Systematic modeling and methodological approaches for optimizing gas storage facility design with fluctuating hydraulic characteristics
As the proportion of natural gas consumption in the energy market gradually increases, optimizing the design of gas storage surface system (GSSS) has become a current research focus. Existing studies on the two independent injection pipeline network (InNET) and production pipeline network (ProNET) f...
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| Veröffentlicht in: | Petroleum science Jg. 22; H. 6; S. 2546 - 2569 |
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| Sprache: | Englisch |
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Beijing
Elsevier B.V
01.06.2025
KeAi Publishing Communications Ltd |
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| ISSN: | 1995-8226, 1672-5107, 1995-8226 |
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| Abstract | As the proportion of natural gas consumption in the energy market gradually increases, optimizing the design of gas storage surface system (GSSS) has become a current research focus. Existing studies on the two independent injection pipeline network (InNET) and production pipeline network (ProNET) for underground natural gas storage (UNGS) are scarce, and no optimization methods have been proposed yet. Therefore, this paper focuses on the flow and pressure boundary characteristics of the GSSS. It constructs systematic models, including the injection multi-condition coupled model (INM model), production multi-condition coupled model (PRM model), injection single condition model (INS model) and production single condition model (PRS model) to optimize the design parameters. Additionally, this paper proposes a hybrid genetic algorithm based on generalized reduced gradient (HGA-GRG) for solving the models. The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network. For the GSSS, nine different condition scenarios are considered, and iterative process analysis and sensitivity analysis of these scenarios are conducted. Moreover, simulation scenarios are set up to verify the applicability of different scenarios to the boundaries. The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890 × 104 CNY, and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655 × 104 CNY, demonstrating greater applicability and economy than those considering only one or two types of conditions. The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS. |
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| AbstractList | As the proportion of natural gas consumption in the energy market gradually increases, optimizing the design of gas storage surface system (GSSS) has become a current research focus. Existing studies on the two independent injection pipeline network (InNET) and production pipeline network (ProNET) for underground natural gas storage (UNGS) are scarce, and no optimization methods have been proposed yet. Therefore, this paper focuses on the flow and pressure boundary characteristics of the GSSS. It constructs systematic models, including the injection multi-condition coupled model (INM model), production multi-condition coupled model (PRM model), injection single condition model (INS model) and production single condition model (PRS model) to optimize the design parameters. Additionally, this paper proposes a hybrid genetic algorithm based on generalized reduced gradient (HGA-GRG) for solving the models. The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network. For the GSSS, nine different condition scenarios are considered, and iterative process analysis and sensitivity analysis of these scenarios are conducted. Moreover, simulation scenarios are set up to verify the applicability of different scenarios to the boundaries. The research results show that the cost of the InNET considering the coupled pressure boundary is 64.4890 × 104 CNY, and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655 × 104 CNY, demonstrating greater applicability and economy than those considering only one or two types of conditions. The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS. As the proportion of natural gas consumption in the energy market gradually increases, optimizing the design of gas storage surface system (GSSS) has become a current research focus. Existing studies on the two independent injection pipeline network (InNET) and production pipeline network (ProNET) for underground natural gas storage (UNGS) are scarce, and no optimization methods have been proposed yet. Therefore, this paper focuses on the flow and pressure boundary characteristics of the GSSS. It constructs systematic models, including the injection multi-condition coupled model (INM model), production multi-condition coupled model (PRM model), injection single condition model (INS model) and production single condition model (PRS model) to optimize the design parameters. Additionally, this paper proposes a hybrid genetic algorithm based on generalized reduced gradient (HGA-GRG) for solving the models. The models and algorithm are applied to a case study with the objective of minimizing the cost of the pipeline network. For the GSSS, nine different condition scenarios are considered, and iterative process analysis and sensitivity analysis of these scenarios are conducted. Moreover, simulation scenarios are set up to verify the applicability of different scenarios to the boundaries. The research results show that the cost of the INNET considering the coupled pressure boundary is 64.4890 x 10 CNY, and the cost of the ProNET considering coupled flow and pressure boundaries is 87.7655 x 10% CNY, demonstrating greater applicability and economy than those considering only one or two types of conditions. The algorithms and models proposed in this paper provide an effective means for the design of parameters for GSSS. |
| Author | Liu, Shi-Tao Liang, Guang-Chuan Li, Cheng-Yu Zhou, Jun Zhao, Yun-Xiang Peng, Jing-Hong Ye, Zhan-Peng Li, Zi-Chen |
| Author_xml | – sequence: 1 givenname: Shi-Tao surname: Liu fullname: Liu, Shi-Tao organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China – sequence: 2 givenname: Cheng-Yu surname: Li fullname: Li, Cheng-Yu organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China – sequence: 3 givenname: Jun surname: Zhou fullname: Zhou, Jun email: zhoujunswpu@163.com organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China – sequence: 4 givenname: Zi-Chen surname: Li fullname: Li, Zi-Chen organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China – sequence: 5 givenname: Zhan-Peng surname: Ye fullname: Ye, Zhan-Peng organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China – sequence: 6 givenname: Jing-Hong surname: Peng fullname: Peng, Jing-Hong organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China – sequence: 7 givenname: Yun-Xiang surname: Zhao fullname: Zhao, Yun-Xiang organization: Yunnan Provincial Energy Research Institute Co., Ltd., Kunming, 650000, Yunnan, China – sequence: 8 givenname: Guang-Chuan surname: Liang fullname: Liang, Guang-Chuan email: lgcdjr@163.com organization: Petroleum Engineering School, Southwest Petroleum University, Chengdu, 610500, Sichuan, China |
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| Keywords | Gas storage Hybrid genetic algorithm based on generalized reduced gradient Hydraulic characteristics Pipeline diameter optimization Multi-condition |
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| SubjectTerms | Algorithms Boundaries Design Design optimization Design parameters Energy consumption Gas pipelines Gas storage Genetic algorithms Hybrid genetic algorithm based on generalized reduced gradient Hydraulic characteristics Hydraulics Injection Multi-condition Natural gas Parameters Pipeline diameter optimization Pressure Sensitivity analysis Simulation Storage facilities Underground storage |
| Title | Systematic modeling and methodological approaches for optimizing gas storage facility design with fluctuating hydraulic characteristics |
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