油页岩
地质学
表征(材料科学)
石油工程
计算机科学
材料科学
纳米技术
古生物学
作者
Yongfei Yang,Fugui Liu,Qi Zhang,Yingwen Li,Ke Wang,Quan Xu,Jiangshan Yang,Zhenxiao Shang,Jun‐Ming Liu,Jinlei Wang,Ziwei Liu,Huaisen Song,Weichen Sun,Jiawei Li,Jun Yao
出处
期刊:Energy & Fuels
[American Chemical Society]
日期:2023-01-25
卷期号:37 (4): 2475-2497
被引量:34
标识
DOI:10.1021/acs.energyfuels.2c03470
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI