| Title | Assessing leakage detectability at geologic CO2 sequestration sites using the probabilistic collocation method |
| Authors | Sun, Alexander Y. Zeidouni, Mehdi Nicot, Jean-Philippe Lu, Zhiming Zhang, Dongxiao |
| Affiliation | Univ Texas Austin, Bur Econ Geol, Jackson Sch Geosci, Austin, TX 78712 USA. Los Alamos Natl Lab, Los Alamos, NM USA. Peking Univ, Coll Engn, Beijing 100871, Peoples R China. |
| Keywords | Carbon sequestration and storage Leakage detection Probabilistic collocation method Detectability Signal-to-noise ratio Uncertainty quantification HETEROGENEOUS POROUS-MEDIA PARTIAL-DIFFERENTIAL-EQUATIONS SOLUTE FLUX APPROACH DEEP SALINE AQUIFER RANDOM INPUT DATA UNCERTAINTY ANALYSIS POLYNOMIAL CHAOS ABANDONED WELL FLOW TRANSPORT |
| Issue Date | 2013 |
| Publisher | 水资源进展 |
| Citation | ADVANCES IN WATER RESOURCES.2013,56,49-60. |
| 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-Loeve (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. (c) 2012 Elsevier Ltd. All rights reserved. |
| URI | http://hdl.handle.net/20.500.11897/223217 |
| ISSN | 0309-1708 |
| DOI | 10.1016/j.advwatres.2012.11.017 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 工学院 |
