Articles | Volume 15, issue 3
Research article
13 Mar 2024
Research article |  | 13 Mar 2024

Investigating rough single-fracture permeabilities with persistent homology

Marco Fuchs, Anna Suzuki, Togo Hasumi, and Philipp Blum

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Cited articles

Alqahtani, N. J., Chung, T., Da Wang, Y., Armstrong, R. T., Swietojanski, P., and Mostaghimi, P.: Flow-Based Characterization of Digital Rock Images Using Deep Learning, SPE J., 26, 1800–1811,, 2021. 
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Andrew, M., Bijeljic, B., and Blunt, M. J.: Pore-scale imaging of trapped supercritical carbon dioxide in sandstones and carbonates, International J. Greenh. Gas Contr., 22, 1–14,, 2014. 
Araya-Polo, M., Alpak, F. O., Hunter, S., Hofmann, R., and Saxena, N.: Deep learning–driven permeability estimation from 2D images, Comput. Geosci., 24, 571–580,, 2020. 
Barton, N. and de Quadros, E. F.: Joint aperture and roughness in the prediction of flow and groutability of rock masses, Int. J. Rock Mech. Min., 34, 252.e1–252.e14,, 1997. 
Short summary
In this study, the permeability of a natural fracture in sandstone is estimated based only on its geometry. For this purpose, the topological method of persistent homology is applied to three geometric data sets with different resolutions for the first time. The results of all data sets compare well with conventional experimental and numerical methods. Since the analysis takes less time to the same amount of time, it seems to be a good alternative to conventional methods.