CO2 reservoir monitoring through Bayesian data assimilation

Carbon capture and storage can be implemented by injecting supercritical carbon dioxide (CO2) into geological carbon reservoirs for long-term containment. Monitoring the pressure and saturation of the CO2 is necessary to optimize the injection amount without causing CO2 leakage or seismic activity. Directly measuring the CO2 at locations within the reservoir requires expensive drilling procedures that may damage the reservoir, so direct measurements are sparse and usually lie along injection and production wells. Indirect measurements such as seismic data are typically noisy, and inverting for the CO2 state is ill-posed. Bayesian data assimilation techniques allow us to integrate known physics for CO2 flow into this inversion process. The most well-established data assimilation algorithms are the family of Kalman filters. The ensemble Kalman filter is designed to efficiently work with large problem sizes and nonlinearity. In this work, we apply the ensemble Kalman filter to seismic measurements of a CO2 reservoir, yielding an estimate of the saturation and pressure fields with quantified uncertainties. This method models the CO2 plume state as a random field with a known distribution and assimilates information from seismic measurements with information from a physics model describing the CO2 flow. We show that the data assimilation strategy is a valuable contribution to advancing reservoir monitoring technology.