Experimental data-driven model development for ESP failure diagnosis based on the principal component analysis

Abstract

The reliable diagnosis of electrical submersible pump (ESP) failure is a vital process for establishing of the optimal production strategies and achieving minimum development costs. Although traditional ammeter charts and nodal analysis are commonly used for ESP failure diagnosis, the techniques have limitations, as it requires manpower and is difficult to diagnose the failure in real-time. Therefore, in this study, ESP failure diagnosis was performed using the principal component analysis (PCA). First, 11 types of 9,955 pieces of data were acquired from a newly constructed ESP experimental system for 300 days. During the experimental period, ESP failure occurred twice with a significant drop in performance: first on day 112 and second on day 271. The PCA model was constructed with the 8,928 pieces of normal status data and tested with the 1,027 pieces of normal and failure status data. Three principal components were extracted from the measured data to identify the patterns of the normal and failure status. Based on the logistic regression method to analyze the efficiency of the PCA model, it was found out that the developed PCA model showed an accuracy of 93.3%. Therefore, the PCA model was found to be reliable and effective for the ESP failure diagnosis and performance analysis.