Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
This work addresses the problem of efficient and accurate monitoring and forecasting for carbon sequestration, which is crucial for environmental management, though it appears incremental as it builds on existing neural operator methods for a specific domain.
The paper tackles the computational challenge of seismic monitoring for carbon storage by introducing a learned coupled inversion framework that uses a Fourier neural operator as a proxy for fluid-flow simulation, reducing computational costs while maintaining accuracy in synthetic experiments and enabling near-zero-cost forecasting of CO2 plumes.
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to effectively invert for the subsurface properties of interest. To drastically reduce the computational cost, we introduce a learned coupled inversion framework based on the wave modeling operator, rock property conversion and a proxy fluid-flow simulator. We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost. We demonstrate the efficacy of our proposed method by means of a synthetic experiment. Finally, our framework is extended to carbon sequestration forecasting, where we effectively use the surrogate Fourier neural operator to forecast the CO2 plume in the future at near-zero additional cost.