Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions
The surface injection and production system (SIPS) is a critical component for effective injection and production processes in underground natural gas storage. As a vital channel, the rational design of the surface injection and production (SIP) pipeline significantly impacts efficiency. This paper...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Natural Gas Industry B |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352854025000245 |
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| _version_ | 1849315921343021056 |
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| author | Jun Zhou Zichen Li Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang |
| author_facet | Jun Zhou Zichen Li Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang |
| author_sort | Jun Zhou |
| collection | DOAJ |
| description | The surface injection and production system (SIPS) is a critical component for effective injection and production processes in underground natural gas storage. As a vital channel, the rational design of the surface injection and production (SIP) pipeline significantly impacts efficiency. This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects. An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model. This paper proposes a hybrid genetic algorithm generalized reduced gradient (HGA-GRG) method, and compares it with the traditional genetic algorithm (GA) in a practical case study. The HGA-GRG demonstrated significant advantages in optimization outcomes, reducing the initial cost by 345.371 × 104 CNY compared to the GA, validating the effectiveness of the model. By adjusting algorithm parameters, the optimal iterative results of the HGA-GRG were obtained, providing new research insights for the optimal design of a SIPS. |
| format | Article |
| id | doaj-art-c7a195708d6b46c39e62d9d7f41c86a6 |
| institution | Kabale University |
| issn | 2352-8540 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Natural Gas Industry B |
| spelling | doaj-art-c7a195708d6b46c39e62d9d7f41c86a62025-08-20T03:51:59ZengKeAi Communications Co., Ltd.Natural Gas Industry B2352-85402025-04-0112223425010.1016/j.ngib.2025.03.009Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditionsJun Zhou0Zichen Li1Shitao Liu2Chengyu Li3Yunxiang Zhao4Zonghang Zhou5Guangchuan Liang6Petroleum Engineering School, Southwest Petroleum University, Chengdu, China; Corresponding author.Petroleum Engineering School, Southwest Petroleum University, Chengdu, ChinaPetroleum Engineering School, Southwest Petroleum University, Chengdu, ChinaPetroleum Engineering School, Southwest Petroleum University, Chengdu, ChinaYunnan Provincial Energy Research Institute Co., Ltd., Kunming, ChinaPetroleum Engineering School, Southwest Petroleum University, Chengdu, ChinaPetroleum Engineering School, Southwest Petroleum University, Chengdu, China; Corresponding author. Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610000, China.The surface injection and production system (SIPS) is a critical component for effective injection and production processes in underground natural gas storage. As a vital channel, the rational design of the surface injection and production (SIP) pipeline significantly impacts efficiency. This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects. An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model. This paper proposes a hybrid genetic algorithm generalized reduced gradient (HGA-GRG) method, and compares it with the traditional genetic algorithm (GA) in a practical case study. The HGA-GRG demonstrated significant advantages in optimization outcomes, reducing the initial cost by 345.371 × 104 CNY compared to the GA, validating the effectiveness of the model. By adjusting algorithm parameters, the optimal iterative results of the HGA-GRG were obtained, providing new research insights for the optimal design of a SIPS.http://www.sciencedirect.com/science/article/pii/S2352854025000245Underground natural gas storageSurface injection and production pipelineParameter optimizationHybrid genetic algorithm |
| spellingShingle | Jun Zhou Zichen Li Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions Natural Gas Industry B Underground natural gas storage Surface injection and production pipeline Parameter optimization Hybrid genetic algorithm |
| title | Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions |
| title_full | Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions |
| title_fullStr | Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions |
| title_full_unstemmed | Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions |
| title_short | Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions |
| title_sort | hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions |
| topic | Underground natural gas storage Surface injection and production pipeline Parameter optimization Hybrid genetic algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2352854025000245 |
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