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

 Title Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators Publication Type Conference Year of Publication 2022 Authors Ziyi Yin, Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann Conference Name International Meeting for Applied Geoscience and Energy Expanded Abstracts Month 05 Keywords CCS, deep learning, Fourier neural operators, inversion, machine learning, multiphysics, SEG, time-lapse Abstract 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 CO$_2$ plume in the future at near-zero additional cost. Notes (IMAGE, Houston) URL https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/yin2022SEGlci/paper.html DOI 10.1190/image2022-3722848.1 Presentation Software Citation Key yin2022SEGlci