11 citations · 22 across the 4 of their papers we have counts for
4 papers
Learning Pore-scale Multiphase Flow from 4D Velocimetry
Chunyang Wang, Linqi Zhu, Yuxuan Gu +9
Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO storage and underground hydrogen storage, yet pore-scale dyn…
Learning CO plume migration in faulted reservoirs with Graph Neural Networks
Xin Ju, François P. Hamon, Gege Wen +3
Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO geological storage. Accurately capturing t…
Deep-learning-based coupled flow-geomechanics surrogate model for CO sequestration
Meng Tang, Xin Ju, Louis J. Durlofsky
A deep-learning-based surrogate model capable of predicting flow and geomechanical responses in CO2 storage operations is presented and applied. The 3D recurrent R-U-Net model comb…
A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage
Hewei Tang, Pengcheng Fu, Christopher S. Sherman +6
Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geolo…