Topological data analysis for pore-network extraction in porous media

Authors

  • Jie Liu School of Mathematical Sciences, Tongji University, Shanghai 200092, P. R. China; Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
  • Tao Zhang Institute of New Energy, China University of Petroleum (East China), Qingdao 266580, P. R. China
  • Zhipeng Duan School of mathematical Sciences, Ministry of Education key Laboratory of NSLSCS, Nanjing Normal University, Nanjing 210023, P. R. China
  • Yuze Zhang School of mathematical Sciences, Ministry of Education key Laboratory of NSLSCS, Nanjing Normal University, Nanjing 210023, P. R. China (Email: 05439@njnu.edu.cn)
  • Shuyu Sun School of Mathematical Sciences, Tongji University, Shanghai 200092, P. R. China (Email: suns@tongji.edu.cn)

Abstract

Pore-network models are widely used to describe pore-scale flow in porous media, and their reliability depends critically on accurate extraction of pore and throat structures. A new extraction framework, termed the topological pore-network finder, is proposed in this work, which combines topological data analysis, medial access path search, and flashlight search medial axis. The topological data analysis is used to identify pore connectivity and cluster the void space, thereby providing robust initial pore centers. The medial access path search method then traces strings between connected pore centers along the medial axis, while the flashlight search medial axis method is used to refine the resulting paths and improve computational efficiency. The method is validated using toy porous media, two- and three-dimensional digital rock samples. Sensitivity analyses show that the pore-network finder is stable with respect to image resolution and string discretization. Compared with the classical maximal-ball method, the pore-network finder achieves at least an order-of-magnitude acceleration while preserving the main geometric statistics and f low-response characteristics of the extracted networks. In addition, because the method operates in continuous space and can reuse information from previous states, it is well suited to quasi-dynamic updates during deformation. The pore-network finder therefore provides an efficient and accurate tool for pore-network extraction and subsequent pore-scale characterization in geo-energy systems.

Document Type: Original article

Cited as: Liu, J., Zhang, T., Duan, Z., Zhang, Y., Sun, S. Topological data analysis for pore-network extraction in porous media. Advances in Geo-Energy Research, 2026, 20(2): 101-113. https://doi.org/10.46690/ager.2026.05.01

DOI:

https://doi.org/10.46690/ager.2026.05.01

Keywords:

Pore-network model, topology data analysis, string method, porous media

References

Biasoti, S., Giorgi, D., Spagnuolo, M. et al. Reeb graphs for shape analysis and applications. Theoretical Computer Science, 2008, 392(1-3): 5-22.

Bui, Q-T. Vo, B., Do, H.-A. N. et al. F-mapper: A fuzzy mapper clustering algorithm. Knowledge- Based Systems, 2020, 189: 105107.

Cai, J., Wei, W., Hu, X., et al. Electrical conductivity models in saturated porous media: A review. Earth-Science Reviews, 2017, 171: 419-433.

Carlsson, G. Topology and data. Bulletin of the American Mathematical Society, 2009, 46(2): 255-308.

Chareyre, B., Cortis, A., Catalano, E., et al. Pore-scale modeling of viscous flow and induced forces in dense sphere packings. Transport in Porous Media, 2012, 94(2): 595-615.

Chen, S., Li, X., Zhu, G., et al. Classification, controlling factors, and multi-scale characterization techniques in shale reservoir pores: A comprehensive review. Gas Science and Engineering, 2025, 140: 205662.

Cui, R., Hassanizadeh, S. M., Sun, S. Pore-network modeling of flow in shale nanopores: Network structure, flow principles, and computational algorithms. Earth-Science Reviews, 2022, 234: 104203.

E, W., Ren, W., Vanden-Eijnden, E. String method for the study of rare events. Physical Review B, 2002, 66(5): 052301.

Edelsbrunner, H., Harer, J. L. Computational topology: An introduction. Providence, USA, American Mathematical Society, 2022.

Feng, X., Liu, J., Shi, J., et al. Phase equilibrium, thermodynamics, hydrogen-induced effects and the interplay mechanisms in underground hydrogen storage. Computational Energy Science, 2024, 1(1): 46-64.

Jiang, Z., Van Dijke, M. I. J., Geiger, S., et al. Pore network extraction for fractured porous media. Advances in Water Resources, 2017, 107: 280-289.

Lee, T.-C., Kashyap, R. L., Chu, C.-N. Building skeleton models via 3D medial surface axis thinning algorithms. CVGIP: Graphical Models and Image Processing, 1994, 56(6): 462-478.

