IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3243-d375098.html
   My bibliography  Save this article

Systematic Investigation of Integrating Small Wind Turbines into Power Supply for Hydrocarbon Production

Author

Listed:
  • Zi Lin

    (Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK)

  • Xiaolei Liu

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Ziming Feng

    (School of Mechanical Science and Engineering, Northeast Petroleum University, Daqing 163318, Heilongjiang, China)

Abstract

In this paper, the technical and economic feasibility of integrating SWTs (Small Wind Turbines) into remote oil production sites are investigated. Compared to large turbines in onshore and offshore wind farms, SWTs are more suitable for individual power generations. A comprehensive approach based on wind energy assessment, wind power prediction, and economic analysis is then recommended, to evaluate how, where, and when small wind production recovery is achievable in oilfields. Firstly, wind resource in oilfields is critically assessed based on recorded meteorological data. Then, the wind power potential is numerically tested using specified wind turbines with density-corrected power curves. Later, estimations of annual costs and energy-saving are carried out before and after the installation of SWT via the LCOE (Levelized Cost of Electricity) and the EROI (Energy Return on Investment). The proposed methodology was tested against the Daqing oilfield, which is the largest onshore oilfield in China. The results suggested that over 80% of the original annual costs in oil production could be saved through the integrations between wind energy and oil production.

Suggested Citation

  • Zi Lin & Xiaolei Liu & Ziming Feng, 2020. "Systematic Investigation of Integrating Small Wind Turbines into Power Supply for Hydrocarbon Production," Energies, MDPI, vol. 13(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3243-:d:375098
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3243/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3243/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tummala, Abhishiktha & Velamati, Ratna Kishore & Sinha, Dipankur Kumar & Indraja, V. & Krishna, V. Hari, 2016. "A review on small scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1351-1371.
    2. Liu, Xiaolei & Falcone, Gioia & Alimonti, Claudio, 2018. "A systematic study of harnessing low-temperature geothermal energy from oil and gas reservoirs," Energy, Elsevier, vol. 142(C), pages 346-355.
    3. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    4. Yan Hu & Lianyong Feng & Charles C.S. Hall & Dong Tian, 2011. "Analysis of the Energy Return on Investment (EROI) of the Huge Daqing Oil Field in China," Sustainability, MDPI, vol. 3(12), pages 1-16, November.
    5. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    6. Kubiszewski, Ida & Cleveland, Cutler J. & Endres, Peter K., 2010. "Meta-analysis of net energy return for wind power systems," Renewable Energy, Elsevier, vol. 35(1), pages 218-225.
    7. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    8. Bortolini, Marco & Gamberi, Mauro & Graziani, Alessandro & Manzini, Riccardo & Pilati, Francesco, 2014. "Performance and viability analysis of small wind turbines in the European Union," Renewable Energy, Elsevier, vol. 62(C), pages 629-639.
    9. Lee Kwan, Calvin, 2010. "The Inner Mongolia Autonomous Region: A major role in China's renewable energy future," Utilities Policy, Elsevier, vol. 18(1), pages 46-52, March.
    10. Pierobon, L. & Benato, A. & Scolari, E. & Haglind, F. & Stoppato, A., 2014. "Waste heat recovery technologies for offshore platforms," Applied Energy, Elsevier, vol. 136(C), pages 228-241.
    11. Zi Lin & Xiaolei Liu, 2020. "Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning," Energies, MDPI, vol. 13(9), pages 1-21, May.
    12. Bo Xu & Lianyong Feng & William X. Wei & Yan Hu & Jianliang Wang, 2014. "A Preliminary Forecast of the Production Status of China’s Daqing Oil field from the Perspective of EROI," Sustainability, MDPI, vol. 6(11), pages 1-21, November.
    13. Tang, Xu & Zhang, Baosheng & Höök, Mikael & Feng, Lianyong, 2010. "Forecast of oil reserves and production in Daqing oilfield of China," Energy, Elsevier, vol. 35(7), pages 3097-3102.
    14. Artur Bugała & Olga Roszyk, 2020. "Investigation of an Innovative Rotor Modification for a Small-Scale Horizontal Axis Wind Turbine," Energies, MDPI, vol. 13(10), pages 1-18, May.
    15. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Xiaolei & Lin, Zi, 2021. "Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory," Energy, Elsevier, vol. 227(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    2. Lin, Zi & Liu, Xiaolei & Lao, Liyun & Liu, Hengxu, 2020. "Prediction of two-phase flow patterns in upward inclined pipes via deep learning," Energy, Elsevier, vol. 210(C).
    3. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    4. Bai, Yajie & Hou, Jian & Liu, Yongge & Zhao, Dong & Bing, Shaoxian & Xiao, Wu & Zhao, Wei, 2022. "Energy-consumption calculation and optimization method of integrated system of injection-reservoir-production in high water-cut reservoir," Energy, Elsevier, vol. 239(PA).
    5. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    6. Zhaoyang Kong & Xiucheng Dong & Bo Xu & Rui Li & Qiang Yin & Cuifang Song, 2015. "EROI Analysis for Direct Coal Liquefaction without and with CCS: The Case of the Shenhua DCL Project in China," Energies, MDPI, vol. 8(2), pages 1-22, January.
    7. Rabbani, R. & Zeeshan, M., 2020. "Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan," Renewable Energy, Elsevier, vol. 154(C), pages 1240-1251.
    8. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    9. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    10. Fizaine, Florian & Court, Victor, 2015. "Renewable electricity producing technologies and metal depletion: A sensitivity analysis using the EROI," Ecological Economics, Elsevier, vol. 110(C), pages 106-118.
    11. Victor Court & Florian Fizaine, 2014. "Energy transition towards renewables and metal depletion: an approach through the EROI concept," Post-Print hal-01411803, HAL.
    12. Salehi, Mohammad & Khajehpour, Hossein & Saboohi, Yadollah, 2020. "Extended Energy Return on Investment of multiproduct energy systems," Energy, Elsevier, vol. 192(C).
    13. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    14. Bo Xu & Lianyong Feng & William X. Wei & Yan Hu & Jianliang Wang, 2014. "A Preliminary Forecast of the Production Status of China’s Daqing Oil field from the Perspective of EROI," Sustainability, MDPI, vol. 6(11), pages 1-21, November.
    15. Xiaohan Huang & Aihua Jiang, 2022. "Wind Power Generation Forecast Based on Multi-Step Informer Network," Energies, MDPI, vol. 15(18), pages 1-17, September.
    16. Hall, Charles A.S. & Lambert, Jessica G. & Balogh, Stephen B., 2014. "EROI of different fuels and the implications for society," Energy Policy, Elsevier, vol. 64(C), pages 141-152.
    17. Chen, Yingchao & Feng, Lianyong & Wang, Jianliang & Höök, Mikael, 2017. "Emergy-based energy return on investment method for evaluating energy exploitation," Energy, Elsevier, vol. 128(C), pages 540-549.
    18. Teschner, Na'ama & Alterman, Rachelle, 2018. "Preparing the ground: Regulatory challenges in siting small-scale wind turbines in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1660-1668.
    19. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).
    20. Jingxuan Feng & Lianyong Feng & Jianliang Wang, 2018. "Analysis of Point-of-Use Energy Return on Investment and Net Energy Yields from China’s Conventional Fossil Fuels," Energies, MDPI, vol. 11(2), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3243-:d:375098. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.