A Hybrid Optimization Methodology Identifying Optimal Operating Conditions for Carbon Dioxide Injection in Geologic Carbon Sequestration
- 주제(키워드) CO2 injection , coupled wellbore-reservoir model , multi-objective optimization , Pareto-optimal solutions , proxy modeling
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 Elsevier Ltd
- 발행년도 2020
- URI http://www.dcollection.net/handler/ewha/000000168676
- 본문언어 영어
- Published As https://dx.doi.org/10.1016/j.ijggc.2020.103067
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Prior to determining the optimal operating parameters for CO2 injection, conditions for both injection wellbore and storage formation should be evaluated; the build-up pressure induced by the CO2 injection could promote fractures in the storage formation, even collapsing the wellbore. In this study, a hybrid optimization methodology, which combined the proxy modeling and multi-objective optimization, was engaged in searching appropriate operating conditions for CO2 injection. The study utilized a fully coupled wellbore-reservoir (WR) model to simulate the CO2 injection scenarios. Three responses, such as pressure, temperature, and CO2 mass flow rate at the bottom-hole of injection wellbore, were investigated. To reduce the computational cost, the statistical proxy models were developed for approximating three responses. The developed fine-tuned proxy models revealed four influential factors; wellhead pressure, injected CO2 temperature, wellbore diameter, and permeability of a storage formation were significant in predicting three responses. Among these four influential factors, permeability was treated to be an uncertainty factor, while the other three factors were treated as tuning factors. According to acquired optimal solution sets, the optimum values for wellhead pressure and injected CO2 temperature were distributed around 10.0 MPa and 35 °C, respectively. For the wellbore diameter, its mean of optimal solutions was 0.1 m, and more solutions were concentrated at this mean value with a decrease in permeability. © 2020 Elsevier Ltd
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