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Optimal strategy between extraction and storage of crude oil

Author

Listed:
  • Ilyes Abid
  • Stéphane Goutte

    (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8)

  • Farid Mkaouar
  • Khaled Guesmi

    (IPAG Lab - IPAG Lab - IPAG Business School)

Abstract

This paper deals with the optimal choice between extraction and storage of crude oil over time. An oil producer should decide on the proportion of oil extracted to sell and the proportion to store. This optimal operational strategy should be conducted on a daily basis while taking into consideration physical, operational and financial constraints such as: storage capacity, crude oil spot price, total quantity available for possible extraction or the maximum amount which could be invested at time t for the extraction choice. Our main results show that when the stock is close to be full, it is better to sell a higher part of the extracted quantity. Therefore, if the stock is empty, the best strategy is to secure the reserves against oil prices fluctuations. In the case of full stock, it is useless to put more quantity in reserves and the best strategy is to sell more output. But, if the stock and the available reserves for extraction are half full, the optimal strategy is to consider both selling and storing output.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ilyes Abid & Stéphane Goutte & Farid Mkaouar & Khaled Guesmi, 2018. "Optimal strategy between extraction and storage of crude oil," Post-Print hal-01968085, HAL.
    • Handle: RePEc:hal:journl:hal-01968085
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    Cited by:

    1. Jean-Michel Sahut & Petr Hajek & Vladimir Olej & Lubica Hikkerova, 2025. "The role of news-based sentiment in forecasting crude oil price during the Covid-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 861-884, February.
    2. M’hamed Gaïgi & Stéphane Goutte & Idris Kharroubi & Thomas Lim, 2021. "Optimal risk management problem of natural resources: application to oil drilling," Annals of Operations Research, Springer, vol. 297(1), pages 147-166, February.
    3. Mark Cummins & Fabian Gogolin & Fearghal Kearney & Greg Kiely & Bernard Murphy, 2023. "Practice-relevant model validation: distributional parameter risk analysis in financial model risk management," Annals of Operations Research, Springer, vol. 330(1), pages 431-455, November.
    4. Yu, Yue & Wang, Jianzhou & Jiang, He & Lu, Haiyan, 2025. "How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective," Resources Policy, Elsevier, vol. 100(C).

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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