SCCS Fall Seminar - David Cameron
Data assimilation to detect leaks in carbon storage aquifers
Abstract: We investigate the role of pressure data in the detection of leaks in carbon storage aquifers. The amount of leakage through the cap rock is determined by simulating injection in a three-region model, includes a storage aquifer, seal and overlying aquifer. The effect of varying the leakage position, leakage permeability and aquifer geology is analyzed by simulating thousands of different leakage cases. A data assimilation method is applied to determine leakage locations and permeabilities for a number of these cases, with pressures at sensor wells and injection wells providing the measured data. Particle Swarm Optimization is used for the minimizations associated with the data assimilation problem. A data-rich scenario with nine sensor wells (completed in the overlying aquifer and storage formation) and a data-scarce scenario with four sensor wells (completed only in the overlying aquifer) are considered. Results indicate that the history matching procedure effectively locates leakage positions in cases with a single leak, for both the data-rich and data-scarce scenarios. For cases with multiple leaks, however, the procedure is less reliable, though the data-rich scenario is shown to provide better matches than the data-scarce scenario.