Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning
| Autor: | Thomas Clay Templeton, Hoonyoung Jeong, Alexander Y. Sun, Ana González-Nicolás |
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| Rok vydání: | 2018 |
| Předmět: |
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Cost estimate business.industry Computer science Impact assessment 0208 environmental biotechnology Cloud computing 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences 020801 environmental engineering Knowledge sharing Metamodeling Workflow Risk analysis (engineering) Web application Computers in Earth Sciences business computer 0105 earth and related environmental sciences Information Systems |
| Zdroj: | Computers & Geosciences. 113:70-80 |
| ISSN: | 0098-3004 |
| DOI: | 10.1016/j.cageo.2018.01.006 |
| Popis: | Carbon capture and storage (CCS) is being evaluated globally as a geoengineering measure for significantly reducing greenhouse emission. However, long-term liability associated with potential leakage from these geologic repositories is perceived as a main barrier of entry to site operators. Risk quantification and impact assessment help CCS operators to screen candidate sites for suitability of CO 2 storage. Leakage risks are highly site dependent, and a quantitative understanding and categorization of these risks can only be made possible through broad participation and deliberation of stakeholders, with the use of site-specific, process-based models as the decision basis. Online decision making, however, requires that scenarios be run in real time. In this work, a Python based, Leakage Assessment and Cost Estimation (PyLACE) web application was developed for quantifying financial risks associated with potential leakage from geologic carbon sequestration sites. PyLACE aims to assist a collaborative, analytic-deliberative decision making processes by automating metamodel creation, knowledge sharing, and online collaboration. In PyLACE, metamodeling, which is a process of developing faster-to-run surrogates of process-level models, is enabled using a special stochastic response surface method and the Gaussian process regression. Both methods allow consideration of model parameter uncertainties and the use of that information to generate confidence intervals on model outputs. Training of the metamodels is delegated to a high performance computing cluster and is orchestrated by a set of asynchronous job scheduling tools for job submission and result retrieval. As a case study, workflow and main features of PyLACE are demonstrated using a multilayer, carbon storage model. |
| Databáze: | OpenAIRE |
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