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

Chaodong Tan,Peiyao Chen,Ziming Feng,Mei Lu,Gang Feng,Xin Ai,Qiannan Zhou

doi:10.3389/feart.2022.852633

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.

Highlights

The sucker rod pumping (SRP) is the main artificial lifting method for oil wells
The AlexNet model has increased by 9.4 and 28.8% which means that our method enhances the separability of the data itself, solves the inseparable defect of the data caused by the traditional normalization method, and is applicable to all models; (2) among the six models, the convolutional neural network is better than support vector machines (SVMs), which means that, in the identification and classification of the dynamometer card, the automatic extraction of the dynamometer card graphic features by the convolutional neural network is better than the manually designed feature extraction method; and (3) the performance of SE-ResNet with both the residual module and the SE module is better than that of the ResNet with only the residual module
1) The defects of the traditional dynamometer card normalization method are demonstrated, and the experimental results show that the working condition diagnosis model with it will get poor results, not meeting actual needs

Summary

INTRODUCTION

The sucker rod pumping (SRP) is the main artificial lifting method for oil wells. Because the rods, pipes, and pumps of SRP work in a harsh environment, the SRP wells frequently fail after long-term operation. Xmax, Ymin, Ymax are the maximum and minimum values of displacement (m) and load (kN), which is called the normalized scale It makes the different normalized dynamometer cards of one working condition show consistency in shape features, which is conducive to the model to better learn the classification features of this working condition and eliminate the noise in the data. It shows that the increase in the k value helps the data introduce more feature information, enhance the separability of the data itself, and greatly improve the accuracy of the model and the recall rate of complicated working conditions. The overall accuracy rate can reach more than 95%, and the average recall rate for complicated working conditions is more than 90%, which meets the actual demand for intelligent diagnosis of working conditions on the oil field

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT