A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
Managed pressure drilling (MPD) is an essential technology for safe and efficient drilling in deep high-temperature and high-pressure formations with narrow safety pressure windows. However, the complex conditions in deep wells make the mechanism of multiphase flow in drilling annulus complicated an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/13/6728 |
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| author | Zhaopeng Zhu Xianzhi Song Rui Zhang Gensheng Li Liang Han Xiaoli Hu Dayu Li Donghan Yang Furong Qin |
| author_facet | Zhaopeng Zhu Xianzhi Song Rui Zhang Gensheng Li Liang Han Xiaoli Hu Dayu Li Donghan Yang Furong Qin |
| author_sort | Zhaopeng Zhu |
| collection | DOAJ |
| description | Managed pressure drilling (MPD) is an essential technology for safe and efficient drilling in deep high-temperature and high-pressure formations with narrow safety pressure windows. However, the complex conditions in deep wells make the mechanism of multiphase flow in drilling annulus complicated and increase the difficulty for accurate prediction of bottomhole pressure (BHP). Recently, an increasing volume of research shows that intelligent technology is an efficient means of accurately predicting BHP. However, few studies have focused on the temporal properties and variation mechanism of BHP. In this paper, hybrid neural network prediction models based on the multi-branch parallel are established by combining the different advantages of back propagation (BP), long short-term memory (LSTM), and a one-dimensional convolutional neural network (1DCNN) model. The results show that the relative error of the best model is about 70% lower than the optimal single intelligent model. Preliminary experimental results reveal that the hybrid models combine the advantages of different single models, which is more accurate and robust for extracting the temporal features of MWD. Finally, based on the trend analysis, the validity of the hybrid model is further verified. This study provides a reference for solving the problem of optimizing temporal characteristics and guidance for fine pressure control in complex formations. |
| first_indexed | 2024-03-09T22:06:02Z |
| format | Article |
| id | doaj.art-dd3f1234ced64dae8a840dfa46c8dbd1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:02Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dd3f1234ced64dae8a840dfa46c8dbd12023-11-23T19:41:42ZengMDPI AGApplied Sciences2076-34172022-07-011213672810.3390/app12136728A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure DrillingZhaopeng Zhu0Xianzhi Song1Rui Zhang2Gensheng Li3Liang Han4Xiaoli Hu5Dayu Li6Donghan Yang7Furong Qin8School of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaSchool of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaManaged pressure drilling (MPD) is an essential technology for safe and efficient drilling in deep high-temperature and high-pressure formations with narrow safety pressure windows. However, the complex conditions in deep wells make the mechanism of multiphase flow in drilling annulus complicated and increase the difficulty for accurate prediction of bottomhole pressure (BHP). Recently, an increasing volume of research shows that intelligent technology is an efficient means of accurately predicting BHP. However, few studies have focused on the temporal properties and variation mechanism of BHP. In this paper, hybrid neural network prediction models based on the multi-branch parallel are established by combining the different advantages of back propagation (BP), long short-term memory (LSTM), and a one-dimensional convolutional neural network (1DCNN) model. The results show that the relative error of the best model is about 70% lower than the optimal single intelligent model. Preliminary experimental results reveal that the hybrid models combine the advantages of different single models, which is more accurate and robust for extracting the temporal features of MWD. Finally, based on the trend analysis, the validity of the hybrid model is further verified. This study provides a reference for solving the problem of optimizing temporal characteristics and guidance for fine pressure control in complex formations.https://www.mdpi.com/2076-3417/12/13/6728bottomhole pressuretemporal propertieshybrid neural networks |
| spellingShingle | Zhaopeng Zhu Xianzhi Song Rui Zhang Gensheng Li Liang Han Xiaoli Hu Dayu Li Donghan Yang Furong Qin A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling Applied Sciences bottomhole pressure temporal properties hybrid neural networks |
| title | A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling |
| title_full | A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling |
| title_fullStr | A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling |
| title_full_unstemmed | A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling |
| title_short | A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling |
| title_sort | hybrid neural network model for predicting bottomhole pressure in managed pressure drilling |
| topic | bottomhole pressure temporal properties hybrid neural networks |
| url | https://www.mdpi.com/2076-3417/12/13/6728 |
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