ISSN (print) - 0131-7652

ISSN (online) - 2686-7605

Vol. 55 No. 3 (2025)
ECONOMY SECTORS AND MARKETS

Correlation between the “Learning effect” and Hydrocarbon Recovery Dynamics

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  • V.A. Kryukov,
  • D. Abugsisa,
  • D.I. Dushenin
V.A. Kryukov
Institute of Economics and Industrial Engineering, SB RAS; National Research University Higher School of Economics (Moscow)
D. Abugsisa
National Research University Higher School of Economics
D.I. Dushenin
Institute of Economics and Industrial Engineering, SB RAS
DOI: https://doi.org/10.30680/ECO0131-7652-2025-3-83-96

Published 2025-06-06

Keywords

  • hydrocarbon production; new development objects; new types of hydrocarbon production objects; forecasting; knowledge generation; learning curves; innovation; hard-to-recover oil reserves

How to Cite

1.
Kryukov В, Abugsisa Д, Dushenin Д. Correlation between the “Learning effect” and Hydrocarbon Recovery Dynamics. ECO [Internet]. 2025 Jun. 6 [cited 2025 Jun. 28];55(3):83-96. Available from: https://ecotrends.ru/index.php/eco/article/view/4866

Abstract

The authors summarize the findings of research into the impact of evolving critical innovative and organizational technologies on the economic efficiency of unconventional hydrocarbon sources development. The analysis is based on the example of building “learning curves” for two production companies participating in and organizing the process of developing the Bakken shale hydrocarbon formation in the United States. The possibilities of using machine learning methods to estimate the speed of technology development depending on the degree of participation of the companies involved in this process are also considered. The results confirm the working hypothesis about the possibility of constructing “learning curves” taking into account the influence of poorly controlled factors on the speed of technology development. The next direction of modification of the model of evolution of innovative technologies is related to the substantiation of the importance of creating a favorable environment to stimulate innovative growth and reduce costs in the course of knowledge sharing and, as a consequence, to increase the rate of learning. The results of this research can be used to stimulate development of innovation processes in the oil and gas sector.
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References

  1. Крюков В.А., Горлов А.А. Прогнозирование процессов развития ветровой энергетики в бассейне Северного моря на базе кривых обучения // Проблемы прогнозирования. 2019. № 2. С. 93–103.
  2. Крюков В.А., Миляев Д.В., Душенин Д.И., Савельева А.Д., Скузоватов М.Ю. Генерация новых знаний в ресурсном секторе экономики // Проблемы прогнозирования. 2022. № 3. С. 28–41. DOI: 10.47711/0868–6351–192–28–41
  3. Миляев Д.В., Душенин Д.И., Киданова О.А. Эффект накопления знаний при определении критериев рентабельности сырьевых проектов // Мир экономики и управления. 2018. Т. 18, № 1. С. 54–69. DOI 10.25205/2542–0429–2018–18–1–54–69
  4. Прищепа О.М., Аверьянова О.Ю., Высоцкий В.И., Морариу Д. Формация Баккен: геология, нефтегазоносность и история разработки // Нефтегазовая геология. Теория и практика. 2013. Т. 8, № 2. URL: http://www.ngtp.ru/rub/9/19_2013.pdf (дата обращения: 06.03.2025). DOI: 10.17353/2070–5379/19_2013
  5. Шарф И.В., Борзенкова Д.Н. Трудноизвлекаемые запасы нефти: понятие, классификационные подходы и стимулирование разработки // Фундаментальные исследования. 2015. № 2. С. 3593–3597.
  6. Шафраник Ю.К., Крюков В.А. Нефтегазовые ресурсы России: трудный путь к многообразию. М.: Перо, 2016. 272 с.
  7. Abdulkareem, S.A., Mustafa, Y.T., Augustijn, E.W., Filatova, T. (2019). Bayesian Networks for Spatial Learning: A Workflow on Using Limited Survey Data for Intelligent Learning in Spatial Agent-Based Models. Geoinformatica. Vol. 23 (2). Pp. 243–268.
  8. Ali, J. et al. (2012). Random forests and decision trees. International Journal of Computer Science Issues (IJCSI). Vol. 9 (5). P. 272.
  9. Barbero, J. et al. (2024). Technologically related diversification: One size does not fit all European regions. Research Policy. Vol. 53 (3). P. 104973. DOI: 10.1016/j.respol.2024.104973
  10. Basilico, S., Graf, H. (2023). Bridging technologies in the regional knowledge space: measurement and evolution. Journal of Evolutionary Economics. Vol. 33 (4). Pp. 1085–1124.
  11. Cooke, P., Leydesdorff, L. (2006). Regional Development in the Knowledge-Based Economy: The Construction of Advantage. The Journal of Technology Transfer. Vol. 31 (1). Pp. 5–15.
  12. Lorwongngam, A.O., Cipolla, C., Gradl, C., Gil Cidoncha, J., Davis, B. (2019). Multidisciplinary data gathering to characterize hydraulic fracture performance and evaluate well spacing in the Bakken / SPE Hydraulic Fracturing Technology Conference and Exhibition, Conference Paper. DOI: 10.2118/194321-MS.
  13. Montgomery, C.T., Michael, B. (2010). Hydraulic fracturing: History of an enduring technology. Journal of Petroleum Technology. Vol. 62 (12). Pp. 26–40. DOI: 10.2118/1210–0026-JPT
  14. Statistical Review of World Energy – 2024, 73rd edition. P. 20. Available at: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.connaissancedesenergies.org/sites/connaissancedesenergies.org/files/pdf-actualites/Statistical%20Review%20of%20World%20Energy%202024.pdf (accessed 06.03.2025).
  15. Turrell, A. (2016). Agent-Based Models: Understanding the Economy from the Bottom Up / Quarterly Bulletin, Q4. – Bank of England. Pp. 173–188. Available at: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/ https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/ABMOverview.BankOfEngland.ATurrell2017.pdf (accessed 06.03.2025).
  16. Zhang, J. et al. (2024).The Path Innovation of Regional Economic Growth: Data Analysis Based on China. Journal of the Knowledge Economy. Vol. 15 (4). Pp. 20111–20134. DOI:10.1007/s13132–024–01847–7