HR: 1340h
AN: GC13A-1211    [Abstracts]
TI: An Improved Strategy to Detect Carbon Dioxide Leakage for Verification of Geologic Carbon Sequestration
AU: * Lewicki, J L
EM: jllewicki@lbl.gov
AF: Lawrence Berkeley National Laboratory, Earth Sciences Division, 1 Cyclotron Rd., MS 90-1116 , Berkeley, CA 94720 United States
AU: Hilley, G E
EM: hilley@pangea.stanford.edu
AF: Stanford University, Department of Geological and Environmental Sciences, 450 Serra Mall, Braun Hall, Building 320, Stanford, CA 94305 United States
AU: Oldenburg, C M
EM: cmoldenburg@lbl.gov
AF: Lawrence Berkeley National Laboratory, Earth Sciences Division, 1 Cyclotron Rd., MS 90-1116 , Berkeley, CA 94720 United States
AB: One strategy to mitigate potential climate change associated with elevated atmospheric CO₂ concentrations is the sequestration or storage of anthropogenic CO₂ in deep geologic formations. While the purpose of geologic carbon sequestration is to trap CO₂ underground, the potential exists for CO₂ to migrate away from the intended storage site along permeable pathways such as well bores or faults and pass from the subsurface to the atmosphere. Therefore, to ensure the success of geologic carbon sequestration projects, the long-term storage of CO₂ must be verified. Although numerous technologies are available to measure near-surface CO₂ concentrations and fluxes, storage verification may be challenging due to the large variation in natural background CO₂ fluxes and concentrations, within which a potentially small CO₂ anomaly will have to be detected. To detect and quantify subtle CO₂ leakage signals, we present a strategy that integrates near-surface measurements of CO₂ fluxes or concentrations 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, and temporal stacking (averaging) that reduces noise from temporally uncorrelated background fluxes. 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 a variety of leakage source geometries one might expect to be present at sequestration sites. These simulations provide a means of estimating the number of measurements required to detect a potential CO₂ leakage signal of given magnitude and area. Our results show that given a rigorous and well-planned field sampling program, subtle CO₂ leakage may be detected using the statistical algorithm; however, leakage of very limited spatial extent or exceedingly small magnitude may be difficult to detect with a reasonable set of monitoring resources. This work was supported in part by the Ernest Lawrence Berkeley National Laboratory, managed for the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.
DE: 1847 Modeling
DE: 1875 Vadose zone
DE: 3305 Climate change and variability (1616, 1635, 3309, 4215, 4513)
SC: Global Climate Change [GC]
MN: Fall Meeting 2005