Petroleum Science >2013,??Issue 1:??73-80 DOI: https://doi.org/10.1007/s12182-013-0252-y
Using the curve moment and the PSO-SVMmethod to diagnose downhole conditions ofa sucker rod pumping unit Open?Access
文章信息
作者：Li Kun,Gao Xianwen,Tian Zhongda and Qiu Zhixue
作者單位：
College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China;College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
投稿時(shí)間：2012-03-07
引用方式：Li, K., Gao, X., Tian, Z. et al. Pet. Sci. (2013) 10: 73. https://doi.org/10.1007/s12182-013-0252-y
文章摘要
Downhole working conditions of sucker rod pumping wells are automatically identified on a
computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and
pattern classification are two key steps. The dynamometer card is firstly divided into four parts which
include different production information according to the “four point method” used in actual oilfield
production, and then the moment invariants for pattern recognition are extracted. An improved support
vector machine (SVM) method is used for pattern classification whose error penalty parameter C and
kernel function parameter g are optimally chosen by the particle swarm optimization (PSO) algorithm.
The simulation results show the method proposed in this paper has good classification results.
computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and
pattern classification are two key steps. The dynamometer card is firstly divided into four parts which
include different production information according to the “four point method” used in actual oilfield
production, and then the moment invariants for pattern recognition are extracted. An improved support
vector machine (SVM) method is used for pattern classification whose error penalty parameter C and
kernel function parameter g are optimally chosen by the particle swarm optimization (PSO) algorithm.
The simulation results show the method proposed in this paper has good classification results.
關(guān)鍵詞
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Sucker rod pumping unit, diagnosis of downhole conditions, dynamometer card, curvemoment, support vector machine, particle swarm optimization