Working condition diagnosis of electric submersible pump based on machine learning

The working condition diagnosis model of the electric submersible pump (ESP) based on machine learning (ML) using real-time current data was established to reduce human error in the working condition recognition and analysis of ESPs with current cards. Firstly, the feature engineering (FE) method was used to acquire the eigenvalues of ESP current. Secondly, the principal component analysis (PCA) method was used for the unsupervised dimensionality reduction clustering of eigenvalues, and the results of clustering were compared with the actual working conditions to prove the effectiveness of clustering. Thirdly, the logistic regression (LR) model was established using the labeled data after dimensionality reduction. Finally, the untrained data was substituted into the established model for error analysis. The complete working condition diagnosis process based on ML using high-density real-time current data was conducted for 56 ESP wells in Oilfield A. The results show that the model successfully realizes the classification and recognition of four common working conditions, including normal, pump depletion, overload shutdown, and frequent short-cycle operation. The accuracy, precision, and recall of the diagnosis reach more than 80%, while the F₁ score reaches 85%, which meets the requirement of classification. The feasibility and reliability of working condition diagnosis of ESPs based on ML using real-time current data are proved.