Monitoring of CO2 saturation plume movement from time-lapse inverted-seismic and gravity data using an ensemble-based method

Carbon capture and storage in subsurface requires frequent monitoring of CO2 plume movement. The correct assessment of the spatial distribution of CO2 saturation front lowers the risk of leakage and thus environmental hazards. In this work, we propose a stochastic inversion framework, the iterative Ensemble Smoother (iES), to predict changes in CO2 saturation plume (ΔSg) using time-lapse seismic and gravity data simultaneously. Here, change in inverted acoustic impedance (ΔIp) is considered as seismic data. The methodology is based on a Bayesian inversion problem, where the prior is provided as an ensemble of changes in CO2 saturation. The realizations of ΔSg in the prior model are computed using geostatistical method and reservoir flow simulator. The updated ΔSg (posterior) are then obtained based on the misfit between simulated and measured geophysical data. The proposed framework is applied and validated on a 3D reservoir model based on the Johansen formation which is a potential large scale offshore CO2 storage site in the North Sea. The numerical results demonstrate that the proposed framework is capable to monitor CO2 plume movement efficiently.