Recent Advances in Multiscale Digital Rock Reconstruction, Flow Simulation, and Experiments during Shale Gas Production

Yongfei Yang, Fugui Liu, Qi Zhang, Yingwen Li, Ke Wang, Quan Xu, Jiangshan Yang, Zhenxiao Shang, Junming Liu, Jinlei Wang, Ziwei Liu, Huaisen Song, Weichen Sun, Jiawei Li, Jun Yao

Research output: Journal article publicationReview articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

The complex and multiscale nature of shale gas transport imposes new challenges to the already well-developed techniques for conventional reservoirs, especially digital core analysis. Multiscale complicated pore systems and distinctive properties limit most reconstruction methods not applicable. High-precision imaging experiments play a key role in the characterization of pore structures and mineral components. While the exhilarating breakthroughs in physical experimental methods and hybrid superposition methods have made significant achievements in shale digital rock reconstruction, rapidly evolving deep learning methods also present a promising option. Benefiting from the digital rock techniques, the pore-scale flow of shale gas can be directly simulated based on digital rock or indirectly modeled using the pore network model. It is precise and realistic to investigate the shale gas flow at the pore scale considering the desorption, surface diffusion, and slippage in nanopores. In this paper, we reviewed the recent advances in off-mentioned methods and processes and presented a hand for the research in this field.

Original languageEnglish
Pages (from-to)2475-2497
Number of pages23
JournalEnergy and Fuels
Volume37
Issue number4
DOIs
Publication statusPublished - 16 Feb 2023
Externally publishedYes

ASJC Scopus subject areas

  • General Chemical Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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