Probabilistic evaluation of above-zone pressure and geochemical monitoring for leakage detection at geological carbon sequestration site

Above-zone monitoring has been proposed as a promising technique for monitoring geological carbon sequestration (GCS) projects for storage integrity. This study presents a probabilistic framework to assess and compare effectiveness of above-zone monitoring of pressure and geochemical parameters for the pilot GCS project at the Cranfield site. Pressure responses in the above-zone monitoring interval (AZMI) to leaks were modeled with a single-phase flow equation and solved analytically and geochemical responses were modeled with a solute transport equation coupled with geochemical reactions, such as mineral dissolution, aqueous complexation, cation exchange, caused by intrusion of the leaked fluids having lower pH and solved semi-analytically. A total of one million realizations were generated using the Monte-Carlo method in terms of 11 parameters required to calculate pressure and geochemical responses to leaks in the AZMI. For each individual realization, time to detect leaks in the AZMI was estimated with pressure with threshold values 200 MPa, 2000 MPa, and 10000 MPa or geochemical parameter (dissolved CO2 in groundwater) with threshold values of 2.33 mmol/kg H2O, 2.82 mmol/kg H2O, 6.94 mmol/kg H2O as indicator and further analyzed statistically over the one million realizations. Our results show that pressure monitoring can detect more than 90% of the leaks whereas geochemical monitoring can detect only up to 50% of the leaks. Detection time ranges from several hours to 200 days for pressure monitoring and from 1 year to 30 years for geochemical monitoring, suggesting that pressure monitoring can provide much early leakage detection. The local relative and global sensitivity analyses were conducted to rank relative importance of the model parameters. Our results show that detection time is sensitive to distance of monitoring well, leakage rate, thickness of the AZMI, and permeability. The global sensitivity analysis shows that the total indices are much higher than the first-order indices, indicating that interactions among the model parameters are crucial to detection time. The probabilistic framework presented in this study can be easily applied to the site configurations of other commercial-scale GCS projects.