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Patent Analysis on the Development of the Shale Petroleum Industry Based on a Network of Technological Indices

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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

This study investigated the technological developments in the shale petroleum industry by analyzing patent data using a network of technological indices. The technological developments were promoted by the beginning of the shale industry, and after the first five years, it showed a more complex development pattern with the convergence of critical technologies. This paper described progress in the shale petroleum technologies as changes in relatedness networks of technological components. The relatedness represents degree of convergence between technological components, and betweenness centrality of network represents priority of technological components. In the results, the progress of the critical technologies such as directional drilling, increasing permeability, and smart systems, were actively carried out from 2012 to 2016. Especially, unconverged technology of increasing permeability and the converged technology of directional drilling and smart system has been intensively developed. Some technological components of the critical technologies are more significant in the form of converged technology.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6746-:d:465787
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    References listed on IDEAS

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    1. Wei, Yi-Ming & Kang, Jia-Ning & Yu, Bi-Ying & Liao, Hua & Du, Yun-Fei, 2017. "A dynamic forward-citation full path model for technology monitoring: An empirical study from shale gas industry," Applied Energy, Elsevier, vol. 205(C), pages 769-780.
    2. 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.
    3. 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.
    4. van Eck, N.J.P. & Waltman, L., 2009. "How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures," ERIM Report Series Research in Management ERS-2009-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Paredes, Dusan & Komarek, Timothy & Loveridge, Scott, 2015. "Income and employment effects of shale gas extraction windfalls: Evidence from the Marcellus region," Energy Economics, Elsevier, vol. 47(C), pages 112-120.
    6. Jong-Hyun Kim & Yong-Gil Lee, 2017. "Analyzing the Learning Path of US Shale Players by Using the Learning Curve Method," Sustainability, MDPI, vol. 9(12), pages 1-8, December.
    7. Nees Jan van Eck & Ludo Waltman, 2009. "How to normalize cooccurrence data? An analysis of some well‐known similarity measures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1635-1651, August.
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    Cited by:

    1. 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.
    2. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.

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