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Automatic Recognition of Sucker-Rod Pumping System Working Conditions Using Dynamometer Cards with Transfer Learning and SVM
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China. University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0069-640x
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2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 19, article id 5659Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 20, no 19, article id 5659
Keywords [en]
working condition recognition, sucker-rod pumping system, dynamometer card, convolutional neural network, transfer learning, support vector machine
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-81094DOI: 10.3390/s20195659ISI: 000586762000001PubMedID: 33022966Scopus ID: 2-s2.0-85092060311OAI: oai:DiVA.org:ltu-81094DiVA, id: diva2:1475037
Note

Validerad;2020;Nivå 2;2020-10-12 (alebob)

Available from: 2020-10-12 Created: 2020-10-12 Last updated: 2022-02-10Bibliographically approved

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Osipov, EvgenyVyatkin, Valeriy

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