Multi-function composite data generation and PIMamba model for fault diagnosis in sucker-rod pumping wells - Senhao Ren; Wenqiang Tang; Chao Ma; Li Hou; Xiaodong Chen; Jiashan Lin; Jie Yang; Yun Yang; Xiao Huo; Guoxin Li; Daowei Zhang (Results in Engineering, 2025)
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.