Multi-Scale Normalization Method Combined With a Deep CNN Diagnosis Model of Dynamometer Card in SRP Well

Abstract

There are more than 20 types of dynamometer card measured of sucker rod pumping (SRP) wells in oil fields, and some working conditions are very complicated. The common diagnosis model of SRP well based on dynamometer card recognition has low accuracy and recall rate of complicated working conditions. In order to improve the accuracy and recall rate of multi-condition diagnosis of SRP well and solve the problem of inseparable data attributes caused by traditional dynamometer card normalization methods, a new dynamometer card preprocessing method is proposed, which uses a clustering analysis algorithm to obtain multiple normalized dynamometer cards of the original dynamometer card and at the same time, adds a set of time-series dynamometer cards to enhance the separability of data. The dynamometer card preprocessing method combined with four deep convolutional neural networks are used to build a diagnosis model. Experiments are conducted under 24 different working conditions, the accuracy of our method is up to 95.8%, and the average recall rate of complicated working conditions is up to 93.1%, which is 13.6 and 35.3% higher than that of the model (AlexNet) built by the traditional preprocessing method. In addition, the preprocessing method of dynamometer card proposed is applicable to all deep learning models and machine learning models. Field applications show that our method is very effective for recalling abnormal working conditions, which is of great significance to the real demand for intelligent diagnosis of SRP well.