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Learning Curve, Change in Industrial Environment, and Dynamics of Production Activities in Unconventional Energy Resources

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  • Jong-Hyun Kim

    (Department of Energy Resources Engineering, Inha University, 100 Inharo, Nam-gu, Incheon 22212, Korea)

  • Yong-Gil Lee

    (Department of Energy Resources Engineering, Inha University, 100 Inharo, Nam-gu, Incheon 22212, Korea)

Abstract

Since 2007, shale oil and gas production in the United States has become a significant portion of the global fossil fuel market. The main cause for the increase in production of shale oil and gas in the US is the adoption of new production technologies, namely, horizontal drilling and hydraulic fracturing. However, the production cost of shale oil and gas in the US is comparably higher than the production cost of conventional oil and gas. In 2014, the crude oil and natural gas price decreased significantly to approximately 40 dollars per barrel, and natural gas prices decreased to 3 dollars per million British thermal unit, and thus the productivity and financial conditions for the exploration and production of shale oil and natural gas for producers in the United States have worsened critically. Therefore, technological innovation has become one of the most interesting issues of the energy industry. The present study analyzes the trends in technological innovation having a relationship with production activities. This study calculates the learning rate of 30 companies from the petroleum exploration and production industry in the United States using an improved learning rate calculation formula that reflects the changes in the oil production ratio. Thus, more statistically confident calculation results and interpretations of strategic production activities with regard to changes in the industrial environment were achieved in this study.

Suggested Citation

  • Jong-Hyun Kim & Yong-Gil Lee, 2018. "Learning Curve, Change in Industrial Environment, and Dynamics of Production Activities in Unconventional Energy Resources," Sustainability, MDPI, vol. 10(9), pages 1-11, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3322-:d:170419
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    References listed on IDEAS

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    Cited by:

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    2. Wei, Yi-Ming & Qiao, Lu & Lv, Xin, 2020. "The impact of mergers and acquisitions on technology learning in the petroleum industry," Energy Economics, Elsevier, vol. 88(C).
    3. Jong-Hyun Kim & Yong-Gil Lee, 2021. "Factors of Collaboration Affecting the Performance of Alternative Energy Patents in South Korea from 2010 to 2017," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    4. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    5. Yang, Lin & Lv, Haodong & Wei, Ning & Li, Yiming & Zhang, Xian, 2023. "Dynamic optimization of carbon capture technology deployment targeting carbon neutrality, cost efficiency and water stress: Evidence from China's electric power sector," Energy Economics, Elsevier, vol. 125(C).
    6. Jong-Hyun Kim & Yong-Gil Lee, 2020. "Patent Analysis on the Development of the Shale Petroleum Industry Based on a Network of Technological Indices," Energies, MDPI, vol. 13(24), pages 1-15, December.
    7. Jong-Hyun Kim & Yong-Gil Lee, 2020. "Progress of Technological Innovation of the United States’ Shale Petroleum Industry Based on Patent Data Association Rules," Sustainability, MDPI, vol. 12(16), pages 1-17, August.

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