Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm

Haibo Cheng, Haibin Yu*, Peng Zeng, Evgeny Osipov, Shichao Li, Valeriy Vyatkin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

30 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number5659
Number of pages15
JournalSensors (Switzerland)
Volume20
Issue number19
DOIs
Publication statusPublished - 1 Oct 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Convolutional neural network
  • Dynamometer card
  • Sucker-rod pumping system
  • Support vector machine
  • Transfer learning
  • Working condition recognition

Fingerprint Dive into the research topics of 'Automatic recognition of sucker-rod pumping system working conditions using dynamometer cards with transfer learning and svm'. Together they form a unique fingerprint.

Cite this