Pore-network modeling of flow in shale nanopores: Network structure, flow principles, and computational algorithms
KAUST DepartmentComputational Transport Phenomena Lab
Computational Transport Phenomena Laboratory (CTPL), Division of Physical Sciences and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Embargo End Date2024-10-05
Permanent link to this recordhttp://hdl.handle.net/10754/685058
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AbstractHydrocarbons in subsurface nanoporous media, such as shale, are promising energy resources to compensate for the shortage of conventional reservoirs. Pore-network modeling serves as a valuable tool for simulating microscale fluid transport and elucidating flow physics in porous media. However, traditional pore-network models have failed to capture features of spatial structure and fluid flow in unconventional shale rock. This work presents a critical review of pore-network modeling of single-phase and two-phase flow in shale rock. Pore-network modeling advances of shale are reviewed based on three major parts: network morphology and geometries, flow principles in nanocapillaries, and pore-network computational algorithms. First, based on key geological features of shale rock, we analyze network topology, multiscale network, pore geometries, and network representativeness of shale pore-network models. Then, we discuss four important aspects that may influence flow principles of fluids in nanocapillaries: gas and liquid slippage, sorption and diffusion behavior, hydrocarbon thermodynamics, and the presence of water. Finally, we present pore-network modeling methods used for flow simulations in shale rock, including quasi-static and dynamic algorithms. We hope that this review could shed light on fundamentals of pore-network modeling of shale rock.
CitationCui, R., Hassanizadeh, S. M., & Sun, S. (2022). Pore-network modeling of flow in shale nanopores: Network structure, flow principles, and computational algorithms. Earth-Science Reviews, 234, 104203. https://doi.org/10.1016/j.earscirev.2022.104203
SponsorsThe authors thank the 2021/22 visiting faculty program from King Abdullah University of Science and Technology (KAUST) which sponsored the collaboration among the authors. R.C. and S.S. would like to thank for the Research Funding from King Abdullah University of Science and Technology (KAUST), Saudi Arabia through the grants BAS/1/1351-01-01 and URF/1/4074-01-01. S.M.H. wishes to thank the German Research Foundation (DFG) for supporting this work through EXC2075-390740016 under Germany's Excellence Strategy and acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).