A new upscaling method for microscopic fluid flow based on digital rocks

Qinzhuo Liao, Liang Xue, Bin Wang, Gang Lei

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This report presents our new findings in microscopic fluid flow based on digital rocks. Permeability of digital rocks can be estimated by pore-scale simulations using the Stokes equation, but the computational cost can be extremely high due to the complicated pore geometry and the large number of voxels. In this study, a novel method is proposed to simplify the three-dimensional pore-scale simulation to multiple decoupled two- dimensional ones, and each two-dimensional simulation provides the velocity distribution over a slice. By this decoupled simulation approach, the expensive simulation based on the Stokes equation is conducted only on two-dimensional domains, and the final three- dimensional simulation of Darcy equation using the finite difference method is very cheap. The proposed method is validated by both sandstone and carbonate rock samples and shows significant enhancement in the computational speed. This work sheds light on large-scale microscopic fluid flow based on digital rocks.

Cited as: Liao, Q., Xue, L., Wang, B., Lei, G. A new upscaling method for microscopic fluid flow based on digital rocks. Advances in Geo-Energy Research, 2022, 6(4): 357-358. https://doi.org/10.46690/ager.2022.04.10


Upscaling, microscopic flow, digital rock, permeability

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Cai, J., Wei, W., Hu, X., et al. Fractal characterization of dynamic fracture network extension in porous media. Fractals, 2017, 25(2): 1750023.

He, X., Luo, L. S. Lattice boltzmann model for the incompressible Navier-Stokes equation. Journal of Statistical Physics, 1997, 88(3): 927-944.

Lei, G., Liao, Q., Patil, S. A new mechanistic model for conductivity of hydraulic fractures with proppants embedment and compaction. Journal of Hydrology, 2021, 601: 126606.

Liao, Q., Lei, G., Zhang, D., et al. Analytical solution for upscaling hydraulic conductivity in anisotropic heterogeneous formations. Advances in Water Resources, 2019, 128: 97-116.

Liu, J., Ma, S., Shen, W., et al. Image feature recognition and gas permeability prediction of Gaomiaozi bentonite based on digital images and machine learning. Advances in Geo-Energy Research, 2022, 6(4): 314-323.

Mostaghimi, P., Blunt, M. J., Bijeljic, B. Computations of absolute permeability on micro-CT Images. Mathematical Geosciences, 2013, 45: 103-125.

Wang, B., Wald, I., Morrical, N., et al. An GPU-accelerated particle tracking method for Eulerian-Lagrangian simulations using hardware ray tracing cores. Computer Physics Communications, 2022, 271: 108221.

Xue, L., Gu, S., Mi, L., et al. An automated data-driven pressure transient analysis of water-drive gas reservoir through the coupled machine learning and ensemble Kalman filter method. Journal of Petroleum Science and Engineering, 2022, 208: 109492.

Zhang, T., Sun, S. An exploratory multi-scale framework to reservoir digital twin. Advances in Geo-Energy Research, 2021, 5(3): 239-251.

DOI: https://doi.org/10.46690/ager.2022.04.10


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