3D digital core reconstruction for deep shale gas reservoirs based on Markov chain-Monte Carlo method

Shengzhen Wang; Junjie Ren

doi:10.1117/12.2656792

13 January 2023 3D digital core reconstruction for deep shale gas reservoirs based on Markov chain-Monte Carlo method

Shengzhen Wang, Junjie Ren

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125100E (2023) https://doi.org/10.1117/12.2656792
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

A deep shale gas reservoir has become a research hotspot in the field of unconventional oil and gas resources in recent years because of its huge reserves and development potential. In view of the characteristics of low permeability, heterogeneity and anisotropy of deep shale gas reservoirs, this paper adopts the Markov Chain-Monte Carlo (MCMC) algorithm based on statistical theory to reconstruct 3D digital cores of shale samples from deep shale gas reservoirs. Compared with traditional methods, such as statistical function, for testing the validity of MCMC algorithm, this paper verifies the validity of MCMC algorithm by calculating gas-water relative permeability curves of the reconstructed digital cores, where MCMC algorithm is applied to obtain different digital cores with similar gas–water relative permeability curves based on the same 2D scanning electron microscope image.

Citation Download Citation

Shengzhen Wang and Junjie Ren "3D digital core reconstruction for deep shale gas reservoirs based on Markov chain-Monte Carlo method", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125100E (13 January 2023); https://doi.org/10.1117/12.2656792

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