Toward an adaptive monitoring design for leakage risk – Closing the loop of monitoring and modeling

Abstract Monitoring is a key component of risk management at geologic carbon storage (GCS) sites, serving both to help operators understand and manage site performance, and to assure the public and other stakeholders that effective containment is maintained and impacts avoided. Potential leakage of CO 2 and/or brine through wellbores, faults, and fractures to potable groundwater resources is a primary risk concern at onshore GCS sites. In this paper, we present an adaptive methodology for leakage risk-based monitoring design. The methodology uses a risk event tree to predict the likelihood of leakage occurrence, with detection probabilities of risk events estimated for multiple monitoring plans. The overall detection probability of a proposed monitoring plan incorporates baseline data, stochastically simulated leakage events, and the likelihood that a set of technologies will detect the changes in baseline conditions induced by the simulated leakage events. The adaptive monitoring design methodology is demonstrated with a representative case study of CO 2 and brine leaking from a well to a potable groundwater aquifer using simulated data at the High Plains aquifer in the United States. Groundwater quality parameters, pH, total dissolved solids and benzene concentrations, were used to calculate the corresponding detection probabilities of conventional groundwater sampling and fixed sensor monitoring for selected leakage scenarios. The overall detection probability considering all monitoring information was then calculated to evaluate proposed monitoring plan designs. Finally, a simple optimization problem to maximize detection probability with constrained monitoring resources was presented as an application example to close the loop of monitoring and modeling.