38 citations · 39 across the 3 of their papers we have counts for
3 papers
physics.geo-ph2021
Computationally Efficient Multiscale Neural Networks Applied To Fluid Flow In Complex 3D Porous Media
Javier Santos, Ying Yin, Honggeun Jo +6
The permeability of complex porous materials can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particu…
physics.app-ph2020★ 38 cited
Modeling nanoconfinement effects using active learning
Javier E. Santos, Mohammed Mehana, Hao Wu +5
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge…
physics.geo-ph2019★ 1 cited
Predicting Effective Diffusivity of Porous Media from Images by Deep Learning
Haiyi Wu, Wen-Zhen Fang, Qinjun Kang +2
We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures…