Diagnosis for Sucker Rod Pumps Using Bayesian Networks and Dynamometer Card

Boyuan Zheng, Xianwen Gao, Rong Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The automatic diagnosis for sucker rod pump (SRP) is an essential measurement to ensure the oil fields' interests in the oil recovery. As the important information resource on monitoring and diagnosis, the dynamometer card (DC) plays an irreplaceable role in oil engineering. In the application, how to use DC to fulfill the diagnosis is always the key to this problem. Thus, a novel method based on load analysis and Bayesian network is proposed in this paper. At first off, DC's coordinate is transformed to cater to the load analysis, which provides an instinctive way for analyzing. After that, five statistical features and Shannon entropy are extracted from the DC, which are employed as the input of the Bayesian network (BN) presented in the particular framework. At last, a set of field dynamometer card is employed as the experimental data and the experimental results demonstrate the feasibility and superiority of the proposed method for diagnosing the working states of SRPs.

Original languageEnglish (US)
Title of host publication2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019
EditorsWei Guo, Steven Li, Qiang Miao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108612
DOIs
StatePublished - Oct 2019
Event10th Prognostics and System Health Management Conference, PHAI-Qingdao 2019 - Qingdao, China
Duration: Oct 25 2019Oct 27 2019

Publication series

Name2019 Prognostics and System Health Management Conference, PHAI-Qingdao 2019

Conference

Conference10th Prognostics and System Health Management Conference, PHAI-Qingdao 2019
CountryChina
CityQingdao
Period10/25/1910/27/19

Keywords

  • Bayesian network classifier
  • Diagnosis
  • Dynamometer card
  • Sucker rod pump

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Safety, Risk, Reliability and Quality

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