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

Qihong Feng, Jiawei Ren, Xianmin Zhang, Xianjun Wang, Sen Wang and Yurun Li
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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

Energies, 2020, vol. 13, issue 16, 1-17

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.

Keywords: tight oil; re-fracturing; horizontal wells; decision index; XGBoost regression; deep 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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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