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Efficiency Increase of Energy Systems in Oil and Gas Industry by Evaluation of Electric Drive Lifecycle

Author

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  • Nikolay Korolev

    (Department of Energy, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

  • Anatoly Kozyaruk

    (Department of Energy, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

  • Valentin Morenov

    (Department of Oil and Gas, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia)

Abstract

The efficiency issue of energy systems in the oil and gas industry is a crucial factor nowadays. Energy share in the production costs of oil and gas can reach 50%. Among the most important energy equipment are the electric drives of the pumps, compressors, auxiliary units, etc. The paper considered the lifecycle of the electric drives used at oil and gas fields and evaluated their efficiency parameters during periods of operational wear or malfunctioning. At the same time, the lifecycle of the energy equipment was seen as its technical state during operation. Based on the spectral analysis of the current using the finite Fourier transform (FFT), the main frequency components were identified that characterize the type and level of malfunction, taking into account defined efficiency indicators of the electric drive. A technique for assessing the technical state of an electric drive was proposed based on the normalized levels of the amplitudes of the frequency components. Predictive management of the electric drive was implemented in terms of correcting the control system when a malfunction occurs, providing the required efficiency indicators. Boundaries for the technical state of the electric drive were determined. Recommendations for further operation, terms of maintenance and repair or preventive management were presented.

Suggested Citation

  • Nikolay Korolev & Anatoly Kozyaruk & Valentin Morenov, 2021. "Efficiency Increase of Energy Systems in Oil and Gas Industry by Evaluation of Electric Drive Lifecycle," Energies, MDPI, vol. 14(19), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6074-:d:641836
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    References listed on IDEAS

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    4. Jia Uddin & Myeongsu Kang & Dinh V. Nguyen & Jong-Myon Kim, 2014. "Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, June.
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    Cited by:

    1. Yuriy Zhukovskiy & Aleksandra Buldysko & Ilia Revin, 2023. "Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current," Energies, MDPI, vol. 16(8), pages 1-23, April.
    2. Yuriy Zhukovskiy & Anastasia Koshenkova & Valeriya Vorobeva & Daniil Rasputin & Roman Pozdnyakov, 2023. "Assessment of the Impact of Technological Development and Scenario Forecasting of the Sustainable Development of the Fuel and Energy Complex," Energies, MDPI, vol. 16(7), pages 1-23, March.

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