Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering

Monitoring CO 2 injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO 2 injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO 2 plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO 2 monitoring domain by applying this scalable DA algorithm to a high-dimensional CO 2 reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO 2 saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO 2 reservoir monitoring.