Geologic carbon sequestration involves the injection of captured carbon dioxide (⁠CO2⁠) into subsurface formations for long-term storage. The movement and fate of the injected CO2 plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for CO2 saturation monitoring to determine the geologic controls on the storage of the injected CO2⁠. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular CO2 injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in CO2 saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the CO2 saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of CO2 saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

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