Lawrence Berkeley National Laboratory

To detect and quantify subtle surface CO2 leakage signals, we present a strategy that combines measurements of CO2 fluxes or concentrations in the near-surface environment with an algorithm that enhances temporally- and spatially-correlated leakage signals while suppressing random background noise. The algorithm consists of a filter that highlights spatial coherence in the leakage signal, and temporal stacking (averaging) that reduces noise from temporally uncorrelated background fluxes/concentrations. We assess the performance of our strategy using synthetic data sets in which the surface leakage signal is either specified directly or calculated using flow and transport simulations of leakage source geometries one might expect to be present at sequestration sites. We estimate the number of measurements required to detect a potential CO2 leakage signal of given magnitude and area. Results show that given a rigorous field-sampling program, subtle CO2 leakage may be detected using the algorithm; however, leakage of very limited spatial extent or exceedingly small magnitude may be difficult to detect with a reasonable set of monitoring resources.