LAPSE:2023.25838
Published Article
LAPSE:2023.25838
Study on Well Selection Method for Refracturing Horizontal Wells in Tight Reservoirs
Qihong Feng, Jiawei Ren, Xianmin Zhang, Xianjun Wang, Sen Wang, Yurun Li
March 31, 2023
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
decision index, deep learning, horizontal wells, re-fracturing, tight oil, XGBoost regression
Suggested Citation
Feng Q, Ren J, Zhang X, Wang X, Wang S, Li Y. Study on Well Selection Method for Refracturing Horizontal Wells in Tight Reservoirs. (2023). LAPSE:2023.25838
Author Affiliations
Feng Q: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Ren J: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Zhang X: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Wang X: Daqing Oilfield Company Limited Production Technology Institute, Daqing 163000, China
Wang S: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China [ORCID]
Li Y: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Journal Name
Energies
Volume
13
Issue
16
Article Number
E4202
Year
2020
Publication Date
2020-08-14
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13164202, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.25838
This Record
External Link

doi:10.3390/en13164202
Publisher Version
Download
Files
[Download 1v1.pdf] (3.6 MB)
Mar 31, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
51
Version History
[v1] (Original Submission)
Mar 31, 2023
 
Verified by curator on
Mar 31, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.25838
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version