Predicting CO2 Plume Migration using Deep Neural Networks (Research Track)
Gege Wen (Stanford University)
Abstract
Carbon capture and sequestration (CCS) is an essential climate change mitigation technology for achieving the 2 degree C target. Numerical simulation of CO2 plume migration in the subsurface is a prerequisite to effective CCS projects. However, stochastic high spatial resolution simulations are currently limited by computational resources. We propose a deep neural network approach to predict the CO2 plume migration in high dimensional systems with complex geology. Upon training, the network is able to give accurate predictions that are 6 orders of magnitude faster than traditional numerical simulators. This approach can be easily adopted to history-matching and uncertainty analysis problems to support the scale-up of CCS deployment.