A Bayesian approach to CO2 leakage detection at saline sequestration sites using pressure measurements

Diffusive leakage of CO2 through the sealing caprock is likely to occur at saline sequestration sites if the permeability of the caprock is too high. Pressure monitoring is a widely used technique for CO2 leakage detection. This study aims to characterize the CO2 leakage level in an idealized CO2 storage site through an assessment of the integrity and permeability of the caprock inferred from pressure measurements in the injection zone. Monte Carlo uncertainty analysis is conducted, allowing the detection power of pressure measurements to be estimated. Based on Bayesian classification theory, the probability of each caprock permeability class given pressure measurements with measurement error can be inferred. The influence of time and location of measurements, the relative magnitude of the measurement error, the CO2 injection rate, and the assumed uncertainty in reservoir properties on the inferred caprock permeability class and the detection power of the pressure monitoring is also evaluated. Pressure monitoring alone is not sufficient to detect a permeable caprock for the system considered in this study, unless the caprock permeability is very high. A significant improvement in the statistical detection power is evident in the case where the uncertainty in reservoir properties is reduced through further site characterization. Complementary monitoring techniques will likely be needed to improve the power of pressure-based CO2 leakage detection to acceptable levels. (C) 2014 Elsevier Ltd. All rights reserved.