IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v261y2022ipas0360544222021168.html
   My bibliography  Save this article

Prediction and visualization analysis of drilling energy consumption based on mechanism and data hybrid drive

Author

Listed:
  • Gao, Kangping
  • Xu, Xinxin
  • Jiao, Shengjie

Abstract

To obtain an accurate and reliable energy consumption (EC) prediction model, and to quantify the relationship between drilling power, EC, and energy efficiency. An EC prediction model and multi-angle visualization analysis method driven by mechanism and data are proposed. Firstly, the power and energy models of each stage of the drilling rig are established through detailed power flow theory. Additionally, based on the deviation between the actual EC results and the theoretical mechanism model calculation results, a least squares support vector machine (LSSVM) data compensation model is established, and the LSSVM model parameters are optimized by the improved whale optimization algorithm; after that, multi-angle visualization analysis of energy parameters was performed by drilling power histogram, energy efficiency ring diagram, energy sequence diagram, and energy bubble diagram. Finally, the experiment of curb drilling shows that the prediction error of the hybrid drive model is 2.44%. Compared with the prediction results of the mechanism model and the data-driven model, the average prediction error is reduced by 0.76% and 2.25%, which verifies the high efficiency of the hybrid-driven model. Also, through the multi-angle visualization analysis of energy parameters, the drilling energy saving is 2127.4 kJ, and the energy efficiency is improved by 26.71%.

Suggested Citation

  • Gao, Kangping & Xu, Xinxin & Jiao, Shengjie, 2022. "Prediction and visualization analysis of drilling energy consumption based on mechanism and data hybrid drive," Energy, Elsevier, vol. 261(PA).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pa:s0360544222021168
    DOI: 10.1016/j.energy.2022.125227
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222021168
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125227?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Levent Kutlu, 2020. "Greenhouse Gas Emission Efficiencies of World Countries," IJERPH, MDPI, vol. 17(23), pages 1-11, November.
    2. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    3. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    4. Matthias Jonas & Rostyslav Bun & Zbigniew Nahorski & Gregg Marland & Mykola Gusti & Olha Danylo, 2019. "Quantifying greenhouse gas emissions," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 839-852, August.
    5. Francisco, Abigail & Truong, Hanh & Khosrowpour, Ardalan & Taylor, John E. & Mohammadi, Neda, 2018. "Occupant perceptions of building information model-based energy visualizations in eco-feedback systems," Applied Energy, Elsevier, vol. 221(C), pages 220-228.
    6. Xiaoling Wang & Jatin Nathwani & Chunyou Wu, 2016. "Visualization of International Energy Policy Research," Energies, MDPI, vol. 9(2), pages 1-14, January.
    Full references (including those not matched with items on IDEAS)

    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. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    2. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    3. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    4. K. S. Reddy & S. Aravindhan & Tapas K. Mallick, 2017. "Techno-Economic Investigation of Solar Powered Electric Auto-Rickshaw for a Sustainable Transport System," Energies, MDPI, vol. 10(6), pages 1-15, May.
    5. Stefano De Pinto & Pablo Camocardi & Christoforos Chatzikomis & Aldo Sorniotti & Francesco Bottiglione & Giacomo Mantriota & Pietro Perlo, 2020. "On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems," Energies, MDPI, vol. 13(13), pages 1-24, June.
    6. Levent Kutlu & Ran Wang, 2021. "Greenhouse Gas Emission Inefficiency Spillover Effects in European Countries," IJERPH, MDPI, vol. 18(9), pages 1-14, April.
    7. Yi-Ming Wei & Jin-Wei Wang & Tianqi Chen & Bi-Ying Yu & Hua Liao, 2018. "Frontiers of Low-Carbon Technologies: Results from Bibliographic Coupling with Sliding Window," CEEP-BIT Working Papers 116, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    8. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    9. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    10. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    11. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    12. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    13. Evgeny Lisin & Wadim Strielkowski & Veronika Chernova & Alena Fomina, 2018. "Assessment of the Territorial Energy Security in the Context of Energy Systems Integration," Energies, MDPI, vol. 11(12), pages 1-14, November.
    14. Soulios, V. & Loonen, R.C.G.M. & Metavitsiadis, V. & Hensen, J.L.M., 2018. "Computational performance analysis of overheating mitigation measures in parked vehicles," Applied Energy, Elsevier, vol. 231(C), pages 635-644.
    15. Li, Hai & Zheng, Peng & Zhang, Tingsheng & Zou, Yingquan & Pan, Yajia & Zhang, Zutao & Azam, Ali, 2021. "A high-efficiency energy regenerative shock absorber for powering auxiliary devices of new energy driverless buses," Applied Energy, Elsevier, vol. 295(C).
    16. Ramon Sala-Garrido & Manuel Mocholi-Arce & Maria Molinos-Senante & Michail Smyrnakis & Alexandros Maziotis, 2021. "Eco-Efficiency of the English and Welsh Water Companies: A Cross Performance Assessment," IJERPH, MDPI, vol. 18(6), pages 1-17, March.
    17. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    18. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    19. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    20. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.

    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:eee:energy:v:261:y:2022:i:pa:s0360544222021168. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.