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Title: The role of optimality in characterizing CO2 seepage from geological carbon sequestration sites

Abstract

Storage of large amounts of carbon dioxide (CO{sub 2}) in deep geological formations for greenhouse gas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application. In this work 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, and (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 expectedmore » to require large numbers of eddy covariance towers for large-scale geologic CO{sub 2} 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 CO{sub 2} seepage without need for detailed understanding of natural system processes. Because of the local extrema in CO{sub 2} 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 CO{sub 2} seepage areas.« less

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Earth Sciences Division
OSTI Identifier:
946460
Report Number(s):
LBNL-1417E
TRN: US200903%%476
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Name: Journal of Greenhouse Gas Control
Country of Publication:
United States
Language:
English
Subject:
54; 58; ALGORITHMS; CARBON DIOXIDE; CARBON SEQUESTRATION; DETECTION; ECOSYSTEMS; GREENHOUSES; MITIGATION; MONITORING; NEURAL NETWORKS; OPTIMIZATION; SAMPLING; SENSITIVITY; STORAGE; TESTING

Citation Formats

Cortis, Andrea, Oldenburg, Curtis M., and Benson, Sally M. The role of optimality in characterizing CO2 seepage from geological carbon sequestration sites. United States: N. p., 2008. Web. doi:10.1016/j.ijggc.2008.04.008.
Cortis, Andrea, Oldenburg, Curtis M., & Benson, Sally M. The role of optimality in characterizing CO2 seepage from geological carbon sequestration sites. United States. doi:10.1016/j.ijggc.2008.04.008.
Cortis, Andrea, Oldenburg, Curtis M., and Benson, Sally M. Mon . "The role of optimality in characterizing CO2 seepage from geological carbon sequestration sites". United States. doi:10.1016/j.ijggc.2008.04.008. https://www.osti.gov/servlets/purl/946460.
@article{osti_946460,
title = {The role of optimality in characterizing CO2 seepage from geological carbon sequestration sites},
author = {Cortis, Andrea and Oldenburg, Curtis M. and Benson, Sally M.},
abstractNote = {Storage of large amounts of carbon dioxide (CO{sub 2}) in deep geological formations for greenhouse gas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application. In this work 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, and (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 CO{sub 2} 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 CO{sub 2} seepage without need for detailed understanding of natural system processes. Because of the local extrema in CO{sub 2} 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 CO{sub 2} seepage areas.},
doi = {10.1016/j.ijggc.2008.04.008},
journal = {Journal of Greenhouse Gas Control},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {9}
}