Seismic Monitoring of CO₂ Plume Dynamics Using Ensemble Kalman Filtering

Monitoring carbon dioxide (CO2) injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO2 injection rates. Bayesian sequential 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 CO2 plume. Existing literature in the seismic-CO2 monitoring domain uses small physical systems with unscalable DA algorithms, ignores the CO2 flow dynamics, or simulates seismic data without the wave equation or with unrealistic survey designs. We improve upon existing DA literature in this domain by applying the scalable ensemble Kalman filter (EnKF) DA algorithm to a high-dimensional CO2 reservoir using two-phase flow dynamics and time-lapse full-waveform seismic data with a realistic surface-seismic survey design. We show that this DA method is more accurate compared to using either the seismic data or the fluid physics alone. Furthermore, we show the stability of this method by testing a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO2 reservoir monitoring.