Probabilistic Design of a Near-Surface CO2 Leak Detection System

Abstract

A methodology is developed for predicting the performance of near-surface CO(2) leak detection systems at geologic sequestration sites. The methodology integrates site characterization and modeling to predict the statistical properties of natural CO(2) fluxes, the transport of CO(2) from potential subsurface leakage points, and the detection of CO(2) surface fluxes by the monitoring network. The probability of leak detection is computed as the probability that the leakage signal is sufficient to increase the total flux beyond a statistically determined threshold. The methodology is illustrated for a highly idealized site monitored with CO(2) accumulation chamber measurements taken on a uniform grid. The TOUGH2 code is used to predict the spatial profile of surface CO(2) fluxes resulting from different leakage rates and different soil permeabilities. A response surface is fit to the TOUGH2 results to allow interpolation across a continuous range of values of permeability and leakage rate. The spatial distribution of leakage probability is assumed uniform in this application. Nonlinear, nonmonotonic relationships of network performance to soil permeability and network density are evident. In general, dense networks (with ∼10-20 m between monitors) are required to ensure a moderate to high probability of leak detection.