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

Xianmin Zhang, Jiawei Ren, Qihong Feng, Xianjun Wang and Wei Wang
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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

Energies, 2021, vol. 14, issue 20, 1-16

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.

Keywords: tight oil; refracturing timing; SVR regression; XGBoost regression; ensemble learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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