Spatial Data Assimilation in Geologic CO2 Sequestration

Geologic uncertainty 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 have developed a framework based on an ensemble-based data assimilation approach called ES-MDA-GEO for spatial data assimilation to reduce the uncertainty in risk forecast. In particular, we took CO2 saturation maps (can be 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 of 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 (see Fig. 1). Predictive accuracy in CO2 saturation plume increases with the increased number of seismic surveys as well (see Fig. 2). We also observed that with the increase of the level of data noise, the difference between the updated models and the ground truth increases. Similar observation was made for the prediction of CO2 plume distribution at the end of post CO2 injection period by increasing the data noise. Last but not least, the spatial data are more valued information than point measurements from monitoring wells to reduce the uncertainty in the risk predictions.