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Optimal planning and modular infrastructure dynamic allocation for shale gas production

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  • Hong, Bingyuan
  • Li, Xiaoping
  • Song, Shangfei
  • Chen, Shilin
  • Zhao, Changlong
  • Gong, Jing

Abstract

Compared with the conventional method utilizing permanent processing facilities, modular infrastructure has shown greater potential to hedge against the uncertainty due to the rapidly declining characteristic of shale gas production. However, there is still a lack of a comprehensive approach that integrates the production planning and the dynamic allocation of the modular infrastructure based on the production curve of shale gas wells. Therefore, a systematic optimization framework is developed to simultaneously optimize the production planning and modular infrastructure allocation over a given time horizon, maximizing the net present value (NPV) of the system, considering the production curve and gas well status, the processing capacity as well as the scheduling of modular infrastructure. The proposed mixed-integer linear programming model combines the decisions relevant to shale gas production, transportation, and processing together with the decisions regarding modular infrastructure, including allocation, capacity selection, installment planning, moving scheduling, and salvage operation. A case study deciding a development strategy for 24 multi-well pads over a period of 15 years is implemented to illustrate the applicability of the proposed model. The results show that the dynamic allocation of the modular infrastructure can adapt to productivity fluctuations. The use of the modular approach increases the NPV by 9.12% and has a high utilization efficiency of processing devices compared to the conventional method. This work reveals that the synergistic interaction of the production planning and modular infrastructure dynamic allocation can increase efficiencies in the uses of energy, resources, and human capital to promote cleaner production practices.

Suggested Citation

  • Hong, Bingyuan & Li, Xiaoping & Song, Shangfei & Chen, Shilin & Zhao, Changlong & Gong, Jing, 2020. "Optimal planning and modular infrastructure dynamic allocation for shale gas production," Applied Energy, Elsevier, vol. 261(C).
  • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919321270
    DOI: 10.1016/j.apenergy.2019.114439
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    5. Bingyuan Hong & Xiaoping Li & Yanbo Li & Yu Li & Yafeng Yu & Yumo Wang & Jing Gong & Dihui Ai, 2021. "Numerical Simulation of Elbow Erosion in Shale Gas Fields under Gas-Solid Two-Phase Flow," Energies, MDPI, vol. 14(13), pages 1-15, June.
    6. Hong, Bingyuan & Qiao, Dan & Li, Yichen & Sun, Xiaoqing & Yang, Baolong & Li, Li & Gong, Jing & Wen, Kai, 2023. "Supply-demand balance of natural gas pipeline network integrating hydraulic and thermal characteristics, energy conservation and carbon reduction," Energy, Elsevier, vol. 283(C).
    7. Bilel Jarraya & Hatem Afi & Anis Omri, 2023. "Analyzing the Profitability and Efficiency in European Non-Life Insurance Industry," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-25, June.
    8. Noor Yusuf & Tareq Al-Ansari, 2023. "Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models," Energies, MDPI, vol. 16(22), pages 1-33, November.
    9. Andres Soage & Ruben Juanes & Ignasi Colominas & Luis Cueto-Felgueroso, 2024. "Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis," Energies, MDPI, vol. 17(4), pages 1-25, February.
    10. Fan, Di & Gong, Jing & Zhang, Shengnan & Shi, Guoyun & Kang, Qi & Xiao, Yaqi & Wu, Changchun, 2021. "A transient composition tracking method for natural gas pipe networks," Energy, Elsevier, vol. 215(PA).
    11. Hong, Bingyuan & Shao, Bowen & Guo, Jian & Fu, Jianzhong & Li, Cuicui & Zhu, Baikang, 2023. "Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines," Applied Energy, Elsevier, vol. 333(C).
    12. Wen, Kai & Qiao, Dan & Nie, Chaofei & Lu, Yangfan & Wen, Feng & Zhang, Jing & Miao, Qing & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Multi-period supply and demand balance of large-scale and complex natural gas pipeline network: Economy and environment," Energy, Elsevier, vol. 264(C).
    13. Hong, Bingyuan & Du, Zhaonan & Qiao, Dan & Liu, Daiwei & Li, Yu & Sun, Xiaoqing & Gong, Jing & Zhang, Hongyu & Li, Xiaoping, 2024. "Sustainable supply chain of distributed multi-product gas fields based on skid-mounted equipment to dynamically respond to upstream and market fluctuations," Energy, Elsevier, vol. 292(C).
    14. Bingyuan Hong & Yanbo Li & Xiaoping Li & Shuaipeng Ji & Yafeng Yu & Di Fan & Yating Qian & Jian Guo & Jing Gong, 2021. "Numerical Simulation of Gas-Solid Two-Phase Erosion for Elbow and Tee Pipe in Gas Field," Energies, MDPI, vol. 14(20), pages 1-18, October.
    15. Hong, Bingyuan & Cui, Xuemeng & Wang, Bohong & Fan, Di & Li, Xiaoping & Gong, Jing, 2022. "Long-term dynamic allocation and maintenance planning of modular equipment to enhance gas field production flexibility," Energy, Elsevier, vol. 252(C).
    16. Wen, Kai & Lu, Yangfan & Lu, Meitong & Zhang, Wenwei & Zhu, Ming & Qiao, Dan & Meng, Fanpeng & Zhang, Jing & Gong, Jing & Hong, Bingyuan, 2022. "Multi-period optimal infrastructure planning of natural gas pipeline network system integrating flowrate allocation," Energy, Elsevier, vol. 257(C).
    17. Ye, Jihong & Jiang, Wei & Yang, Xinxiang & Hong, Bingyuan, 2023. "Emergency materials response framework for petrochemical enterprises based on multi-objective optimization," Energy, Elsevier, vol. 269(C).

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