Petroleum Science >2013,??Issue 1:??81-90 DOI: https://doi.org/10.1007/s12182-013-0253-x
A new diagnostic method for identifying working conditions of submersible reciprocating pumping systems Open?Access
文章信息
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;Research and Development Center, Daqing Oilfield Company, Daqing, Heilongjiang 163000, China;Research Institute of Oil Production Engineering, Daqing Oilfield Company, Daqing, Heilongjiang 163000, China;School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
文章摘要
from onshore oil wells, and the identification of its working condition has an important influence on oil
production. In this paper we proposed a diagnostic method for identifying the working condition of the
submersible pumping system. Based on analyzing the working principle of the pumping unit and the pump
structure, different characteristics in loading and unloading processes of the submersible linear motor were
obtained at different working conditions. The characteristic quantities were extracted from operation data
of the submersible linear motor. A diagnostic model based on the support vector machine (SVM) method
was proposed for identifying the working condition of the submersible pumping unit, where the inputs of
the SVM classifier were the characteristic quantities. The performance and the misjudgment rate of this
method were analyzed and validated by the data acquired from an experimental simulation platform. The
model proposed had an excellent performance in failure diagnosis of the submersible pumping system.
The SVM classifier had higher diagnostic accuracy than the learning vector quantization (LVQ) classifier.
關(guān)鍵詞
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Submersible reciprocating pump, working condition, failure diagnosis, linear motor, characteristic quantity, support vector machine, misjudgment rate