It has been a great challenge for the oil and gas industry to timely identify any electrical submersible pump (ESP) abnormal performance to avoid ESP failure. Given the high cost of the ESP failure, more and more real-time surveillance systems are applied to monitor ESP performance to generate alarms in the case of failures. This paper presents a robust principal component analysis (PCA) model to perform fault detection for ESP systems continuously. A three-dimensional plot of scores of principal components was used to observe different patterns during the stable and failure periods. 47 cases of actual failure events and 40 cases of stable operating events were tested on the robust PCA model to generate prediction results. The testing results demonstrate that the robust PCA model has managed to identify 20 failure events before the actual failure time out of the 47 failure cases and has successfully distinguished all the 40 stable operating wells. This study has concluded that PCA has the potential to be used as a monitoring platform to recognize dynamic change and therefore to predict the developing failures in the ESP system.
© 2021 The Authors. Published by American Chemical Society.