Prediction of Sucker Rod Pump Failures Using Scaled Load Ratios and Machine Learning

Summary Monitoring of sucker rod pumps is essential for maintaining continuous production. Traditionally, pump anomalies have been diagnosed by classifying downhole pump cards that are numerically calculated from surface measurements and rod string properties. However, pumps may continue to operate normally even when their downhole pump cards are classified as abnormal. Under constraints of limited manpower and budget, prioritizing the prediction and prevention of actual pump failures is more practical than attempting to manage all frequent anomalies detected through downhole pump card classification. With this study, we propose a real-time pump failure prediction method utilizing deep learning and a novel metric—the scaled load ratio, which is the ratio of the normalized minimum to maximum surface rod loads. The proposed method does not require downhole pump card computations, as it accurately predicts pump failures using only surface pump load data. In predicting pump failures in two United States (US) shale oil fields, the proposed method achieved an F1 score of 0.857 and was able to predict failures with an average lead time of 13.97 days before they occurred.