Research on the Working Condition Recognition Technology of Rod Pumping System Based on Deep Learning

Petroleum resources are a vital strategic resource related to national development. During the oil extraction process in China, pumping units operate in harsh environments for long periods, with complex working conditions and a high risk of equipment damage. Timely detection and understanding of the working conditions of the rod pumping system are of great significance for improving the production efficiency and economic benefits of oil fields. This paper adopts a method based on deep learning for the working condition recognition of the rod pumping system, taking the indicator diagram data of the rod pumping system as the research object and preprocessing the data. Three convolutional neural network models, namely LeNet5, AlexNet, and ResNet, are built for training and recognition. Through analysis, research, and experimental comparison, it is found that the ResNet model has the better condition in the working condition recognition of the rod pumping system, which can better meet the actual requirements of working condition recognition of the rod pumping system. The research shows that the deep learning method has strong recognition ability and good feasibility in the working condition recognition of the rod pumping system.