Data-Driven Fault Prediction for Electric Submersible Progressing Cavity Pump Wells

During the development of coalbed methane (CBM), electric submersible progressing cavity pumps (ESPCPs) face challenges such as handling gas–liquid mixtures, high water content, declining pump efficiency, and frequent failures. This study proposes a data-driven fault prediction and diagnostic method, analyzing various factors affecting pump performance, such as gas–liquid ratio, water content of extracted fluids, pumping depth, and current fluctuations, to explore their correlation with pump failures. The dataset used in this study was collected from 85 ESPCP wells in the Northern Zhachi Oilfield between January 2019 and December 2022, with daily acquisition of key operational parameters including tubing pressure, pump current, vibration, temperature, produced liquid rate, and gas–liquid ratio. Baseline models including ARIMA and Gradient Boosting Decision Trees (GBDT) were trained for performance comparison, with the proposed PCA–LSTM model achieving a 15% and 9% improvement in validation accuracy over ARIMA and GBDT, respectively. Model performance is quantitatively reported: acceptance rate 86.79% and validation accuracy 72.22%. The results indicate that the PCA–LSTM model can effectively identify and predict ESPCP failure types, providing vital technical support for the efficient operation and maintenance of CBM wells, and its practical applicability has been well recognized by field engineers.