Digital core reconstruction for shale gas reservoirs based on Markov chain-Monte Carlo method and large-field splicing technology

Shengzhen Wang; Junjie Ren; Shuai Wu

doi:10.1117/12.2691841

23 August 2023 Digital core reconstruction for shale gas reservoirs based on Markov chain-Monte Carlo method and large-field splicing technology

Shengzhen Wang, Junjie Ren, Shuai Wu

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Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127841B (2023) https://doi.org/10.1117/12.2691841
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

Deep shale gas reservoirs are characterized by deep burial, low permeability, heterogeneity and anisotropy, with pore sizes concentrated at micro- and nano-scale. The reconstruction of 3D digital cores based on local 2D scanning electron microscope(SEM) images have some problems, such as strong heterogeneity of pore distribution and poor representativeness of visual field. In this paper, we use the large-field splicing technology to obtain a large-field high-precision image by concatenating consecutive small-field high-precision images of shale gas reservoirs; Based on the large-field image, the Markov chain-Monte Carlo (MCMC) algorithm is adopted to reconstruct 3D digital cores of shale gas reservoirs. This algorithm is based on the conditional probability distribution constraints, which can better reflect the pore distribution and pore connectivity properties of the reservoir. Meanwhile, the large-field splicing technology also addresses the problem that small-field images are not representative of shale gas reservoirs.

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Shengzhen Wang, Junjie Ren, and Shuai Wu "Digital core reconstruction for shale gas reservoirs based on Markov chain-Monte Carlo method and large-field splicing technology", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127841B (23 August 2023); https://doi.org/10.1117/12.2691841

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KEYWORDS

3D image reconstruction

Scanning electron microscopy

Image restoration

Reconstruction algorithms

Autocorrelation

Porosity

Fourier transforms

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