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

TL;DR

This paper introduces a learned coupled inversion framework using Fourier neural operators to efficiently monitor and forecast carbon sequestration by approximating complex physics with a surrogate model, significantly reducing computational costs.

Contribution

The paper presents a novel framework that integrates Fourier neural operators as surrogates for fluid-flow simulation in coupled seismic inversion and forecasting, enabling faster computations.

Findings

Fourier neural operators accurately approximate fluid-flow simulations.

The framework reduces computational costs by using surrogate models.

Effective forecasting of CO2 plumes with minimal additional computation.

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 CO2 plume in the future at near-zero additional cost.