HR: 11:35h
AN: H12D-06    [Abstracts]
TI: The Role of Optimality in Characterizing CO₂ Seepage from Geological Carbon Sequestration Sites
AU: * Cortis, A
EM: acortis@lbl.gov
AF: Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road 90-1116, Berkeley, CA 94720, United States
AU: Oldenburg, C M
EM: cmoldenburg@lbl.gov
AF: Earth Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road 90-1116, Berkeley, CA 94720, United States
AU: Benson, S M
EM: smbenson@stanford.edu
AF: Energy Resources Engineering Department, Stanford University, 367 Panama Street Green Earth Sciences 065, Stanford, CA 94305-2220, United States
AB: Storage of large amounts of carbon dioxide (CO₂) in deep geological formations for greenhouse-gas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application. In this talk we explore various optimization strategies for characterizing surface leakage (seepage) using near-surface measurement approaches such as accumulation chambers and eddy covariance towers. Seepage characterization objectives and limitations need to be defined carefully from the outset especially in light of large natural background variations that can mask seepage. The cost and sensitivity of seepage detection are related to four critical length scales pertaining to the size of the: (1) region that needs to be monitored; (2) footprint of the measurement approach; (3) main seepage zone; and (4) region in which concentrations or fluxes are influenced by seepage. Seepage characterization objectives may include one or all of the tasks of detecting, locating, and quantifying seepage. Each of these tasks has its own optimal strategy. Detecting and locating seepage in a region in which there is no expected or preferred location for seepage nor existing evidence for seepage requires monitoring on a fixed grid, e.g., using eddy covariance towers. The fixed-grid approaches needed to detect seepage are expected to require large numbers of eddy covariance towers for large-scale geologic CO₂ storage. Once seepage has been detected and roughly located, seepage zones and features can be optimally pinpointed through a dynamic search strategy, e.g., employing accumulation chambers and/or soil-gas sampling. Quantification of seepage rates can be done through measurements on a localized fixed grid once the seepage is pinpointed. Background measurements are essential for seepage detection in natural ecosystems. Artificial neural networks are considered as regression models useful for distinguishing natural system behavior from anomalous behavior suggestive of CO₂ seepage without need for detailed understanding of natural system processes. Because of the local extrema in CO₂ fluxes and concentrations in natural systems, simple steepest-descent algorithms are not effective and evolutionary computation algorithms are proposed as a paradigm for dynamic monitoring networks to pinpoint CO₂ seepage areas. This work was carried out within the ZERT project, funded by the Assistant Secretary for Fossil Energy, Office of Sequestration, Hydrogen, and Clean Coal Fuels, National Energy Technology Laboratory, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
DE: 0555 Neural networks, fuzzy logic, machine learning
DE: 1848 Monitoring networks
SC: Hydrology [H]
MN: 2007 Fall Meeting