Statistical Approaches to Quantifying Uncertainty of Monitoring and Performance at Geologic CO2 Storage Sites

This work evaluates the detection sensitivity of deep subsurface pressure monitoring within an uncertain CO2 sequestration system by linking the output of an analytical reduced-order model and first-order uncertainty analysis. A baseline (nonleaky) modeling run was compared against 10 different leakage scenarios, where the cap rock permeability was increased by factors of 2 to 100 (cap rock permeability from 10 -3 to 10 -1 millidarcy). The uncertainty variance outputs were used to develop percentile estimates and detection sensitivity for pressure throughout the deep subsurface as a function of space (lateral distance from the injection wells and vertical orientation within the reservoir) and time (years since injection), or P(x, z, t). Conditional probabilities were computed for combinations of x, z, and t, which were then used to generate power curves for detecting leakage scenarios. The results suggest that measurements of the absolute change in pressure within the target injection aquifer would not be able to distinguish small leakage rates (i.e., less than 50x baseline) from baseline conditions, and that only large leakage rates (i.e., >100x baseline) would be discriminated with sufficient statistical power (>99%). Combining measurements, for example by taking the ratio of formation pressure in Aquifer 2/Aquifer 1, provides better statistical power for distinguishing 4 Chapter 2 was the basis for the peer-reviewed publication. In the publication, several figures were presented in the Electronic Supplementary Material. However, in this dissertation the figures have been renumbered in consecutive numerical order and are embedded into the main text. Azzolina, N.A.; Small, M.J.; Nakles, D.V.; and Bromhal, G.S. (2014) Effectiveness of subsurface pressure monitoring for brine leakage detection in an uncertain CO2 sequestration system. Stochastic Environmental Research and Risk Assessment, 28:895-909.