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Study on Well Selection Method for Refracturing Horizontal Wells in Tight Reservoirs

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  • Qihong Feng

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Jiawei Ren

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Xianmin Zhang

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Xianjun Wang

    (Daqing Oilfield Company Limited Production Technology Institute, Daqing 163000, China)

  • Sen Wang

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Yurun Li

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

Refracturing technology is one of the key technologies to recover the productivity of horizontal wells in tight oil reservoirs, and the selection of best candidate wells from target blocks is the basis of this technology. Based on the refracturing production database, this paper analyzes the direct relationship between geological data, initial fracturing completion data, and dynamic production data, and the stimulation effect of refracturing. Considering the interaction among multiple factors, the factors affecting the stimulation effect of refracturing are classified and integrated, and a comprehensive index including geology, engineering, and production is constructed, making this index meaningful both for physical and engineering properties. The XGBoost decision tree model is established to analyze the weight of influence for the comprehensive index of geology, engineering, and production in predicting the stimulation effect of refracturing. A comprehensive decision index of refracturing well selection is formed by combining the above three for performing a fast selection of horizontal candidate wells for fracturing. Taking a horizontal well test area in Songliao Basin as an example, the target wells of refracturing are selected by this method, and field operation is carried out, and a good stimulation effect is achieved. The results show that the comprehensive decision-making index constructed by this method is reliable and has certain guiding significance for well selection and stimulation potential evaluation of tight oil reservoir.

Suggested Citation

  • Qihong Feng & Jiawei Ren & Xianmin Zhang & Xianjun Wang & Sen Wang & Yurun Li, 2020. "Study on Well Selection Method for Refracturing Horizontal Wells in Tight Reservoirs," Energies, MDPI, vol. 13(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4202-:d:398946
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    References listed on IDEAS

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    1. Zhou Zhou & Shiming Wei & Rong Lu & Xiaopeng Li, 2020. "Numerical Study on the Effects of Imbibition on Gas Production and Shut-In Time Optimization in Woodford Shale Formation," Energies, MDPI, vol. 13(12), pages 1-18, June.
    2. Fanhui Zeng & Xiaozhao Cheng & Jianchun Guo & Liang Tao & Zhangxin Chen, 2017. "Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs," Energies, MDPI, vol. 10(4), pages 1-22, April.
    3. Kiran Nandlal & Ruud Weijermars, 2019. "Impact on Drained Rock Volume (DRV) of Storativity and Enhanced Permeability in Naturally Fractured Reservoirs: Upscaled Field Case from Hydraulic Fracturing Test Site (HFTS), Wolfcamp Formation, Midl," Energies, MDPI, vol. 12(20), pages 1-36, October.
    4. Tengfei Wang & Jiexiang Wang, 2019. "Catalytic Effect of Cobalt Additive on the Low Temperature Oxidation Characteristics of Changqing Tight Oil and Its SARA Fractions," Energies, MDPI, vol. 12(15), pages 1-21, July.
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    Cited by:

    1. Xianmin Zhang & Jiawei Ren & Qihong Feng & Xianjun Wang & Wei Wang, 2021. "Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm," Energies, MDPI, vol. 14(20), pages 1-16, October.

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