基于LeNet模型的游梁式抽油机工况诊断研究

Research on Working Condition Diagnosis of Beam Pumping Unit Based on LeNet Model

叶哲伟 易钦珏 罗良

YE Zhewei YI Qinjue LUO Liang

西南石油大学机电工程学院, 四川 成都 610500

School of Mechanical and Electrical Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China

游梁式抽油机是有杆泵系统中应用最广泛的部件，分析有杆泵的示功图是判断抽油机井下工况的重要手段。针对传统示功图识别方法存在依靠专家经验以及需要人工进行特征提取，导致出现相似示功图时识别准确度低的问题开展研究。通过深度学习卷积神经网络在图像识别领域的应用，提出了一种基于LeNet的卷积神经网络模型，实现了示功图的自动识别，所搭建的模型在简化模型结构的同时考虑了抽油机常见的15种井下工况，并引入了Dropout层以及局部响应归一化层防止模型过拟合的同时提高模型的泛化能力。实验结果表明，该模型不仅收敛速度快，而且对于工况进行诊断的准确度平均为94.68%，满足抽油机工况检测的诊断精度要求。该研究为抽油机井工况智能监控预警系统的构建提供了依据，对建设智慧油田以及油田的高效生产具有重要意义。

The beam pumping unit is the most widely used component in the rod pump system, and analyzing the indicator diagram of the rod pump is an important means to judge the downhole working conditions of the pumping unit. Traditional indicator diagram recognition method relies on expert experience and manual feature extraction, which leads to low recognition accuracy when dealing with similar indicator diagrams. Through the application of deep learning convolutional neural network in the field of image recognition, a LeNet-based convolutional neural network model was proposed, which realized the automatic recognition of the indicator diagram, and the model built simplified the model structure while considering 15 common downhole conditions of pumping units, and a dropout layer and a local response normalization layer are introduced to prevent the model from overfitting and improve the generalization ability of the model. The experimental results showed that the model not only converged quickly, but also had an average accuracy of 94.68% for diagnosing working conditions, which met the accuracy requirements of pumping unit working conditions detection. The research provides a basis for the construction of an intelligent monitoring and early warning system for pumping wells, and is of great significance to the construction of smart oilfields and efficient production of oilfields.

10.11885/j.issn.1674-5086.2021.05.06.02