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Prediction of Refracturing Timing of Horizontal Wells in Tight Oil Reservoirs Based on an Integrated Learning Algorithm

Author

Listed:
  • Xianmin Zhang

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

  • Jiawei Ren

    (Oil and Gas Technology Research Institute Petro China Changqing Oilfield Company, Xi’an 710018, China)

  • Qihong Feng

    (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)

  • Wei Wang

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

Abstract

Refracturing technology can effectively improve the EUR of horizontal wells in tight reservoirs, and the determination of refracturing time is the key to ensuring the effects of refracturing measures. In view of different types of tight oil reservoirs in the Songliao Basin, a library of 1896 sets of learning samples, with 11 geological and engineering parameters and corresponding refracturing times as characteristic variables, was constructed by combining numerical simulation with field statistics. After a performance comparison and analysis of an artificial neural network, support vector machine and XGBoost algorithm, the support vector machine and XGBoost algorithm were chosen as the base model and fused by the stacking method of integrated learning. Then, a prediction method of refracturing timing of tight oil horizontal wells was established on the basis of an ensemble learning algorithm. Through the prediction and analysis of the refracturing timing corresponding to 257 groups of test data, the prediction results were in good agreement with the real value, and the correlation coefficient R 2 was 0.945. The established prediction method can quickly and accurately predict the refracturing time, and effectively guide refracturing practices in the tight oil test area of the Songliao basin.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6524-:d:653845
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    References listed on IDEAS

    as
    1. 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.
    2. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
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    Cited by:

    1. Jianchao Cai & Reza Rezaee & Victor Calo, 2022. "Recent Advances in Multiscale Petrophysics Characterization and Multiphase Flow in Unconventional Reservoirs," Energies, MDPI, vol. 15(8), pages 1-2, April.

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