Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning
ROP is an important index to evaluate the efficiency of oil and gas drilling. In order to accurately predict the ROP of an oilfield in Xinjiang working area, a ROP prediction model based on the historical drilling data of this working area was established based on stacking ensemble learning. This mo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2023/6645604 |
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| _version_ | 1832548014881767424 |
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| author | Yangfeng Ren Baoping Lu Shuangjin Zheng Kai Bai Lin Cheng Hao Yan Gan Wang |
| author_facet | Yangfeng Ren Baoping Lu Shuangjin Zheng Kai Bai Lin Cheng Hao Yan Gan Wang |
| author_sort | Yangfeng Ren |
| collection | DOAJ |
| description | ROP is an important index to evaluate the efficiency of oil and gas drilling. In order to accurately predict the ROP of an oilfield in Xinjiang working area, a ROP prediction model based on the historical drilling data of this working area was established based on stacking ensemble learning. This model integrates the K-nearest neighbor algorithm and support vector machine algorithm by stacking ensemble strategy and uses genetic algorithm to optimize model parameters, forming a new method of ROP prediction suitable for this oilfield. The prediction results show that the accuracy of ROP prediction by this method is up to 92.5%, and the performance is stable, which can provide reference for the optimization of drilling parameters in this oilfield and has specific guiding significance for improving the efficiency of drilling operations. |
| format | Article |
| id | doaj-art-cd76e0bcec064cb386fdce14c0acee0d |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-cd76e0bcec064cb386fdce14c0acee0d2025-02-03T06:42:41ZengWileyGeofluids1468-81232023-01-01202310.1155/2023/6645604Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble LearningYangfeng Ren0Baoping Lu1Shuangjin Zheng2Kai Bai3Lin Cheng4Hao Yan5Gan Wang6School of Petroleum EngineeringState Key Laboratory of Shale Oil and Gas Enrichment Mechanism and Effective DevelopmentSchool of Petroleum EngineeringSchool of Computer ScienceSchool of Petroleum EngineeringSchool of Petroleum EngineeringSchool of Petroleum EngineeringROP is an important index to evaluate the efficiency of oil and gas drilling. In order to accurately predict the ROP of an oilfield in Xinjiang working area, a ROP prediction model based on the historical drilling data of this working area was established based on stacking ensemble learning. This model integrates the K-nearest neighbor algorithm and support vector machine algorithm by stacking ensemble strategy and uses genetic algorithm to optimize model parameters, forming a new method of ROP prediction suitable for this oilfield. The prediction results show that the accuracy of ROP prediction by this method is up to 92.5%, and the performance is stable, which can provide reference for the optimization of drilling parameters in this oilfield and has specific guiding significance for improving the efficiency of drilling operations.http://dx.doi.org/10.1155/2023/6645604 |
| spellingShingle | Yangfeng Ren Baoping Lu Shuangjin Zheng Kai Bai Lin Cheng Hao Yan Gan Wang Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning Geofluids |
| title | Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning |
| title_full | Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning |
| title_fullStr | Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning |
| title_full_unstemmed | Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning |
| title_short | Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning |
| title_sort | research on the rate of penetration prediction method based on stacking ensemble learning |
| url | http://dx.doi.org/10.1155/2023/6645604 |
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