Assessing leakage detectability at geologic CO2 sequestration sites using the probabilistic collocation method |
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| Institution: | 1. Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, TX, USA;2. EES-16, Los Alamos National Laboratory, Los Alamos, NM, USA;3. College of Engineering, Peking University, Beijing, China;1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;2. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China;3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China;4. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China;5. Institute of Digital Media, Peking University, Beijing, China;1. Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China;2. School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China;3. Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China;4. State Key Lab of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;1. Mechanical Engineering, University of Cincinnati, P.O. Box 210072, Cincinnati, OH 45221-0072, USA;2. Institute for Computational Mechanics and Its Applications, Northwestern Polytechnical University, Xi''an, Shaanxi 710072, PR China;1. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China;2. Shenzhen Key Laboratory of 3rd Generation Semiconductor Devices, Shenzhen 518055, China;3. Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, China;4. State Key Lab. of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;1. Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, China;2. South University of Science and Technology of China, No.1088, Xueyuan Blvd, Shenzhen, 518055, China;3. Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China |
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| Abstract: | We present an efficient methodology for assessing leakage detectability at geologic carbon sequestration sites under parameter uncertainty. Uncertainty quantification (UQ) and risk assessment are integral and, in many countries, mandatory components of geologic carbon sequestration projects. A primary goal of risk assessment is to evaluate leakage potential from anthropogenic and natural features, which constitute one of the greatest threats to the integrity of carbon sequestration repositories. The backbone of our detectability assessment framework is the probability collocation method (PCM), an efficient, nonintrusive, uncertainty-quantification technique that can enable large-scale stochastic simulations that are based on results from only a small number of forward-model runs. The metric for detectability is expressed through an extended signal-to-noise ratio (SNR), which incorporates epistemic uncertainty associated with both reservoir and aquifer parameters. The spatially heterogeneous aquifer hydraulic conductivity is parameterized using Karhunen–Loève (KL) expansion. Our methodology is demonstrated numerically for generating probability maps of pressure anomalies and for calculating SNRs. Results indicate that the likelihood of detecting anomalies depends on the level of uncertainty and location of monitoring wells. A monitoring well located close to leaky locations may not always yield the strongest signal of leakage when the level of uncertainty is high. Therefore, our results highlight the need for closed-loop site characterization, monitoring network design, and leakage source detection. |
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