Abstract and subjects
Uncertainty in geological models usually leads to large uncertainty in the predictions of risk- related system properties and/or risk metrics (e.g., CO2 plumes and CO2/brine leakage rates) at a geologic CO2 storage site. Different types of data (e.g., point measurements from monitoring wells and spatial data from 4D seismic surveys) can be leveraged or assimilated to reduce the risk predictions. In this work, we develop a novel framework for spatial data assimilation and risk forecasting. Under the U.S. Department of Energy's National Risk Assessment Partnership (NRAP), we have developed a framework using an ensemble- based data assimilation approach for spatial data assimilation and forecasting. In particular, we took CO2 saturation maps interpreted from 4D seismic surveys as inputs for spatial data assimilation. Three seismic surveys at Years 1, 3, and 5 were considered in this study. Accordingly, three saturation maps were generated for data assimilation. The impact from the level of data noise was also investigated in this work. Our results show increased similarity between the updated reservoir models and the "ground- truth" model with the increased number of seismic surveys. Predictive accuracy in CO2 saturation plume increases with the increased number of seismic surveys as well. We also observed that with the increase in the level of data noise from 1% to 10%, the difference between the updated models and the ground truth does not increase significantly. Similar observations were made for the prediction of CO2 plume distribution at the end of the CO2 injection period by increasing the data noise.