Lindquist, W. B., Lee, S. M., Coker, D. A., et al. Medial axis analysis of void structure in three-dimensional tomographic images of porous media. Journal of Geophysical Research: Solid Earth, 1996, 101(B4): 8297-8310.

Liu, F., Zhang, Z., Liao, B., et al. Recent advances in phase change microcapsules for oilfield applications. Advances in Geo-Energy Research, 2025, 16(3): 211-228.

Liu, J., Tang, Q., Kou, J., et al. A quantitative study on the approximation error and speed-up of the multi-scale MCMC (Monte Carlo Markov chain) method for molecular dynamics. Journal of Computational Physics, 2022, 469: 111491.

Liu, J., Wang, K., Song, H., et al. Porous media flow modeling from molecular simulations to pore-network modeling: Physics-consistent upscaling of interfacial transport parameters. Computational Geosciences, 2026, 30(2): 15.

Liu, J., Zhang, T., Sun, S. Molecular insights into the carbon dioxide sequestration in kerogen: An accelerated algorithm coupling molecular dynamics simulations and Monte Carlo methods. Process Safety and Environmental Protection, 2024a, 185: 1336-1351.

Liu, J., Zhang, T., Sun, S. A new pixel-free algorithm of pore-network extraction for fluid flow in porous media: Flashlight search medial axis. Advances in Geo-Energy Research, 2024b, 13(1): 32-41.

Marco-Sola, S., Sammeth, M., Guigó, R., et al. The GEM mapper: Fast, accurate and versatile alignment by filtration. Nature Methods, 2012, 9(12): 1185-1188.

Morimoto, T., Zhao, B., Taborda, D. M., et al. Critical appraisal of pore network models to simulate fluid flow through assemblies of spherical particles. Computers and Geotechnics, 2022, 150: 104900.

Ni, X., Chen, W., Li, Z., et al. Reconstruction of different scales of pore-fractures network of coal reservoir and its permeability prediction with Monte Carlo method. International Journal of Mining Science and Technology, 2017, 27(4): 693-699.

Niasar, V., Hassanizadeh, S., Pyrak-Nolte, L., et al. Simulating drainage and imbibition experiments in a high-porosity micromodel using an unstructured pore network model. Water Resources Research, 2009, 45(2): W02430.

Qin, X., Wang, H., Xia, Y., et al. Micro-and nanoscale flow mechanisms in porous rocks based on pore-scale modeling. Capillarity, 2024, 13(1): 24-28.

Raoof, A., Hassanizadeh, S. M. A new method for generating pore-network models of porous media. Transport in Porous Media, 2010, 81: 391-407.

Shao, J., You, L., Jia, N., et al. Investigation of induced change in pore structure by the reaction of shale with fracturing fluid. Gas Science and Engineering, 2023, 110: 204860.

Silin, D., Patzek, T. Pore space morphology analysis using maximal inscribed spheres. Physica A: Statistical Mechanics and Its Applications, 2006, 371(2): 336-360.

Singh, G., Mémoli, F., Carlsson, G. E. Topological methods for the analysis of high dimensional data sets and 3D object recognition. Eurographics Symposium on PointBased Graphics, 2007, 2: 91-100.

Thompson, K. E., Fogler, H. S. Modeling flow in disordered packed beds from pore-scale fluid mechanics. AIChE Journal, 1997, 43(6): 1377-1389.

Veen, H., Saul, N., Eargle, D., et al. Kepler Mapper: A flexible Python implementation of the Mapper algorithm. Journal of Open Source Software, 2019, 4(42): 1315.

Yang, L., Liu, Z., Zhao, Z., et al. Experimental study of carbonated water imbibition in deep coal rocks using nuclear magnetic resonance spectroscopy. Capillarity, 2025, 16(2): 27-38.

Zhang, T., Salama, A., Sun, S., et al. Pore network modeling of drainage process in patterned porous media: A quasistatic study. Journal of Computational Science, 2015, 9: 64-69.

Zhang, Y., Liu, J., Zhang, T., et al. Medial Access Path Search (MAPS) for pore-network extraction. Computational Geosciences, 2024, 28: 979-989.

Zhang, Y., Yang, X., Zhang, L., et al. Energy landscape analysis for two-phase multi-component NVT flash systems by using ETD type high-index saddle dynamics. Journal of Computational Physics, 2023, 477: 111916.

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Published

2026-03-19

How to Cite

Liu, J., Zhang, T., Duan, Z., Zhang, Y., & Sun, S. (2026). Topological data analysis for pore-network extraction in porous media. Advances in Geo-Energy Research, 20(2), 101–113. https://doi.org/10.46690/ager.2026.05.01