Abstract
In order to solve the problems of time-consuming, poor repeatability and inability to directly reflect the pore structure of the core by traditional experimental methods to obtain the reservoir parameters, a method was proposed to study the pore structure of inner core using digital core and pore network model. Firstly, the core CT scan image is processed by filtering and denoising, threshold segmentation and pore-throat network skeleton extraction. Then, the digital core and pore network model are constructed by digital image technology and maximum sphere algorithm, and the core physical parameters are statistically analyzed. Finally, a digital core pore network model is used to simulate oil-water two-phase flow. The results show that the digital core pore network model can better reflect the real core pore space characteristics and accurately reflect the pore throat distribution and morphology characteristics. Through practical application, the 3D pore network model of a digital core can accurately reflect the core's microporosity and throat structure and fully understand the mechanism of fluid flow in porous media, which has high application value. In addition, the method can be repeated many times, which is time-consuming and controllable and makes up for the limitations of conventional physical experiments.
References
Hai-Tao W, Li W, Fu-Qiang L, Jin-Yan Z. Investigation of image segmentation effect on the accuracy of reconstructed digital core models of coquina carbonate. Applied Geophysics 2020; 17: 501-12. https://doi.org/10.1007/s11770-020-0846-2.
Zhao Y, Li X, Fang Z, Yong’an Z, Mingke Z, Xing D. Numerical simulation of resistivity of 3D digital core of fractured shale oil reservoir. Journal of Xi’an Shiyou University (Natural Science Edition) 2022; 37: 51-7. https://doi.org/10.3969/j.issn.1673-064X.2022.01.006.
Li Q, Chen Z, He J-J, Hao S-Y, Wang R, Yang H-T, et al. Reconstructing the 3D digital core with a fully convolutional neural network. Applied Geophysics 2020; 17: 401-10. https://doi.org/10.1007/s11770-020-0822-x.
Cui L-K, Sun J-M, Yan W-C, Dong H-M. Multi-scale and multi-component digital core construction and elastic property simulation. Applied Geophysics 2020; 17: 26-36. https://doi.org/10.1007/s11770-019-0789-7.
Mosser L, Dubrule O, Blunt MJ. Stochastic reconstruction of an oolitic limestone by generative adversarial networks. Transp Porous Media 2017; 125: 81-103. https://doi.org/10.1007/s11242-018-1039-9.
Altowairqi Y, Rezaee R, Urosevic M, Piane CD. Measuring ultrasonic characterisation to determine the impact of TOC and the stress field on shale gas anisotropy. The APPEA Journal 2013; 53: 245-54. https://doi.org/10.1071/AJ12021.
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 39, IEEE; 2015; p. 3431-40. https://doi.org/10.1109/CVPR.2015.7298965.
Xiao X, Zhang X, Zhang J, Luo X. Study of three-dimensional digital core reconstruction based on multiple-point geostatistics in a cylindrical coordinate system. IEEE Access 2019; 7: 8522-7. https://doi.org/10.1109/ACCESS.2019.2957580.
Arns CH, Bauget F, Sakellariou A, Senden T.J, Sheppard AP, Sok RM, et al. Digital core laboratory: petrophysical analysis from 3D imaging of reservoir core fragments. Petrophysics 2005; 46: 122-34.
Zhou S, Yan G, Xue H, Guo W, Li X. 2D and 3D nanopore characterization of gas shale in Longmaxi formation based on FIB-SEM. Mar Pet Geol 2016; 73: 174-80. https://doi.org/10.1016/j.marpetgeo.2016.02.033.
Wu Y, Lin C, Ren L, Yan W, An S, Chen B, et al. Reconstruction of 3D porous media using multiple-point statistics based on a 3D training image. J Nat Gas Sci Eng 2018; 51: 29-140. https://doi.org/10.1016/j.jngse.2017.12.032.
Gubaidullin MG, Belozerov IP. Digital core modeling technology for determining the reservoir-capacitive properties of terrigenous reservoirs. IOP Conf Ser Mater Sci Eng 2021; 1201: 60-73. https://doi.org/10.1088/1757-899X/1201/1/012065.
Mahendran Siddharth, Ali Haider, Vidal Rene. 3D pose regression using convolutional neural networks: IEEE conference on computer vision and pattern recognition workshops (CVPRW)., IEEE; 2017, p. 2174-82. https://doi.org/10.1109/CVPRW.2017.73.
Wang Y, Arns CH, Rahman SS, Arns J-Y. Porous structure reconstruction using convolutional neural networks. Math Geosci 2018; 50: 781-99. https://doi.org/10.1007/s11004-018-9743-0.
Li Q, Chen Z, He J-J, Hao S-Y, Wang R, Yang H-T, et al. Reconstructing the 3D digital core with a fully convolutional neural network. Applied Geophysics 2021; 17: 401-10. https://doi.org/10.1007/s11770-020-0822-x.
Hasnan HK, Sheppard A, Hassan MHA, Abdullah WH. Digital core analysis: Characterizing reservoir quality through thin sandstone layers in heterolithic rocks. J Appl Geophy 2020; 182: 104-15. https://doi.org/10.1016/j.jappgeo.2020.104178.
Wu Y, Lin C, Ren L, Yan W, An S, Chen B, et al. Reconstruction of 3D porous media using multiple-point statistics based on a 3D training image. J Nat Gas Sci Eng 2019; 51: 129-40. https://doi.org/10.1016/j.jngse.2017.12.032.
Aghaei A, Piri M. Direct pore-to-core up-scaling of displacement processes: Dynamic pore network modeling and experimentation. J Hydrol (Amst) 2015; 522: 488-509. https://doi.org/10.1016/j.jhydrol.2015.01.004.
Madonna C, Quintal B, Frehner M, Almqvist BSG, Pistone M, Marone F, et al. Synchrotron-based X-ray tomographic microscopy for rock physics investigations. Geophysics 2020; 78: 53-64. https://doi.org/10.1190/GEO2012-0113.1.
Gubaidullin MG, Belozerov I.P. Digital core modeling technology for determining the reservoir-capacitive properties of terrigenous reservoirs. IOP Conf Ser Mater Sci Eng 2021; 1201: 012065-63. https://doi.org/10.1088/1757-899X/1201/1/012065.
Zhu W. Study on digital rock reconstruction using the process-based method. Progress in Geophysics 2020; 35: 1539-44. https://doi.org/10.6038/pg2020DD0232.
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