Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning

Zhen Zhang, Xupeng He, Yiteng Li, Marwa AlSinan, Hyung Kwak, Hussein Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Geologic CO2 sequestration (GCS) is a promising engineering measure to reduce global greenhouse emissions. However, accurate detection of CO2 leakage locations from underground traps remains a challenging problem. This study proposes a workflow that combines Bayesian inversion and deep learning algorithms to detect the sites of CO2 leakage. There are four main steps in the workflow. Step 1: we identify the key uncertainty parameters. Here we mean the CO2 leakage location. Then we get the training set using Latin Hypercube Sampling (LHS) method and perform the high-fidelity simulation using CMG. Step 2: we train the surrogate model using the data set collected from the last step, in which the Bayesian optimization is used to tune the hyperparameters automatically. Step 3: we perform the Bayesian inversion to invert the CO2 leakage location, in which the surrogate serves as the forward model to reduce the computational expense. Step 4: we feed the inverted CO2 leakage location into the high-fidelity model to produce the pressure response. If the error between the pressure response between the surrogate and the high-fidelity model is small enough, the solution is accepted. Otherwise, the accuracy of the surrogate model and the convergence of the Bayesian inversion process are revisited. We validate this method using a synthetic model of CO2 injection. Results show that the proposed Bayesian inversion assisted by the deep learning algorithm can accurately detect the CO2 leakage location with narrow uncertainties. This approach provides an accurate and efficient way to detect CO2 leakage locations in real-time applications.
Original languageEnglish (US)
Title of host publicationDay 1 Sun, February 19, 2023
StatePublished - Mar 7 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-03-10
Acknowledgements: We would like to thank CMG Ltd. for providing the IMEX academic license, KAUST for the support, and UQLab for the software license.


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