A Fault Diagnosis Model of an Electric Submersible Pump Based on Mechanism Knowledge

Electric submersible pumps (ESPs) are crucial equipment in offshore oilfield production. Due to their complex structure and the variable geological environments in which they work, ESPs are prone to a wide range of complex faults. Existing fault diagnosis models for ESP wells face several issues, including high subjective dependence, large sample data requirements, and poor adaptability to different geological environments. These issues lead to relatively low accuracy in ESP well fault diagnosis. To address these challenges, this paper integrates the mechanistic knowledge of ESP wells with their working parameters to construct a fault symptom inference model for ESP wells. A fault diagnosis model for ESP wells is formed by combining deep learning with an expert rule-based fault diagnosis method. The two models are connected in series to construct a mechanism knowledge-integrated ESP fault diagnosis model (MK-ESPFDM), achieving real-time and accurate diagnosis of faults in ESP wells. A series of experiments demonstrate that the proposed algorithm strategy can effectively improve the diagnostic accuracy of the model. It also reduces human subjectivity and enhances the model's adaptability to different faults and geological environments. The research presented in this paper has reached a high level in the field of ESP well fault diagnosis.