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Applications of System Dynamics and Big Data to Oil and Gas Production Dynamics in the Permian Basin

Author

Listed:
  • James R. Burns

    (Texas Tech University, USA)

  • Pinyarat Sirisomboonsuk

    (The University of Texas Permian Basin, USA)

Abstract

In this paper, the authors create, justify, and document a system dynamics model of the oil and gas production within the Permian Basin of Texas. Then the researchers show how to fit the model to historical time series data (big data). The authors use the model to better understand the process structure, the production dynamics, and to explore the deleterious consequences of limited pipeline capacity in the Permian Basin. The model is also employed to better understand how to increase revenues derived from the basin. From this model, numerous suggestions are made as to how to improve the overall revenue and profitability coming from the Permian Basin. The model's ultimate purposes and its associated big data are to foster a basic appreciation of the causality inherent in the ‘system' and how basic model parameters affect and influence measures of model performance.

Suggested Citation

  • James R. Burns & Pinyarat Sirisomboonsuk, 2022. "Applications of System Dynamics and Big Data to Oil and Gas Production Dynamics in the Permian Basin," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(1), pages 1-22, January.
    • Handle: RePEc:igg:jban00:v:9:y:2022:i:1:p:1-22
    as

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    References listed on IDEAS

    as
    1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    2. Kartik Modi & Harshal Lowalekar & N.M.K. Bhatta, 2019. "Revolutionizing supply chain management the theory of constraints way: a case study," International Journal of Production Research, Taylor & Francis Journals, vol. 57(11), pages 3335-3361, June.
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