1887
Volume 30, Issue 3
  • ISSN: 1354-0793
  • E-ISSN:

Abstract

This paper covers a novel micro-level application of image processing in understanding the topological and petrophysical properties of Indian Gondwana shale using X-ray computed microtomography images. The complexity and randomness in the pore system are explained through the concept of fractal dimension (FD). In this paper, a quantitative analysis of 2D and 3D fractal dimensions of pores, grains and interfaces was performed for Indian Gondwana shale, using the box-counting method. A pore network is formed by the connection of many subpore clusters, each with a different volume. Hence, an image segmentation algorithm was applied to label different subclusters, and subsequently an analysis of FD was carried out on such subclusters of pores and grains. We implemented a novel application of Betti numbers (B0, B1 and B2) and Euler characteristics on our sample and calculated the possible flow channels of the sample. The FD of grains was found to be greater than the FD of the pore–grain interfaces, while the FD of pores was found to have the smallest value. Consequently, we also observed how the FD of both pores and grains was majorly controlled by the largest subcluster, and during fluid intrusion we observed a significant decrease in the FD of pores. Finally, the pore network with a larger B0 and larger difference of B1 was proved to be best for the storage of hydrocarbons and for fluid movement along more flow channels.

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/content/journals/10.1144/petgeo2023-105
2024-07-15
2024-09-12
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References

  1. Andrä, H., Combaret, N. et al.2013. Digital rock physics benchmarks – Part I: Imaging and segmentation. Computers & Geosciences, 50, 25–32, https://doi.org/10.1016/j.cageo.2012.09.005
    [Google Scholar]
  2. Bouda, M., Caplan, J.S. and Saiers, J.E.2016. Box-counting dimension revisited: presenting an efficient method of minimizing quantization error and an assessment of the self-similarity of structural root systems. Frontiers in Plant Science, 7, https://doi.org/10.3389/fpls.2016.00149
    [Google Scholar]
  3. Bourke, P.2014. Box Counting Fractal Dimension of Volumetric Data, http://paulbourke.net/fractals/cubecount
  4. Dong, H. and Blunt, M.J.2009. Pore-network extraction from micro-computerized-tomography images. Physical Review E, 80, 036307, https://doi.org/10.1103/PhysRevE.80.036307
    [Google Scholar]
  5. Falconer, K.2004. Fractal Geometry: Mathematical Foundations and Applications. John Wiley & Sons, Chichester, UK.
    [Google Scholar]
  6. Fu, J., Wang, M., Chen, B., Wang, J., Xiao, D., Luo, M. and Evans, B.2023. A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation. Engineering with Computers, 39, 3895–3926, https://doi.org/10.1007/s00366-023-01841-8
    [Google Scholar]
  7. Ghanizadeh, A., Clarkson, C.R., Aquino, S., Ardakani, O.H. and Sanei, H.2015. Petrophysical and geomechanical characteristics of Canadian tight oil and liquid-rich gas reservoirs: II. Geomechanical property estimation. Fuel, 153, 682–691, https://doi.org/10.1016/j.fuel.2015.02.113
    [Google Scholar]
  8. Ghosh, R., Sarkar, P. and Singh, K.H.2021. Elastic anisotropy modeling of organic-rich lower Gondwana shale in eastern India. Pure and Applied Geophysics, 178, 123–139, https://doi.org/10.1007/s00024-020-02620-y
    [Google Scholar]
  9. Giesche, H.2006. Mercury porosimetry: a general (practical) overview. Particle & Particle Systems Characterization, 23, 9–19, https://doi.org/10.1002/ppsc.200601009
    [Google Scholar]
  10. Grady, L.J. and Polimeni, J.R.2010. Measuring networks. In:Discrete Calculus: Applied Analysis on Graphs for Computational Science. Springer, London, 267–289.
    [Google Scholar]
  11. Hazra, B., Varma, A.K., Bandopadhyay, A.K., Chakravarty, S., Buragohain, J., Samad, S.K. and Prasad, A.K.2016. FTIR, XRF, XRD and SEM characteristics of Permian shales, India. Journal of Natural Gas Science and Engineering, 32, 239–255, https://doi.org/10.1016/j.jngse.2016.03.098
    [Google Scholar]
  12. Hensel, F., Moor, M. and Rieck, B.2021. A survey of topological machine learning methods. Frontiers in Artificial Intelligence, 4, 681108, https://doi.org/10.3389/frai.2021.681108
    [Google Scholar]
  13. Ismail, M.S., Noorani, M.S.M., Ismail, M., Razak, F.A. and Alias, M.A.2022. Early warning signals of financial crises using persistent homology. Physica A: Statistical Mechanics and its Applications, 586, 126459, https://doi.org/10.1016/j.physa.2021.126459
    [Google Scholar]
  14. Krohn, C.E. and Thompson, A.H.1986. Fractal sandstone pores: Automated measurements using scanning-electron-microscope images. Physical Review B, 33, 6366, https://doi.org/10.1103/PhysRevB.33.6366
    [Google Scholar]
  15. Lage, J.1998. The fundamental theory of flow through permeable media from Darcy to turbulence. In: Ingham, D.B. and Pop, I. (eds) Transport Phenomena in Porous Media. Elsevier, Amsterdam, 1–30, https://doi.org/10.1016/B978-008042843-7/50001-5
    [Google Scholar]
  16. Lee, K.S. and Kim, T.H.2016. Integrative Understanding of Shale Gas Reservoirs. Springer Briefs in Applied Sciences and Technology. Springer, Cham, Switzerland.
