Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction
Horizontal well production prediction is crucial for the efficient development of tight reservoirs. However, owing to the complexity of the parameters affecting horizontal well production, improving the prediction accuracy has always been the unremitting goal pursued by the oil and gas industry. Lim...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10833609/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590340928831488 |
|---|---|
| author | Chao Wang Ruogu Wang Yuhan Lin Jiafei Zhang Xiaofei Xie Zidan Zhao Yunlin Xu |
| author_facet | Chao Wang Ruogu Wang Yuhan Lin Jiafei Zhang Xiaofei Xie Zidan Zhao Yunlin Xu |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | Horizontal well production prediction is crucial for the efficient development of tight reservoirs. However, owing to the complexity of the parameters affecting horizontal well production, improving the prediction accuracy has always been the unremitting goal pursued by the oil and gas industry. Limited by the number of parameters, the traditional linear fitting method has low computational efficiency and a large error, which brings difficulties to horizontal well production prediction. In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. At the same time, on the basis of the previous data processing, the fuzzy set classification method is used to build the model, and the learning model is more close to different types of well production, which enhances the applicability of the model in the field practice. Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. The results show that the improved support vector machine method has high accuracy in the prediction of small sample data, and the method can be widely used in the production prediction of horizontal Wells in tight reservoirs, providing a reference for the efficient development of tight reservoirs. |
| format | Article |
| id | doaj-art-86d1eb0a0b8648faa2ade6bea4579746 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-86d1eb0a0b8648faa2ade6bea45797462025-01-24T00:01:53ZengIEEEIEEE Access2169-35362025-01-0113118231183510.1109/ACCESS.2025.352746510833609Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production PredictionChao Wang0https://orcid.org/0009-0007-5935-7388Ruogu Wang1Yuhan Lin2Jiafei Zhang3Xiaofei Xie4Zidan Zhao5Yunlin Xu6Shaanxi Yanchang Petroleum (Group) Company Ltd., Natural Gas Research Institute Branch, Xi’an, ChinaShaanxi Yanchang Petroleum (Group) Company Ltd., Natural Gas Research Institute Branch, Xi’an, ChinaChangqing Geophysical Exploration Division, CNPC Eastern Geophysical Exploration Company Ltd., Xi’an, ChinaShaanxi Yanchang Petroleum (Group) Company Ltd., Natural Gas Research Institute Branch, Xi’an, ChinaShaanxi Yanchang Petroleum (Group) Company Ltd., Natural Gas Research Institute Branch, Xi’an, ChinaShaanxi Yanchang Petroleum (Group) Company Ltd., Natural Gas Research Institute Branch, Xi’an, ChinaShaanxi Yanchang Petroleum (Group) Company Ltd., Natural Gas Research Institute Branch, Xi’an, ChinaHorizontal well production prediction is crucial for the efficient development of tight reservoirs. However, owing to the complexity of the parameters affecting horizontal well production, improving the prediction accuracy has always been the unremitting goal pursued by the oil and gas industry. Limited by the number of parameters, the traditional linear fitting method has low computational efficiency and a large error, which brings difficulties to horizontal well production prediction. In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. At the same time, on the basis of the previous data processing, the fuzzy set classification method is used to build the model, and the learning model is more close to different types of well production, which enhances the applicability of the model in the field practice. Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. The results show that the improved support vector machine method has high accuracy in the prediction of small sample data, and the method can be widely used in the production prediction of horizontal Wells in tight reservoirs, providing a reference for the efficient development of tight reservoirs.https://ieeexplore.ieee.org/document/10833609/SVMchaotic genetic algorithmhorizontal welltight reservoirfuzzy set |
| spellingShingle | Chao Wang Ruogu Wang Yuhan Lin Jiafei Zhang Xiaofei Xie Zidan Zhao Yunlin Xu Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction IEEE Access SVM chaotic genetic algorithm horizontal well tight reservoir fuzzy set |
| title | Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction |
| title_full | Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction |
| title_fullStr | Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction |
| title_full_unstemmed | Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction |
| title_short | Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction |
| title_sort | optimized application of cga svm in tight reservoir horizontal well production prediction |
| topic | SVM chaotic genetic algorithm horizontal well tight reservoir fuzzy set |
| url | https://ieeexplore.ieee.org/document/10833609/ |
| work_keys_str_mv | AT chaowang optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction AT ruoguwang optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction AT yuhanlin optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction AT jiafeizhang optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction AT xiaofeixie optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction AT zidanzhao optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction AT yunlinxu optimizedapplicationofcgasvmintightreservoirhorizontalwellproductionprediction |