Rapid Forecasting of Carbon Storage and Migration using Neural Operators and Transfer Learning

Presenter

Siddharth Misra, Ph.D.
Associate Professor
Harold Vance Department of Petroleum Engineering
Texas A&M University

Description

The combination of Fourier Neural Operator (FNO) network and Transfer Learning offers a powerful solution for reducing both computational time and data needed in predicting the 3D migration of CO₂ plumes within a storage reservoir. This approach enables efficient and accurate analysis of CO₂ saturation and pressure evolution in large reservoirs with diverse geological and engineering conditions. The effectiveness of this technique was demonstrated through experiments on the 3D SACROC geomodel, which showed that the transfer learning implementation requires 75% less training time and 50% less data compared to conventional approaches. A traditional forecasting of 40 minutes was reduced to 1 min using this approach.