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Powering Multiple Gas Condensate Wells in Russia’s Arctic: Power Supply Systems Based on Renewable Energy Sources

Author

Listed:
  • Gennadiy Stroykov

    (Organization and Management Department, Saint-Petersburg Mining University, 21 Line, 2, 199106 St. Petersburg, Russia)

  • Alexey Y. Cherepovitsyn

    (Organization and Management Department, Saint-Petersburg Mining University, 21 Line, 2, 199106 St. Petersburg, Russia)

  • Elizaveta A. Iamshchikova

    (Petroleum Economics and Management, IFP School, 232 Avenue Napoléon Bonaparte, 92852 Rueil-Malmaison, France)

Abstract

Using renewable energy off-grid power supply and choosing the right equipment that meets the operating conditions in the Arctic can provide companies with reliable power sources for producing gas at facilities located in remote areas and will reduce capital and operating costs associated with the construction of power transmission lines. For more than 15 years, a remote control system powered by renewable energy has been used in parallel with power transmission lines by Gazprom to operate its multiwell pads in Russia’s Far North, which validates the relevance of this study. The subject of the study is a group of gas condensate wells that consists of four multiwell pads operated by Wintershall Russland GmbH. The article discusses a stand-alone renewable-based power system as an option for powering remote oil and gas production facilities. The procedures used in the study include calculating such parameters as power output and power consumption, choosing equipment, describing the design features of a power supply system for a multiwell pad, conducting an economic assessment of the project, comparing different power supply options, analyzing project risks, and developing measures to mitigate these risks.

Suggested Citation

  • Gennadiy Stroykov & Alexey Y. Cherepovitsyn & Elizaveta A. Iamshchikova, 2020. "Powering Multiple Gas Condensate Wells in Russia’s Arctic: Power Supply Systems Based on Renewable Energy Sources," Resources, MDPI, vol. 9(11), pages 1-15, November.
  • Handle: RePEc:gam:jresou:v:9:y:2020:i:11:p:130-:d:440659
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    References listed on IDEAS

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    1. Escalante Soberanis, M.A. & Mithrush, T. & Bassam, A. & Mérida, W., 2018. "A sensitivity analysis to determine technical and economic feasibility of energy storage systems implementation: A flow battery case study," Renewable Energy, Elsevier, vol. 115(C), pages 547-557.
    2. M. Kruk & A. Semenov & A. Cherepovitsyn & A. Nikulina, 2018. "Environmental and Economic Damage from the Development of Oil and Gas Fields in the Arctic Shelf of the Russian Federation," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 423-433.
    3. Elvir Akhmetshin & Sergey Zhiltsov & Anna Dmitrieva & Andrei Plotnikov & Angelina Kolomeytseva, 2019. "The Formation of the Contemporary Renewable Energy Sector and its Role in the Industry Development," International Journal of Energy Economics and Policy, Econjournals, vol. 9(6), pages 373-378.
    4. Mikhail Kozhevnikov & Lazar Gitelman & Elena Magaril & Romen Magaril & Alexandra Aristova, 2017. "Risk Reduction Methods for Managing the Development of Regional Electric Power Industry," Sustainability, MDPI, vol. 9(12), pages 1-14, November.
    5. Abbassi, Abdelkader & Abbassi, Rabeh & Heidari, Ali Asghar & Oliva, Diego & Chen, Huiling & Habib, Arslan & Jemli, Mohamed & Wang, Mingjing, 2020. "Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach," Energy, Elsevier, vol. 198(C).
    6. Natalia Romasheva & Alina Ilinova, 2019. "CCS Projects: How Regulatory Framework Influences Their Deployment," Resources, MDPI, vol. 8(4), pages 1-19, December.
    7. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    8. Hongli Zhang & Lei Shen & Shuai Zhong & Ayman Elshkaki, 2020. "Economic Structure Transformation and Low-Carbon Development in Energy-Rich Cities: The Case of the Contiguous Area of Shanxi and Shaanxi Provinces, and Inner Mongolia Autonomous Region of China," Sustainability, MDPI, vol. 12(5), pages 1-14, March.
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    Cited by:

    1. Oksana Marinina & Anna Nechitailo & Gennady Stroykov & Anna Tsvetkova & Ekaterina Reshneva & Liudmila Turovskaya, 2023. "Technical and Economic Assessment of Energy Efficiency of Electrification of Hydrocarbon Production Facilities in Underdeveloped Areas," Sustainability, MDPI, vol. 15(12), pages 1-25, June.
    2. Natalya Romasheva & Alina Cherepovitsyna, 2023. "Renewable Energy Sources in Decarbonization: The Case of Foreign and Russian Oil and Gas Companies," Sustainability, MDPI, vol. 15(9), pages 1-26, April.

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