Diagnosis of electrical submersible pump failure using deep learning model with sand-water flow experimental data

Abstract

Reliability of electrical submersible pump (ESP) failure diagnosis is crucial because unexpected failures can lead to additional production costs. This study presents a deep-learning method based on a long short-term memory autoencoder (LSTM-AE) model with principal component analysis (PCA) for ESP failure diagnosis. To obtain data on variables related to ESP failure, a sand–water flow experiment was designed and conducted. An LSTM-AE model was then developed based on the PCA of the experimental data, demonstrating a failure diagnosis accuracy of 90.69%, which is higher than that of the LSTM-AE model without PCA. The failure-detection point was predicted, closely aligning with the initial point of failure. To assess its suitability for field applications, the proposed LSTM-AE model with PCA was tested with data from the Sandy 03 well in the Permian Basin, USA. The LSTM-AE model with PCA achieved a failure diagnosis accuracy of 81.81%, and the initial failure detection point was accurate. These results indicate that the LSTM-AE model with PCA can effectively capture long-term dependencies in time-series data and provide reliable ESP failure diagnosis. By accurately identifying potential failures, this approach offers significant potential for improving operational efficiency and reducing maintenance costs in ESP systems.