ESTIMATING THE PROBABILITY OF LEAK DETECTION BY DEPLOYMENT OF A SOIL CO2 MONITORING NETWORK AT A SEQUESTRATION SITE

This paper demonstrates a methodology combining site characterization and soil CO₂ monitoring data for both detecting CO₂ leaks during geologic sequestration and considering cost-effectiveness trade-offs in the design of the monitoring network. Near surface CO₂ fluxes resulting from a leak are simulated using the TOUGH2 model for different values of soil permeability, leakage rate and vadose zone thickness. Natural background soil CO₂ flux rates are characterized by a Bayesian hierarchical model that predicts the background flux as a function of soil temperature. A presumptive leak is assumed if the monitored flux rate exceeds a critical value corresponding to a very high (e.g., 99%) prediction interval for the natural flux conditioned on temperature. A probabilistic calculation then combines the probability distribution of random leak locations relative to monitoring coordinates, the predicted size of the flux relative to the leakage rate, and the probability that the total flux (natural + leakage) exceeds the critical value for detection. A hypothetical example is presented for an idealized site considering several monitoring network designs with different numbers and locations for measurements. Our results show how increasing the number of monitors increases the probability of detection for a leak of a given size, for different site conditions. Extensions are discussed, including application to actual sites with spatially nonuniform leak probabilities and optimal network design to achieve a given detection probability.