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Optimal risk management problem of natural resources: Application to oil drilling

Author

Listed:
  • M’hamed Gaîgi
  • Stéphane Goutte

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

  • Idris Kharroubi

    (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Thomas Lim

    (ENSIIE - Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise)

Abstract

The aim of this paper is to determine the optimal balance between extraction and storage of a natural resource (in particular crude oil) over time under a large array of environmental, operational and financial constraints for an infinite maturity time. We consider a manager that owns an oil field from which he can extract oil and decides to sell or store it. This operational strategy has to be carried out in continuous time and has to satisfy physical, operational, environmental and financial constraints such as storage capacity, crude oil spot price volatility, amount available for possible extraction or maximum amount that could be invested at time t for the extraction choice. The costs of storage and extraction are also taken into account to better fit the real market scenario. We solve the optimization problem of the manager's profit under this large array of constraints and provide an optimal strategy. We then examine different numerical scenarios to check the robustness and the corresponding optimal strategies given by our model, which is obtained by a numerical approach, with respect to different possible events related to the market , environmental policies or ecological constraints.

Suggested Citation

  • M’hamed Gaîgi & Stéphane Goutte & Idris Kharroubi & Thomas Lim, 2019. "Optimal risk management problem of natural resources: Application to oil drilling," Working Papers halshs-01968000, HAL.
    • Handle: RePEc:hal:wpaper:halshs-01968000
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01968000
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    References listed on IDEAS

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    Cited by:

    1. Pierre Bras & Gilles Pag`es, 2022. "Langevin algorithms for Markovian Neural Networks and Deep Stochastic control," Papers 2212.12018, arXiv.org, revised Jan 2023.
    2. Pierre Bras & Gilles Pagès, 2022. "Langevin algorithms for Markovian Neural Networks and Deep Stochastic control," Working Papers hal-03980632, HAL.
    3. Yoshioka, Hidekazu & Yoshioka, Yumi, 2024. "Assessing fluctuations of long-memory environmental variables based on the robustified dynamic Orlicz risk," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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    More about this item

    Keywords

    Environment; Oil Extraction; Ecological; Oil Storage; Drilling; Optimal Strategy;
    All these keywords.

    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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