., Siavashi, J., Ebadi, M., et al. Upscaling permeability anisotropy in digital sandstones using convolutional neural networks. Journal of Natural Gas Science and Engineering, 2021, 96: 104263.

Niu, Y., Wang, Y. D., Mostaghimi, P., et al. An innovative application of generative adversarial networks for physically accurate rock images with an unprecedented field of view. Geophysical Research Letters, 2020, 47(23): e2020GL089029.

Okabe, H., Blunt, M. J. Prediction of permeability for porous media reconstructed using multiple-point statistics. Physical Review E, 2004, 70(6): 066135.

Pak, T., Butler, I. B., Geiger, S., et al. Multiscale pore-network representation of heterogeneous carbonate rocks. Water Resources Research, 2016, 52(7): 5433-5441.

Ruspini, L. C., Øren, P. E., Berg, S., et al. Multiscale digital rock analysis for complex rocks. Transport in Porous Media, 2021, 139(2): 301-325.

Sadeghnejad, S., Enzmann, F., Kersten, M. Digital rock physics, chemistry, and biology: challenges and prospects of pore-scale modelling approach. Applied Geochemistry, 2021, 131: 105028.

Saxena, N., Hows, A., Hofmann, R., et al. Rock properties from micro-CT images: Digital rock transforms for resolution, pore volume, and field of view. Advances in Water Resources, 2019, 134: 103419.

Tahmasebi, P., Javadpour, F., Sahimi, M. Multiscale and multiresolution modeling of shales and their flow and morphological properties. Scientific Reports, 2015, 5(1): 16373.

Wang, Y. D., Blunt, M. J., Armstrong, R. T., et al. Deep learning in pore scale imaging and modeling. Earth-Science Reviews, 2021, 215: 103555.

Wang, S., Tan, M., Wu, H., et al. A digital rock physics-based multiscale multicomponent model construction of hotdry rocks and microscopic analysis of acoustic properties under high-temperature conditions. SPE Journal, 2022, 27(5): 3119-3135.

Wu, K., Van Dijke, M. I., Couples, G. D., et al. 3D stochastic modelling of heterogeneous porous media-applications to reservoir rocks. Transport in Porous Media, 2006, 65: 443-467.

Zakrzewski, M., Schertel, A., Brus, G., et al. A threedimensional reconstruction of coal microstructure using the Cryo-FIB-SEM technique. Fuel, 2019, 275: 117919. Zhang, T. MPS-driven digital rock modeling and upscaling. Mathematical Geosciences, 2015, 47(8): 937-954.