    [Google Scholar]
  17. Li, B. and Chen, Y.2016. Influence of dry density on soil-water retention curve of unsaturated soils and its mechanism based on mercury intrusion porosimetry. Transactions of Tianjin University, 22, 268–272, https://doi.org/10.1007/s12209-016-2744-5
    [Google Scholar]
  18. Li, X., Luo, M. and Liu, J.2019. Fractal characteristics based on different statistical objects of process-based digital rock models. Journal of Petroleum Science and Engineering, 179, 19–30, https://doi.org/10.1016/j.petrol.2019.03.068
    [Google Scholar]
  19. Li, Y., Gao, X., Meng, S., Wu, P., Niu, X., Qiao, P. and Elsworth, D.2019. Diagenetic sequences of continuously deposited tight sandstones in various environments: A case study from upper Paleozoic sandstones in the Linxing area, eastern Ordos basin, China. AAPG Bulletin, 103, 2757–2783, https://doi.org/10.1306/03061918062
    [Google Scholar]
  20. Liu, Z., Herring, A., Robins, V. and Armstrong, R.T.2017. Prediction of permeability from Euler characteristic of 3D images. Paper presented at theInternational Symposium of the Society of Core Analysts, 27 August–1 September 2017, Vienna, Austria.
    [Google Scholar]
  21. Mandelbrot, B.B.1983. The Fractal Geometry of Nature. Freeman, New York.
    [Google Scholar]
  22. Mehmani, A., Kelly, S. and Torres-Verdín, C.2019. Leveraging digital rock physics workflows in unconventional petrophysics: A review of opportunities, challenges, and benchmarking. Paper SPWLA-2019-CCCC presented at theSPWLA 60th Annual Logging Symposium, June 15–19, 2019, The Woodlands, Texas, USA, https://doi.org/10.30632/T60ALS-2019_CCCC
    [Google Scholar]
  23. Mishra, S., Mendhe, V.A., Kamble, A.D., Bannerjee, M., Varma, A.K., Singh, B.D. and Pandey, J.2016. Prospects of shale gas exploitation in Lower Gondwana of Raniganj Coalfield (West Bengal), India. Journal of Palaeosciences, 65, 31–46, https://doi.org/10.54991/jop.2016.297
    [Google Scholar]
  24. Moon, C., Mitchell, S.A., Heath, J.E. and Andrew, M.2019. Statistical inference over persistent homology predicts fluid flow in porous media. Water Resources Research, 55, 9592–9603, https://doi.org/10.1029/2019WR025171
    [Google Scholar]
  25. Ostu, N.1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–66, https://doi.org/10.1109/TSMC.1979.4310076
    [Google Scholar]
  26. Otter, N., Porter, M.A., Tillmann, U., Grindrod, P. and Harrington, H.A.2017. A roadmap for the computation of persistent homology. EPJ Data Science, 6, 17, https://doi.org/10.1140/epjds/s13688-017-0109-5
    [Google Scholar]
  27. Rabbani, A., Ayatollahi, S., Kharrat, R. and Dashti, N.2016. Estimation of 3-D pore network coordination number of rocks from watershed segmentation of a single 2-D image. Advances in Water Resources, 94, 264–277, https://doi.org/10.1016/j.advwatres.2016.05.020
    [Google Scholar]
  28. Ruiz de Miras, J.R., Navas, J., Villoslada, P. and Esteban, F.J.2011. UJA-3DFD: A program to compute the 3D fractal dimension from MRI data. Computer Methods and Programs in Biomedicine, 104, 452–460, https://doi.org/10.1016/j.cmpb.2010.08.015
    [Google Scholar]
  29. Rybakken, E., Baas, N. and Dunn, B.2019. Decoding of neural data using cohomological feature extraction. Neural Computation, 31, 68–93, https://doi.org/10.1162/neco_a_01150
    [Google Scholar]
  30. Sadeghnejad, S., Enzmann, F. and Kersten, M.2021. Digital rock physics, chemistry, and biology: challenges and prospects of pore-scale modelling approach. Applied Geochemistry, 131, 105028, https://doi.org/10.1016/j.apgeochem.2021.105028
    [Google Scholar]
  31. Sarkar, P. and Singh, K.H.2016. Petrophysical characterization of Gondwana shales of South Karanpura Coal Field, Jharkhand, India. ASEG Extended Abstracts, 2016, 1–8, https://doi.org/10.1071/ASEG2016ab248
    [Google Scholar]
