Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates

TitleUncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates
Publication TypeConference
Year of Publication2023
AuthorsZiyi Yin, Rafael Orozco, Mathias Louboutin, Ali Siahkoohi, Felix J. Herrmann
Conference NameEngineering Mechanics Institute Conference
Keywordsamortized Bayes, conditional normalizing flows, deep learning, EMI, end-to-end, Fourier neural operators, GCS, inversion, machine learning, Normalizing flows, time-lapse, uncertain quantification

Time-lapse seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics and wave physics. It also requires differentiation through the solvers with respect to properties of interest in the subsurface. In this talk, we present a learned end-to-end inversion framework, which uses a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator in order to greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. Through synthetic experiments, we demonstrate the efficacy of this framework on inverting the subsurface permeability of the reservoir and on monitoring CO2 plumes. We further quantify the uncertainty of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can also forecast the growth of CO2 plumes in the future with uncertainty estimation without any acquired seismic data.


(EMI, Atlanta)

Citation Keyyin2023EMIutm