Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
In modern oil and gas exploration and development, wellbore trajectory optimization and control is the key technology to improve drilling efficiency, reduce costs, and ensure safety. In the drilling operation of non-vertical wells in complex formations, the traditional static trajectory function, co...
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| Format: | Article |
| Language: | English |
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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/2076-3417/15/13/7258 |
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| author | Haipeng Gu Yang Wu Xiaowei Li Zhaokai Hou |
| author_facet | Haipeng Gu Yang Wu Xiaowei Li Zhaokai Hou |
| author_sort | Haipeng Gu |
| collection | DOAJ |
| description | In modern oil and gas exploration and development, wellbore trajectory optimization and control is the key technology to improve drilling efficiency, reduce costs, and ensure safety. In the drilling operation of non-vertical wells in complex formations, the traditional static trajectory function, combined with the classical optimization algorithm, has difficulty adapting to the parameter fluctuation caused by formation changes and lacks real-time performance. Therefore, this paper proposes a wellbore trajectory optimization model based on deep reinforcement learning to realize non-vertical well trajectory design and control while drilling. Aiming at the real-time optimization requirements of complex drilling scenarios, the TD3 algorithm is adopted to solve the problem of high-dimensional continuous decision-making through delay strategy update, double Q network, and target strategy smoothing. After reinforcement learning training, the trajectory offset is significantly reduced, and the accuracy is greatly improved. This research shows that the TD3 algorithm is superior to the multi-objective optimization algorithm in optimizing key parameters, such as well deviation, kickoff point (KOP), and trajectory length, especially in well deviation and KOP optimization. This study provides a new idea for wellbore trajectory optimization and design while drilling, promotes the progress and development of intelligent drilling technology, and provides a theoretical basis and technical support for more accurate, efficient, concise, and effective wellbore trajectory optimization and design while drilling in the future. |
| format | Article |
| id | doaj-art-7eeaef085b8b485a848be3bd0727b489 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7eeaef085b8b485a848be3bd0727b4892025-08-20T03:28:34ZengMDPI AGApplied Sciences2076-34172025-06-011513725810.3390/app15137258Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 AlgorithmHaipeng Gu0Yang Wu1Xiaowei Li2Zhaokai Hou3School of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, ChinaSchool of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, ChinaSchool of Mathematics and Statistics, Northeast Petroleum University, Daqing 163318, ChinaNEPU Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572000, ChinaIn modern oil and gas exploration and development, wellbore trajectory optimization and control is the key technology to improve drilling efficiency, reduce costs, and ensure safety. In the drilling operation of non-vertical wells in complex formations, the traditional static trajectory function, combined with the classical optimization algorithm, has difficulty adapting to the parameter fluctuation caused by formation changes and lacks real-time performance. Therefore, this paper proposes a wellbore trajectory optimization model based on deep reinforcement learning to realize non-vertical well trajectory design and control while drilling. Aiming at the real-time optimization requirements of complex drilling scenarios, the TD3 algorithm is adopted to solve the problem of high-dimensional continuous decision-making through delay strategy update, double Q network, and target strategy smoothing. After reinforcement learning training, the trajectory offset is significantly reduced, and the accuracy is greatly improved. This research shows that the TD3 algorithm is superior to the multi-objective optimization algorithm in optimizing key parameters, such as well deviation, kickoff point (KOP), and trajectory length, especially in well deviation and KOP optimization. This study provides a new idea for wellbore trajectory optimization and design while drilling, promotes the progress and development of intelligent drilling technology, and provides a theoretical basis and technical support for more accurate, efficient, concise, and effective wellbore trajectory optimization and design while drilling in the future.https://www.mdpi.com/2076-3417/15/13/7258intelligent drilling technologytrajectory accuracyreinforcement learningdynamic trajectory control |
| spellingShingle | Haipeng Gu Yang Wu Xiaowei Li Zhaokai Hou Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm Applied Sciences intelligent drilling technology trajectory accuracy reinforcement learning dynamic trajectory control |
| title | Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm |
| title_full | Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm |
| title_fullStr | Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm |
| title_full_unstemmed | Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm |
| title_short | Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm |
| title_sort | research on wellbore trajectory optimization and drilling control based on the td3 algorithm |
| topic | intelligent drilling technology trajectory accuracy reinforcement learning dynamic trajectory control |
| url | https://www.mdpi.com/2076-3417/15/13/7258 |
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