Monitoring CO2-plume evolution is essential for ensuring geologic storage security and integrity. Traditional numerical simulation-based data-assimilation workflows are computationally expensive, an aspect further complicated by the fact that geologic uncertainty must be incorporated for robust performance prediction. Therefore, reservoir simulation and model calibration accounting for geologic uncertainty are not amenable to real-time monitoring of CO2-plume evolution for large-scale applications. The authors propose a deep-learning (DL)-based approach that enables near-real-time CO2-plume visualization and rapid data assimilation that incorporates multiple geological realizations for predicting future plume evolution and area-of-review (AOR) determination.
Methodology
The proposed DL model takes available observed data from CO2 sequestration projects as input, including bottomhole pressure at the injection well, distributed pressure measurements at monitoring wells, and CO2-saturation-log data at monitoring wells.
