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Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part I: Bakken shale play case study

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  • Tan, Siah Hong
  • Barton, Paul I.

Abstract

Associated or stranded natural gas presents a challenge to monetize due to its low volume and lack of supporting infrastructure. Recent proposals for deploying mobile, modular plants, such as those which perform GTL (gas-to-liquids) conversion or produce LNG (liquefied natural gas) on a small scale, have been identified as possible attractive routes to gas monetization. However, such technologies are yet unproven in the marketplace. To assess their potential, we propose a multi-period optimization framework which determines the optimal dynamic allocation and operating decisions for a decision maker who utilizes mobile plants to monetize associated or stranded gas. We then apply this framework to a case study of the Bakken shale play. Our framework is implemented to determine the optimal NPV (net present value) which would be realized over a twenty-year time frame. Sensitivity studies on the technology costs and conversion inputs conclude that the profitability and viability of mobile technologies remain valid for a wide range of possible inputs.

Suggested Citation

  • Tan, Siah Hong & Barton, Paul I., 2015. "Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part I: Bakken shale play case study," Energy, Elsevier, vol. 93(P2), pages 1581-1594.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1581-1594
    DOI: 10.1016/j.energy.2015.10.043
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    Cited by:

    1. Allman, Andrew & Zhang, Qi, 2020. "Dynamic location of modular manufacturing facilities with relocation of individual modules," European Journal of Operational Research, Elsevier, vol. 286(2), pages 494-507.
    2. Tan, Siah Hong & Barton, Paul I., 2016. "Optimal dynamic allocation of mobile plants to monetize associated or stranded natural gas, part II: Dealing with uncertainty," Energy, Elsevier, vol. 96(C), pages 461-467.
    3. Kim, Juwon & Seo, Youngkyun & Chang, Daejun, 2016. "Economic evaluation of a new small-scale LNG supply chain using liquid nitrogen for natural-gas liquefaction," Applied Energy, Elsevier, vol. 182(C), pages 154-163.
    4. Eduardo Alarcon-Gerbier & Zarina Chokparova & Nassim Ghondaghsaz & Wanqi Zhao & Hani Shahmoradi-Moghadam & Uwe Aßmann & Orçun Oruç, 2022. "Software-Defined Mobile Supply Chains: Rebalancing Resilience and Efficiency in Production Systems," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    5. Crow, Daniel J.G. & Giarola, Sara & Hawkes, Adam D., 2018. "A dynamic model of global natural gas supply," Applied Energy, Elsevier, vol. 218(C), pages 452-469.
    6. Tan, Siah Hong & Barton, Paul I., 2017. "Optimal shale oil and gas investments in the United States," Energy, Elsevier, vol. 141(C), pages 398-422.
    7. 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).
    8. 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).
    9. 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).

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