9 citations · 11 across the 3 of their papers we have counts for
3 papers
Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations
Yifu Han, Francois P. Hamon, Louis J. Durlofsky
Deep learning surrogate modeling shows great promise for subsurface flow applications, but the training demands can be substantial. Here we introduce a new surrogate modeling frame…
Surrogate Model for Geological CO2 Storage and Its Use in Hierarchical MCMC History Matching
Yifu Han, Francois P. Hamon, Su Jiang +1
Deep-learning-based surrogate models show great promise for use in geological carbon storage operations. In this work we target an important application - the history matching of s…
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…