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Seismic monitoring of carbon storage sequestration is a challenging probleminvolving both fluid-flow physics and wave physics. Additionally, monitoringusually requires the solvers for these physics to be coupled and differentiableto effectively invert for the subsurface properties of interest. To drasticallyreduce the computational cost, we introduce a learned coupled inversionframework based on the wave modeling operator, rock property conversion and aproxy fluid-flow simulator. We show that we can accurately use a Fourier neuraloperator as a proxy for the fluid-flow simulator for a fraction of thecomputational cost. We demonstrate the efficacy of our proposed method by meansof a synthetic experiment. Finally, our framework is extended to carbonsequestration forecasting, where we effectively use the surrogate Fourierneural operator to forecast the CO2 plume in the future at near-zero additionalcost.

Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators