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

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.

Automated summary

The complex and multiscale nature of shale gas transport presents new challenges to conventional reservoir techniques, particularly digital core analysis. High-precision imaging experiments and deep learning methods show promise in characterizing pore structures and modeling shale gas flow. This paper reviews recent advances in these methods and presents a guide for future research in this field.