Identification and parameter characterization of pores and fractures in shales based on multi-scale digital core data

Abstract

Accurate pore structure characterization, as a basic tool for efficient exploration and development in reservoirs and digital rock, has become increasingly popular nowadays. However, using single-scale digital core data, it is difficult to evaluate the multi-scale pore structures in shales. This study proposes an integrated workflow for identifying and extracting pore parameters from multi-scale three-dimensional and two-dimensional digital rock images, which includes full-diameter core computed tomography (CT), micro-CT, focused ion beam-scanning electron microscopy and scanning electron microscopy images. This workflow realizes the identification and parameter extraction of pores and fractures from mesoscopic to microscopic scales. First, meso-fractures are extracted using the connected domain analysis method from full-diameter CT images, and the apparent attitudes are calculated using the least squares and connected domain analysis method. Then, micropores and fractures are identified from Micro-CT and focused ion beamscanning electron microscopy data, and the pore network models are established. Features, including pore radius, surface area, volume, throat radius, length, and coordination number, are calculated based on the maximum ball method. Different types of pores in scanning electron microscopy images are automatically identified using deep learning methods, and the pore parameters are computed using connected domain analysis methods. Subsequently, the workflow is applied to a practical case and the results show accurate extractions of pore structure information. This study provides important guidance and support for the quantitative evaluation of pores and fractures in unconventional reservoirs.

Document Type: Original article

Cited as: Zhou, Y., Zhong, X., Nie, X. Identification and parameter characterization of pores and fractures in shales based on multi-scale digital core data. Advances in Geo-Energy Research, 2024, 13(2): 146-160. https://doi.org/10.46690/ager.2024.08.08

References

Arif, M., Mahmoud, M., Zhang, Y., et al. X-ray tomography imaging of shale microstructures: A review in the context of multiscale correlative imaging. International Journal of Coal Geology, 2021, 233: 103641.

Badrinarayanan, V., Kendall, A., Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.

Borjigin, T., Lu, L., Yu, L., et al. Formation, preservation and connectivity control of organic pores in shale. Petroleum Exploration and Development, 2021, 48(4): 798-812.

Borjigin, T., Shen, B., Yu, L., et al. Mechanisms of shale gas generation and accumulation in the Ordovician Wufeng-Longmaxi Formation, Sichuan Basin, SW China. Petroleum Exploration and Development, 2017, 44(1): 69-78.

Byun, H., Kim, J., Yoon, D., et al. A deep convolutional neural network for rock fracture image segmentation. Earth Science Informatics, 2021, 14: 1937-1951.

Chen, X., Tang, X., He, R., et al. Intelligent identification and quantitative characterization of pores in shale SEM images based on pore-net deep-learning network model. Petrophysics, 2024, 65(2): 233-245.

Chen, Y., Jiang, C., Leung, J. Y., et al. Multiscale characterization of shale pore-fracture system: Geological controls on gas transport and pore size classification in shale reservoirs. Journal of Petroleum Science and Engineering, 2021, 202: 108442.

Ding, W., Wang, Y., Wang, S., et al. Research progress and insight on non-tectonic fractures in shale reservoirs. Earth Science Frontiers, 2024, 31(1): 297. (in Chinese)

Dong, H., Blunt, M. J. Pore-network extraction from microcomputerized-tomography images. Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 2009, 80: 036307.

Gao, S., Zhou, H., Gao, Y., et al. BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability. Medical Image Analysis, 2023, 89: 102889.

Gobert, C., Kudzal, A., Sietins, J., et al. Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning. Additive Manufacturing, 2020, 36: 101460.

Goral, J., Deo, M. Nanofabrication of synthetic nanoporous geomaterials: From nanoscale-resolution 3D imaging to nano-3D-printed digital (shale) rock. Scientific Reports, 2020, 10(1): 21596.

Guo, Q., Wang, Y., Yang, S., et al. A method of blasted rock image segmentation based on improved watershed algorithm. Scientific Reports, 2022, 12(1): 7143.

Iqbal, M. A., Rezaee, R., Smith, G., et al. Shale lithofacies controls on porosity and pore structure: An example from Ordovician Goldwyer Formation, Canning Basin, Western Australia. Journal of Natural Gas Science and Engineering, 2021, 89: 103888.

Jiang, Z., Tahmasebi, P., Mao, Z. Deep residual U-net convolution neural networks with autoregressive strategy for fluid flow predictions in large-scale geosystems. Advances in Water Resources, 2021, 150: 103878.

Josh, M., Delle Piane, C., Esteban, L., et al. Advanced laboratory techniques characterising solids, fluids and pores in shales. Journal of Petroleum Science and Engineering, 2019, 180: 932-949.

Kumar, S., Mendhe, V. A., Kamble, A. D., et al. Geochemical attributes, pore structures and fractal characteristics of Barakar shale deposits of Mand-Raigarh Basin, India. Marine and Petroleum Geology, 2019, 103: 377-396.

Kwiecińska, B., Pusz, S., Valentine, B. J., et al. Application of electron microscopy TEM and SEM for analysis of coals, organic-rich shales and carbonaceous matter. International Journal of Coal Geology, 2019, 211: 103203.

Li, B., Nie, X., Cai, J., et al. U-Net model for multicomponent digital rock modeling of shales based on CT and QEMSCAN images. Journal of Petroleum Science and Engineering, 2022, 216: 110734.

