Time-lapse seismic data inversion for estimating reservoir parametersusing deep learning

Geological carbon sequestration involves the injection of captured carbon dioxide (CO2) into sub-surface formations for long-term storage. The movement and fate of the injected CO2 plume is ofgreat concern to regulators as monitoring helps to identify potential leakage zones and determinesthe possibility of safe long-term storage. To address this concern, we design a deep learning frame-work for carbon dioxide (CO2) saturation monitoring to determine the geological controls on thestorage of the injected CO2. We use different combinations of porosities and permeabilities for agiven reservoir to generate saturation and velocity models. We train the deep learning model with afew 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-lapseseismic response to the change in CO2 saturation during the training phase. We then apply thetrained model to data sets comprising different time-lapse seismic image slices (corresponding todifferent time instances) generated using different porosity and permeability distributions that arenot part of the training to estimate the CO2 saturation values along with the plume extent. Theproposed 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 seismicdata. The proposed method improves the efficiency of time-lapse inversion by streamlining thelarge number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geological uncertainty for a given reservoir by accounting for the statis-tical distribution of porosity and permeability during the training phase. Tests on different examplesverify the effectiveness of the proposed approach