  32. Sarkar, P., Kumar, A., Singh, K.H., Ghosh, R. and Singh, T.N.2018a. Pore system, microstructure and porosity characterization of Gondwana shale of Eastern India using laboratory experiment and watershed image segmentation algorithm. Marine and Petroleum Geology, 94, 246–260, https://doi.org/10.1016/j.marpetgeo.2018.04.006
    [Google Scholar]
  33. Sarkar, P., Singh, K.H., Ghosh, R. and Singh, T.N.2018b. Estimation of elastic parameters, mineralogy and pore characteristics of Gondwana shale in Eastern India for evaluation of shale gas potential. Current Science, 115, 710–720, https://doi.org/10.18520/cs/v115/i4/710-720
    [Google Scholar]
  34. Sarkar, P., Ghosh, R., Singh, K.H. and Singh, T.N.2021. Pore characteristics of Gondwana shale of eastern India. Journal of the Geological Society of India, 97, 363–374, https://doi.org/10.1007/s12594-021-1694-2
    [Google Scholar]
  35. Sayers, C.M.2005. Seismic anisotropy of shales. Geophysical Prospecting, 53, 667–676, https://doi.org/10.1111/j.1365-2478.2005.00495.x
    [Google Scholar]
  36. Sheppard, A.P., Sok, R.M. and Averdunk, H.2005. Improved pore network extraction methods. Paper SCA2005-20 presented at theInternational Symposium of the Society of Core Analysts, 21–25 August 2005, Toronto, Canada.
    [Google Scholar]
  37. Song, W., Wang, D., Yao, J., Li, Y., Sun, H., Yang, Y. and Zhang, L.2019. Multiscale image-based fractal characteristic of shale pore structure with implication to accurate prediction of gas permeability. Fuel, 241, 522–532, https://doi.org/10.1016/j.fuel.2018.12.062
    [Google Scholar]
  38. Stoyans, D. and Stoyans, H.1994. Fractals, Random Shapes and Point Fields: Methods of Geometrical Statistics. John Wiley & Sons, Chichester, UK.
    [Google Scholar]
  39. Suzuki, A., Miyazawa, M., Minto, J.M., Tsuji, T., Obayashi, I., Hiraoka, Y. and Ito, T.2021. Flow estimation solely from image data through persistent homology analysis. Scientific Reports, 11, 17948, https://doi.org/10.1038/s41598-021-97222-6
    [Google Scholar]
  40. Teramoto, T., Kamiya, T., Sakurai, T. and Kanaya, F.2018. Betti number ratios as quantitative indices for bone morphometry in three dimensions. Computer Methods and Programs in Biomedicine, 162, 93–98, https://doi.org/10.1016/j.cmpb.2018.05.012
    [Google Scholar]
  41. Tewari, A., Dutta, S. and Sarkar, T.2016. Organic geochemical characterization and shale gas potential of the Permian barren Measures Formation, west Bokaro sub-basin, eastern India. Journal of Petroleum Geology, 39, 49–60, https://doi.org/10.1111/jpg.12627
    [Google Scholar]
  42. Townsend, J., Micucci, C.P., Hymel, J.H., Maroulas, V. and Vogiatzis, K.D.2020. Representation of molecular structures with persistent homology for machine learning applications in chemistry. Nature Communications, 11, 3230, https://doi.org/10.1038/s41467-020-17035-5
    [Google Scholar]
  43. US EIA2013. Technically Recoverable Shale Oil and Shale Gas Resources. An Assessment of 137 Shale Formations in 41 Countries Outside the United States. United States Energy Information Administration (US EIA), Washington, DC.
    [Google Scholar]
  44. Varma, A.K., Hazra, B., Samad, S.K., Panda, S. and Mendhe, V.A.2014. Methane sorption dynamics and hydrocarbon generation of shale samples from West Bokaro and Raniganj basins, India. Journal of Natural Gas Science and Engineering, 21, 1138–1147, https://doi.org/10.1016/j.jngse.2014.11.011
    [Google Scholar]
  45. Verri, I., Della Torre, A. et al.2017. Development of a digital rock physics workflow for the analysis of sandstones and tight rocks. Journal of Petroleum Science and Engineering, 156, 790–800, https://doi.org/10.1016/j.petrol.2017.06.053
    [Google Scholar]
  46. Wang, C., Wu, K., Scott, G.G. and Jia, A.2022. Merge pore clusters: A novel method to construct pore networks and predict permeability from 2D rock images. Advances in Water Resources, 166, 104238, https://doi.org/10.1016/j.advwatres.2022.104238
    [Google Scholar]
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