Joint Inversion of Geophysical Data for Geologic Carbon Sequestration
Monitoring: A Differentiable Physics-Informed Deep Learning Model
Abstract
Geophysical monitoring of geologic carbon sequestration is critical for
risk assessment during and after carbon dioxide (CO2) injection.
Integration of multiple geophysical measurements is a promising approach
to achieve high-resolution reservoir monitoring. However, joint
inversion of large geophysical data is challenging due to high
computational costs and difficulties in effectively incorporating
measurements from different sources and with different resolutions. This
study develops a differentiable physics model for large-scale joint
inverse problems with reparameterization of model variables by deep
neural networks and implementation of a differentiable programming
approach of the forward model. The main novelty is the use of automatic
differentiation and parallel computing for efficient multiphysics data
assimilation. The application to the Sleipner benchmark model
demonstrates that the proposed method is effective in estimation of
reservoir properties from seismic and resistivity data and shows
promising results for CO2 storage monitoring.