9 citations · 9 across the 4 of their papers we have counts for
4 papers
Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon Storage
Hewei Tang, Qingkai Kong, Joseph P. Morris
Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration…
Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR
Hewei Tang, Pengcheng Fu, Honggeun Jo +5
Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projec…
Machine learning-based porosity estimation from spectral decomposed seismic data
Honggeun Jo, Yongchae Cho, Michael J. Pyrcz +2
Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. Although impedance inversion is often used by combin…
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…