Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells
Petroleum is a critical energy resource in modern society, and its exploration and production are essential for meeting global energy demands. Dynamometer cards are important graphics that reflect the operational conditions of pumping wells, and their recognition is crucial for optimizing oil well p...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025021474 |
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| author | Senhao Ren Wenqiang Tang Chao Ma Li Hou Xiaodong Chen Jiashan Lin Jie Yang Yun Yang Xiao Huo Guoxin Li Daowei Zhang |
| author_facet | Senhao Ren Wenqiang Tang Chao Ma Li Hou Xiaodong Chen Jiashan Lin Jie Yang Yun Yang Xiao Huo Guoxin Li Daowei Zhang |
| author_sort | Senhao Ren |
| collection | DOAJ |
| description | Petroleum is a critical energy resource in modern society, and its exploration and production are essential for meeting global energy demands. Dynamometer cards are important graphics that reflect the operational conditions of pumping wells, and their recognition is crucial for optimizing oil well production and diagnosing faults. With the development of deep learning, several automated methods based on deep learning have been proposed to analyze the specific working conditions of pumping wells from dynamometer cards. However, the sucker rod production system (SRPS) operates in a complex and variable environment, resulting in scarce effective samples and dynamometer card features that are sparse and informationally limited. To overcome these challenges, we propose a multi-function composite data generation paradigm that integrates diverse functional characteristics, generating 11 classes of highly interpretable single-condition images as training data for a prior model. This establishes a foundation of prior knowledge for training on subsequent actual condition data. Additionally, we introduce the Patch Importance Mamba (PIMamba) model, a dynamometer card recognition framework based on the State Space Model (SSM) architecture. The PIMamba model includes a Patch Importance (PI) module that assigns higher weights to data blocks containing key feature information, effectively filtering out irrelevant or low-sensitivity data and enhancing feature extraction precision and efficiency. In the Gaskule area of the western Qaidam Basin, PIMamba achieved a dynamometer card recognition accuracy of 94.73 %, offering a novel approach to fault recognition in dynamometer cards and highlighting the significant potential of deep learning in the petroleum sector. |
| format | Article |
| id | doaj-art-de6dbd6bd78e435799931531aedaa96e |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-de6dbd6bd78e435799931531aedaa96e2025-08-20T03:17:27ZengElsevierResults in Engineering2590-12302025-09-012710607510.1016/j.rineng.2025.106075Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wellsSenhao Ren0Wenqiang Tang1Chao Ma2Li Hou3Xiaodong Chen4Jiashan Lin5Jie Yang6Yun Yang7Xiao Huo8Guoxin Li9Daowei Zhang10State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation & Institute of Sedimentary Geology & College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, PR China; Key Laboratory of Sedimentary Basin and Oil and Gas Resources & Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu 610059, PR ChinaState Key Laboratory of Oil and Gas Reservoir Geology and Exploitation & Institute of Sedimentary Geology & College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, PR China; Key Laboratory of Sedimentary Basin and Oil and Gas Resources & Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu 610059, PR China; Corresponding author.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation & Institute of Sedimentary Geology & College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, PR China; Key Laboratory of Sedimentary Basin and Oil and Gas Resources & Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu 610059, PR ChinaResearch Institute of Petroleum Exploration & Development, PetroChina & Artificial Intelligence Technology R&D Center for Exploration and Development, CNPC, PR ChinaPlateau Saline Lacustrine Basin Oil-Gas Geology Key Laboratory of Qinghai Province, Dunhuang 7362023, PR China; Qinghai Oilfield Company, PetroChina, Dunhuang 7362023, PR ChinaKey Laboratory of Sedimentary Basin and Oil and Gas Resources & Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu 610059, PR ChinaPlateau Saline Lacustrine Basin Oil-Gas Geology Key Laboratory of Qinghai Province, Dunhuang 7362023, PR China; Qinghai Oilfield Company, PetroChina, Dunhuang 7362023, PR ChinaPlateau Saline Lacustrine Basin Oil-Gas Geology Key Laboratory of Qinghai Province, Dunhuang 7362023, PR China; Qinghai Oilfield Company, PetroChina, Dunhuang 7362023, PR ChinaPlateau Saline Lacustrine Basin Oil-Gas Geology Key Laboratory of Qinghai Province, Dunhuang 7362023, PR China; Qinghai Oilfield Company, PetroChina, Dunhuang 7362023, PR ChinaChina National Petroleum Corporation, Beijing 100007, PR ChinaChina National Petroleum Corporation, Beijing 100007, PR ChinaPetroleum is a critical energy resource in modern society, and its exploration and production are essential for meeting global energy demands. Dynamometer cards are important graphics that reflect the operational conditions of pumping wells, and their recognition is crucial for optimizing oil well production and diagnosing faults. With the development of deep learning, several automated methods based on deep learning have been proposed to analyze the specific working conditions of pumping wells from dynamometer cards. However, the sucker rod production system (SRPS) operates in a complex and variable environment, resulting in scarce effective samples and dynamometer card features that are sparse and informationally limited. To overcome these challenges, we propose a multi-function composite data generation paradigm that integrates diverse functional characteristics, generating 11 classes of highly interpretable single-condition images as training data for a prior model. This establishes a foundation of prior knowledge for training on subsequent actual condition data. Additionally, we introduce the Patch Importance Mamba (PIMamba) model, a dynamometer card recognition framework based on the State Space Model (SSM) architecture. The PIMamba model includes a Patch Importance (PI) module that assigns higher weights to data blocks containing key feature information, effectively filtering out irrelevant or low-sensitivity data and enhancing feature extraction precision and efficiency. In the Gaskule area of the western Qaidam Basin, PIMamba achieved a dynamometer card recognition accuracy of 94.73 %, offering a novel approach to fault recognition in dynamometer cards and highlighting the significant potential of deep learning in the petroleum sector.http://www.sciencedirect.com/science/article/pii/S2590123025021474Dynamometer cardSucker-rod pumping systemState space modelData generationPatch importance |
| spellingShingle | Senhao Ren Wenqiang Tang Chao Ma Li Hou Xiaodong Chen Jiashan Lin Jie Yang Yun Yang Xiao Huo Guoxin Li Daowei Zhang Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells Results in Engineering Dynamometer card Sucker-rod pumping system State space model Data generation Patch importance |
| title | Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells |
| title_full | Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells |
| title_fullStr | Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells |
| title_full_unstemmed | Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells |
| title_short | Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells |
| title_sort | multi function composite data generation and pimamba model for fault diagnosis in sucker rod pumping wells |
| topic | Dynamometer card Sucker-rod pumping system State space model Data generation Patch importance |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025021474 |
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