Paper contribution

Permeability Estimation from 3D Digital Core Models Derived from X-Ray CT Imaging

Ha Jung Je; Kwang Yeom Kim

Proceedings
EUROCK 2026
Pages
573-579
Submitted
28 January 2026
Accepted
15 June 2026
Published
12 August 2026

Summary

Abstract

Understanding fluid flow characteristics within porous media is crucial for various subsurface resource development applications, including geological storage of carbon dioxide, oil and gas recovery, and groundwater management. Traditional physical experiments provide only bulk properties like porosity and permeability, offering limited insight into internal pore structures and flow behaviors. Moreover, repetitive techniques such as Mercury Intrusion Porosimetry (MIP) and gas injection can damage samples and raise costs. To address these limitations, 3D digital core modeling has been increasingly used to simulate complex pore structures and predict fluid flow. Among available techniques, Pore Network Modeling (PNM) and the Lattice Boltzmann Method (LBM) are widely applied as alternatives to laboratory experiments. However, key parameters in PNM’s SNOW algorithm still rely on empirical tuning, while LBM requires adequate resolution and sample size. Yet, quantitative evaluations of these conditions remain limited. This study proposes a method to evaluate permeability accurately and consistently using 3D CT images alone, without physical testing. A digital core was generated from CT image stacks of Bentheimer sandstone, obtained via the Digital Porous Media Portal. Simulations were performed using both PNM and LBM. Based on pore characteristics, we developed an integrated optimization algorithm that automatically calibrates SNOW sensitivity parameters in PNM and resolution settings in LBM, ensuring reproducibility across varying sample conditions. By combining the strengths of both approaches and addressing their limitations, this study establishes a standardized framework for deriving reliable permeability values from CT images alone, advancing digital rock physics and enabling non-destructive evaluation in future subsurface applications.

Contributors

Authors

  1. Ha Jung Je NATIONAL Korea Maritime & Ocean University, Korea, Republic of (South Korea) ORCID 0000-0002-4648-7470
  2. Kwang Yeom Kim NATIONAL Korea Maritime & Ocean University, Korea, Republic of (South Korea)

Index terms

Keywords

Full text

Attachments

Works cited

References

  1. Bultreys, T., De Boever, W. and Cnudde, V. (2016) “Imaging and image-based fluid transport modeling at the pore scale in geological materials: A practical introduction to the current state-of-the-art,” Earth-Science Reviews. Elsevier B.V., pp. 93–128. Available at: https://doi.org/10.1016/j.earscirev.2016.02.001.
  2. Cnudde, V. and Boone, M.N. (2013) “High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications,” Earth-Science Reviews, pp. 1–17. Available at: https://doi.org/10.1016/j.earscirev.2013.04.003.
  3. Gao, S., Meegoda, J.N. and Hu, L. (2012) “Two methods for pore network of porous media,” International Journal for Numerical and Analytical Methods in Geomechanics, 36(18), pp. 1954–1970. Available at: https://doi.org/10.1002/nag.1134.
  4. Jiang, Z. et al. (2012) “Stochastic Pore Network Generation from 3D Rock Images,” Transport in Porous Media, 94(2), pp. 571–593. Available at: https://doi.org/10.1007/s11242-011-9792-z.
  5. Jivkov, A.P. et al. (2013) “A novel architecture for pore network modelling with applications to permeability of porous media,” Journal of Hydrology, 486, pp. 246–258. Available at: https://doi.org/10.1016/j.jhydrol.2013.01.045.
  6. Ketcham, R.A. and Carlson, W.D. (2001) “Acquisition, optimization and interpretation of x-ray computed tomographic imagery: Applications to the geosciences,” Computers and Geosciences, 27(4), pp. 381–400. Available at: https://doi.org/10.1016/S0098-3004(00)00116-3.
  7. Otsu, N. (1979) “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, SMC-9(1), pp. 62–66.
  8. Peng, S. et al. (2012) “Using X-ray computed tomography in pore structure characterization for a Berea sandstone: Resolution effect,” Journal of Hydrology, 472–473, pp. 254–261. Available at: https://doi.org/10.1016/j.jhydrol.2012.09.034.
  9. Rao, P. and Schaefer, L. (2019) “Lattice Boltzmann models for micro-tomographic pore-spaces,” Computers and Fluids, 193. Available at: https://doi.org/10.1016/j.compfluid.2019.104294.
  10. Saxena, N. et al. (2017) “Effect of image segmentation & voxel size on micro-CT computed effective transport & elastic properties,” Marine and Petroleum Geology, 86, pp. 972–990. Available at: https://doi.org/10.1016/j.marpetgeo.2017.07.004.
  11. Soulaine, C. et al. (2016) “The Impact of Sub-Resolution Porosity of X-ray Microtomography Images on the Permeability,” Transport in Porous Media, 113(1), pp. 227–243. Available at: https://doi.org/10.1007/s11242-016-0690-2.
  12. Xie, Q. et al. (2026) “Pore-Scale Transition Behavior of Digital Carbonate Rock Dissolution During CO2 Geo-Sequestration,” Water Resources Research, 62(2). Available at: https://doi.org/10.1029/2025WR041604.
  13. Xiong, Q., Baychev, T.G. and Jivkov, A.P. (2016) “Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport,” Journal of Contaminant Hydrology. Elsevier B.V., pp. 101–117. Available at: https://doi.org/10.1016/j.jconhyd.2016.07.002.
  14. Yang, E., Kang, D.H. and Yun, T.S. (2021) “Ternary segmentation and estimation of permeability for porous rocks based on 3D X-ray computed tomographic images by hidden Markov random field and Brinkman-force lattice Boltzmann model,” Journal of Hydrology, 599. Available at: https://doi.org/10.1016/j.jhydrol.2021.126377.
  15. Yi, Z. et al. (2017) “Pore network extraction from pore space images of various porous media systems,” Water Resources Research, 53(4), pp. 3424–3445. Available at: https://doi.org/10.1002/2016WR019272.