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• W2895748024 abstract "Underground hydrocarbon reservoir rocks comprise numerous multiscale irregular pores that significantly affect the mechanical and fluid transport properties of the rock. It is considerably challenging for in situ geological monitoring and laboratory tests to accurately characterize the changes in the interior structure and the corresponding mechanical properties of the rock mass during dynamic excavation processes. The 3-D numerical reconstruction models that are based on the statistical information extracted from X-ray computed tomography (XCT) images provide a feasible method to obtain and characterize the interior pore structures and their effects on the physical responses of reservoir rocks. However, obtaining sufficient high-resolution 2-D XCT images is economically expensive by the traditional fan beam CT scan system. Reconstructing 3-D porous structures by computational methods using statistical information extracted from XCT images usually has low efficiency. Therefore, in this paper, we introduce a novel method to numerically reconstruct natural sandstone rock using a small number of 2-D XCT images. The Bayesian information criterion was used to determine the minimum number of 2-D XCT images required to ensure the expected reconstruction accuracy. A multithread parallel reconstruction scheme was employed to improve the efficiency. The accuracy of the proposed method was verified by comparing the statistical correlation functions, geometrical and topological characteristics, and mechanical properties of pore structures between the reconstructed model and a sandstone prototype. This paper provides a method to achieve fast, economic, and accurate 3-D reconstruction of porous rock." @default.
• W2895748024 created "2018-10-12" @default.
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• W2895748024 date "2019-04-01" @default.
• W2895748024 modified "2023-10-16" @default.
• W2895748024 title "3-D Reconstruction Method for Complex Pore Structures of Rocks Using a Small Number of 2-D X-Ray Computed Tomography Images" @default.
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• W2895748024 doi "https://doi.org/10.1109/tgrs.2018.2869939" @default.
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