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.

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A Bayesian belief network (BBN) methodology is developed for integrating CO2 leak detection inferences from multiple monitoring technologies at a geologic sequestration site. The methodology is demonstrated using two monitoring methods: near‐surface soil CO2 flux measurement and near‐surface perfluoromethylcyclohexane (PMCH) tracer monitoring, from the Zero Emission Research and Technology (ZERT) release test in 2007. Statistical models are fitted to natural background soil CO2 flux and background PMCH tracer concentrations to determine critical levels for leak inference. Leakage‐induced increments of soil CO2 flux and PMCH tracer concentrations are computed through TOUGH2 simulations for different leakage rates and subsurface permeabilities. The background characterizations and the simulation results are subsequently used to determine the conditional probabilities of leak detection in the BBN model. The BBN model is illustrated for use in evaluating the performance of alternative monitoring networks in a network design phase, and for combining inferences from multiple observations in the operational phase of a site. The detection capabilities of combined networks with different monitoring densities for soil CO2 flux and PMCH tracer concentration are compared. Given the test condition, the greater sensitivity of the PMCH tracer allows it to detect smaller leaks, while detection by the soil CO2 flux monitors implies that a larger leak is most likely present. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd