Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference

TitleMonitoring Subsurface CO2 Plumes with Sequential Bayesian Inference
Publication TypeConference
Year of Publication2023
AuthorsTing-ying Yu, Rafael Orozco, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann
Conference NameInternational Meeting for Applied Geoscience and Energy
Month08
KeywordsBayesian inference, CCS, deep learning, Imaging, monitoring, SEG, Uncertainty quantification
Abstract

To monitor and predict CO2 plume dynamics during geological carbon storage, reservoir engineers usually perform two-phase flow simulations. While these simulations may provide useful insights, their usefulness is limited due to numerous complicating factors including uncertainty in the dynamics of the plume itself. To study this phenomenon, we consider stochasticity in the dynamic caused by unknown random changes in the injection rate. By conditioning the CO2 plume predictions on seismic observations, we correct the CO2 plume predictions and quantify uncertainty with machine learning.

Notes

(IMAGE, Houston)

URLhttps://slimgroup.github.io/IMAGE2023/SequentialBayes/abstract.html
Presentation
Citation Keyyu2023IMAGEmsc