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Knowledge Generation in Raw Material Industries

Author

Listed:
  • V. A. Kryukov

    (Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences)

  • D. V. Milyaev

    (Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences)

  • D. I. Dushenin

    (Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences)

  • A. D. Savel’eva

    (Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences)

  • M. Yu. Skuzovatov

    (Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences)

Abstract

— The article presents a mathematical toolkit for forecasting the development of innovative technologies that can be used for cost-effective development of hard-to-recover hydrocarbon reserves. The proposed approach is a symbiosis of agent-based models, Bayesian networks, learning curves, statistical analysis, and numerical simulation modeling methods. These techniques have not been used as a set in research related to the oil and gas industry ever before. The practical significance of the modeling toolkit is based on the prioritization of measures aimed at innovative growth in the oil and gas sector. To test the approach, calculations are performed for three objects: complex structures of the Predpatomsky trough in eastern Siberia and the Republic of Sakha (Yakutia), deposits of the Pre-Jurassic complex in the south-east of western Siberia, and natural bitumen in the Republic of Tatarstan. The calculations confirm the working hypothesis about the practicability of forming technological clusters for efficient extraction of hard-to-recover hydrocarbons.

Suggested Citation

  • V. A. Kryukov & D. V. Milyaev & D. I. Dushenin & A. D. Savel’eva & M. Yu. Skuzovatov, 2022. "Knowledge Generation in Raw Material Industries," Studies on Russian Economic Development, Springer, vol. 33(3), pages 257-266, June.
    • Handle: RePEc:spr:sorede:v:33:y:2022:i:3:d:10.1134_s1075700722030078
    DOI: 10.1134/S1075700722030078
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    References listed on IDEAS

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    1. Turrell, Arthur, 2016. "Agent-based models: understanding the economy from the bottom up," Bank of England Quarterly Bulletin, Bank of England, vol. 56(4), pages 173-188.
    2. V. A. Kryukov & A. A. Gorlov, 2019. "Forecasting the Development Process of Wind Energy in the North Sea Basin Based on Learning Curves," Studies on Russian Economic Development, Springer, vol. 30(2), pages 177-184, March.
    3. Philip Cooke & Loet Leydesdorff, 2006. "Regional Development in the Knowledge-Based Economy: The Construction of Advantage," The Journal of Technology Transfer, Springer, vol. 31(1), pages 5-15, January.
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