Li, B., Nie, X., Zhu, L., et al. Reconstruction of 3D shale digital core based on generative adeversial neural networks with gradient penalty. Journal of Xi’an Shiyou University (Natural Science Edition), 2023a, 38(2): 53- 60. (in Chinese)

Li, J., Song, Z., Wang, M., et al. Quantitative characterization of microscopic occurrence and mobility of oil in shale matrix pores: A case study of the Shahejie Formation in the Dongying Sag. Petroleum Science Bulletin, 2024, 9 (1): 1-20. (in Chinese)

Li, X., Li, B., Liu, F., et al. Advances in the application of deep learning methods to digital rock technology. Advances in Geo-Energy Research, 2023b, 8(1): 5-18.

Liu, Q., Ren, Y., Wang, W., et al. Intelligent representation method of shale pore structure based on semantic segmentation. Journal of Beijing University of Aeronautics and Astronautics, 2024, in press, http://doi.org/10.13700/j.bh.1001-5965.2024.0018. (in Chinese)

Liu, Q., Sun, M., Sun, X., et al. Pore network characterization of shale reservoirs through state-of-the-art X-ray computed tomography: A review. Gas Science and Engineering, 2023, 113: 204967.

Loucks, R. G., Reed, R. M., Ruppel, S. C., et al. Morphology, genesis, and distribution of nanometer-scale pores in siliceous mudstones of the Mississippian Barnett Shale. Journal of Sedimentary Research, 2009, 79(12): 848-861.

Masihi, M., Shams, R., King, P. R. Pore level characterization of Micro-CT images using percolation theory. Journal of Petroleum Science and Engineering, 2022, 211: 110113.

Mastalerz, M., Drobniak, A., Hower, J. C. Controls on reservoir properties in organic-matter-rich shales: Insights from MICP analysis. Journal of Petroleum Science and Engineering, 2021, 196: 107775.

Mastalerz, M., Hampton, L. B., Drobniak, A., et al. Significance of analytical particle size in low-pressure N2 and CO2 adsorption of coal and shale. International Journal of Coal Geology, 2017, 178: 122-131.

Mukherjee, M., Vishal, V. Gas transport in shale: A critical review of experimental studies on shale permeability at a mesoscopic scale. Earth-Science Reviews, 2023, 244: 104522.

Nie, X., Zou, C., Li, Z., et al. Numerical simulation of the electrical properties of shale gas reservoir rock based on digital core. Journal of Geophysics and Engineering, 2016, 13(4): 481-490.

Nie, X., Zou, C., Pan, L., et al. Fracture analysis and determination of in-situ stress direction from resistivity and acoustic image logs and core data in the Wenchuan Earthquake Fault Scientific Drilling Borehole-2 (50-1370 m). Tectonophysics, 2013, 593: 161-171.

Ou, C., Li, C. 3D discrete network modeling of shale bedding fractures based on lithofacies characterization. Petroleum Exploration and Development, 2017, 44(2): 336-345.

Ronneberger, O., Fischer, P., Brox, T. U-Net: Convolutional networks for biomedical image segmentation. Paper Presented at Medical Image Computing and Computer Assisted Intervention - MICCAI 2015, Munich, Germany, 5-9 October, 2015.

Roslin, A., Marsh, M., Provencher, B., et al. Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: Semantic segmentation using a 2.5D CNN. Minerals Engineering, 2023, 195: 108027.

Saxena, N., Day-Stirrat, R. J., Hows, A., et al. Application of deep learning for semantic segmentation of sandstone thin sections. Computers & Geosciences, 2021, 152: 104778.

Schlüter, S., Sheppard, A., Brown, K., et al. Image processing of multiphase images obtained via X-ray microtomography: A review. Water Resources Research, 2014, 50(4): 3615-3639.

Shelhamer, E., Long, J., Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

Slatt, R. M., O’Brien, N. R. Pore types in the Barnett and Woodford gas shales: Contribution to understanding gas storage and migration pathways in fine-grained rocks. AAPG Bulletin, 2011, 95(12): 2017-2030.

Valentine, B. J., Hackley, P. C. Scanning electron microscopic evaluation of broad ion beam milling effects to sedimentary organic matter: Sputter-induced artifacts or naturally occurring porosity? International Journal of Coal Geology, 2023, 277: 104348.

Wei, J., Zhang, A., Li, J., et al. Study on microscale pore structure and bedding fracture characteristics of shale oil reservoir. Energy, 2023, 278: 127829.

Wu, Y., Tahmasebi, P., Lin, C., et al. A comprehensive investigation of the effects of Organic-Matter pores on shale properties: A multicomponent and multiscale modeling. Journal of Natural Gas Science and Engineering, 2020, 81: 103425.

Yalamanchi, P., Datta Gupta, S. Estimation of pore structure and permeability in tight carbonate reservoir based on machine learning (ML) algorithm using SEM images of Jaisalmer sub-basin, India. Scientific Reports, 2024, 14(1): 930.

Yang, W., Cai, J., Wang, Q., et al. The controlling effect of organic matter coupling with organic matter porosity on shale gas enrichment of the Wufeng-Longmaxi marine shale. Petroleum Science Bulletin, 2020, 5(2): 148-160. (in Chinese)

Yin, S., Feng, K., Nie, X., et al. Characterization of marine shale in Western Hubei Province based on unmanned aerial vehicle oblique photographic data. Advances in Geo-Energy Research, 2022, 6(3): 252-263.

Zhou, Y., Chang, D., Zheng, J., et al. A fast workflow for automatically extracting the apparent attitude of fractures in 3-D digital core images. Processes, 2023, 11(9): 2517.