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Development of Shale Gas Supply Chain Network under Market Uncertainties

Author

Listed:
  • Jorge Chebeir

    (Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Aryan Geraili

    (Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Jose Romagnoli

    (Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

Abstract

The increasing demand of energy has turned the shale gas and shale oil into one of the most promising sources of energy in the United States. In this article, a model is proposed to address the long-term planning problem of the shale gas supply chain under uncertain conditions. A two-stage stochastic programming model is proposed to describe and optimize the shale gas supply chain network. Inherent uncertainty in final products’ prices, such as natural gas and natural gas liquids (NGL), is treated through the utilization of a scenario-based method. A binomial option pricing model is utilized to approximate the stochastic process through the generation of scenario trees. The aim of the proposed model is to generate an appropriate and realistic supply chain network configuration as well as scheduling of different operations throughout the planning horizon of a shale gas development project.

Suggested Citation

  • Jorge Chebeir & Aryan Geraili & Jose Romagnoli, 2017. "Development of Shale Gas Supply Chain Network under Market Uncertainties," Energies, MDPI, vol. 10(2), pages 1-31, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:246-:d:90782
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    References listed on IDEAS

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    Cited by:

    1. Fadhil Y. Al-Aboosi & Mahmoud M. El-Halwagi, 2019. "A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty," Sustainability, MDPI, vol. 11(18), pages 1-39, September.
    2. Becerra-Fernandez, Mauricio & Cosenz, Federico & Dyner, Isaac, 2020. "Modeling the natural gas supply chain for sustainable growth policy," Energy, Elsevier, vol. 205(C).
    3. Oke, Doris & Mukherjee, Rajib & Sengupta, Debalina & Majozi, Thokozani & El-Halwagi, Mahmoud, 2020. "On the optimization of water-energy nexus in shale gas network under price uncertainties," Energy, Elsevier, vol. 203(C).
    4. Ali Elkamel, 2018. "Energy Production Systems," Energies, MDPI, vol. 11(10), pages 1-4, September.
    5. Shiyu Chen & Wei Wang & Enrico Zio, 2021. "A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains," Energies, MDPI, vol. 14(9), pages 1-27, May.
    6. Xiaoqian Guo & Qiang Yan & Anjian Wang, 2017. "Assessment of Methods for Forecasting Shale Gas Supply in China Based on Economic Considerations," Energies, MDPI, vol. 10(11), pages 1-14, October.
    7. Ahn, Yuchan & Kim, Junghwan & Kwon, Joseph Sang-Il, 2020. "Optimal design of supply chain network with carbon dioxide injection for enhanced shale gas recovery," Applied Energy, Elsevier, vol. 274(C).

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