Author
Listed:
- Zhibin Gu
(Sichuan Changning Natural Gas Development Co., Ltd., Chengdu 610000, China)
- Bingxiao Liu
(Sichuan Changning Natural Gas Development Co., Ltd., Chengdu 610000, China)
- Wang Liu
(Sichuan Changning Natural Gas Development Co., Ltd., Chengdu 610000, China)
- Lei Liu
(Sichuan Changning Natural Gas Development Co., Ltd., Chengdu 610000, China)
- Haiyu Wei
(College of Civil Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing 404120, China)
- Bo Yu
(College of Civil Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing 404120, China)
- Lifei Dong
(College of Civil Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing 404120, China)
- Pinzhi Zhong
(College of Civil Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing 404120, China)
- Hun Lin
(Department of Safety Engineering, Chongqing University of Science & Technology, Chongqing 401331, China)
Abstract
The fracture network of the Y214 block in the Changning area of China is complex, and there are significant differences in the productivity of different shale gas wells. However, traditional machine learning models have problems such as missing key parameters, poor fitting effects and low prediction accuracy, which make it difficult to effectively evaluate the impact of crack network complexity on productivity. Therefore, the Pearson correlation coefficient was used to analyze the correlation between evaluation parameters, such as mineral content, horizontal stress difference, natural fractures and gas production. Combined with the improved particle swarm optimization (IPSO) algorithm and support vector machine (SVM) algorithm, a fracture network index (FNI) model was proposed to effectively evaluate the complexity of fracture networks, and the model was verified by comparing it with the performance evaluation results from the other two traditional models. Finally, the correlation between the fracture network index and the actual average daily gas production of different fracturing sections was calculated and analyzed. The results showed that the density of natural fractures was the key factor in controlling gas production (the Pearson correlation coefficient was 0.39), and the correlation between other factors was weak. In the process of fitting the actual data, the coefficient of determination, R², of the IPSO-SVM-FNI model training set increased by 8% and 24% compared with the two traditional models, and the fitting effect was greatly improved. In the prediction process based on actual data, the R² of the IPSO-SVM-FNI model test set was improved by 22% and 20% compared with the two traditional models, and the prediction accuracy was also significantly improved. The fracture index was concentrated, and its main distribution range was in the range of [0.2, 0.8]. The fracturing section with a higher FNI showed higher average daily gas production, and there was a significant positive correlation between fracture network complexity and gas production. Indeed, the research results provide some ideas and references for the evaluation of fracturing effects in shale reservoirs.
Suggested Citation
Zhibin Gu & Bingxiao Liu & Wang Liu & Lei Liu & Haiyu Wei & Bo Yu & Lifei Dong & Pinzhi Zhong & Hun Lin, 2024.
"Analysis on Correlation Model Between Fracture Network Complexity and Gas-Well Production: A Case in the Y214 Block of Changning, China,"
Energies, MDPI, vol. 17(23), pages 1-15, November.
Handle:
RePEc:gam:jeners:v:17:y:2024:i:23:p:6026-:d:1533483
Download full text from publisher
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6026-:d:1533483. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.