33 citations · 36 across the 4 of their papers we have counts for
5 papers · 1 filter
Prediction of Fault Slip Tendency in CO Storage using Data-space Inversion
Xiaowen He, Su Jiang, Louis J. Durlofsky
Accurately assessing the potential for fault slip is essential in many subsurface operations. Conventional model-based history matching methods, which entail the generation of post…
Recurrent Transformer U-Net Surrogate for Flow Modeling and Data Assimilation in Subsurface Formations with Faults
Yifu Han, Louis J. Durlofsky
Many subsurface formations, including some of those under consideration for large-scale geological carbon storage, include extensive faults that can strongly impact fluid flow. In…
Likelihood-Free Inference and Hierarchical Data Assimilation for Geological Carbon Storage
Wenchao Teng, Louis J. Durlofsky
Data assimilation will be essential for the management and expansion of geological carbon storage operations. In traditional data assimilation approaches a fixed set of geological…
Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Data
Nanzhe Wang, Louis J. Durlofsky
Geological carbon storage entails the injection of megatonnes of supercritical CO2 into subsurface formations. The properties of these formations are usually highly uncertain, whic…
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…