Deciphering Rod Pump Anomalies: A Deep Learning Autoencoder Approach

Abstract

This paper investigates the application of a self-coder neural network in oilfield rod pump anomaly detection. Rod pumps are critical equipment in oilfield production engineering, and their stability and reliability are crucial to the production efficiency and economic benefits. However, rod pumps are often affected by anomalies such as wax deposition, leading to increased maintenance costs and production interruptions. Traditional wax deposition detection methods are inefficient and fail to provide early warning capabilities. This paper reviews the research progress in sucker rod pump anomaly detection and autoencoder neural networks, providing a detailed description of the construction and training process of the autoencoder neural network model. Utilizing data from the rod-pumped wells of the Tuha oilfield in China, this study achieves the automatic recognition of various anomalies through data preprocessing and the training of an autoencoder model. This study also includes a comparative analysis of the differences in the anomaly detection performance between the autoencoder and traditional methods and verifies the effectiveness and superiority of the proposed method.