A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling
The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low ma...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7256 |
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| author | Yuqiang Zhang Xuezhe Yao Wenping Zhang Zhaopeng Zhu |
| author_facet | Yuqiang Zhang Xuezhe Yao Wenping Zhang Zhaopeng Zhu |
| author_sort | Yuqiang Zhang |
| collection | DOAJ |
| description | The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling. |
| format | Article |
| id | doaj-art-5cb8f088e57e40f78f0b7632f6e3aeaa |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-5cb8f088e57e40f78f0b7632f6e3aeaa2025-08-20T03:16:46ZengMDPI AGApplied Sciences2076-34172025-06-011513725610.3390/app15137256A Novel Adaptive Transient Model of Gas Invasion Risk Management While DrillingYuqiang Zhang0Xuezhe Yao1Wenping Zhang2Zhaopeng Zhu3Engineering Technology Management Department, Jianghan Oilfield Company, Qianjiang 433124, ChinaCollege of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaDrilling Engineering Technology Research Center, Sinopec Petroleum Engineering Technology Research Institute Co., Ltd., Beijing 102206, ChinaCollege of Mechanical and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, ChinaThe deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling.https://www.mdpi.com/2076-3417/15/13/7256gas kickfuzzy neural networkPID controlbottom hole pressureintelligent control |
| spellingShingle | Yuqiang Zhang Xuezhe Yao Wenping Zhang Zhaopeng Zhu A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling Applied Sciences gas kick fuzzy neural network PID control bottom hole pressure intelligent control |
| title | A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling |
| title_full | A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling |
| title_fullStr | A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling |
| title_full_unstemmed | A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling |
| title_short | A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling |
| title_sort | novel adaptive transient model of gas invasion risk management while drilling |
| topic | gas kick fuzzy neural network PID control bottom hole pressure intelligent control |
| url | https://www.mdpi.com/2076-3417/15/13/7256 |
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