Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM

Sensors

Evgeny Osipov¹  Valeriy Vyatkin¹  Haibin Yu²  Peng Zeng²  Shichao Li²  Haibo Cheng² 

[1] Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden;State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;

关键词: working condition recognition;    sucker-rod pumping system;    dynamometer card;    convolutional neural network;    transfer learning;    support vector machine;   

DOI  :  10.3390/s20195659

来源: DOAJ

【 摘 要 】

Sucker-rod pumping systems are the most widely applied artificial lift equipment in the oil and gas industry. Accurate and intelligent working condition recognition of pumping systems imposes major impacts on oilfield production benefits and efficiency. The shape of dynamometer card reflects the working conditions of sucker-rod pumping systems, and different conditions can be indicated by their typical card characteristics. In traditional identification methods, however, features are manually extracted based on specialist experience and domain knowledge. In this paper, an automatic fault diagnosis method is proposed to recognize the working conditions of sucker-rod pumping systems with massive dynamometer card data collected by sensors. Firstly, AlexNet-based transfer learning is adopted to automatically extract representative features from various dynamometer cards. Secondly, with the extracted features, error-correcting output codes model-based SVM is designed to identify the working conditions and improve the fault diagnosis accuracy and efficiency. The proposed AlexNet-SVM algorithm is validated against a real dataset from an oilfield. The results reveal that the proposed method reduces the need for human labor and improves the recognition accuracy.

【 授权许可】

Unknown