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A working condition recognition method of sucker-rod pumping wells based on multi-view learning and Hessian regularization
ZHOU Bin, WANG Yanjiang, LIU Weifeng, LIU Baodi
(College of Information and Control Engineering in China University of Petroleum(East China), Qingdao 266580, China)
Abstract:
To resolve the problems in working condition recognition of sucker-rod pumping wells and to further improve the accuracy and practicality, a novel method based on multi-view learning and Hessian regularization to identify the working condition was proposed. Firstly, the measured dynamometer cards, electrical power and wellhead temperature data were characterized based on the prior information and empirical knowledge. Then a multi-view logistic regression model with log loss function and Hessian regularization for working condition recognition was established. Finally, the working condition was classified and recognized by an alternating optimization algorithm. The proposed method was applied to eleven cases of typical working condition recognition in a block in Shengli Oilfield, and the results were compared with traditional recognition methods based on measured dynamometer cards, electrical power data and multi-sources of feature connection, respectively. The comparison shows that the recognition rates are improved by 2.4%, 11% and 13.8%, respectively. The performance is even much better with a small amount of marked training samples.
Key words:  sucker-rod pumping wells  working condition recognition  multi-view learning  logistic regression  Hessian regularization