中国石油大学学报（自然科学版）2018，Vol.42Issue(3)：154-161,8.DOI:10.3969/j.issn.1673-5005.2018.03.019
基于Hessian正则化多视角学习的抽油机井工况识别新方法
A working condition recognition method of sucker-rod pumping wells based on multi-view learning and Hessian regularization
摘要
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 meth-ods 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 e-ven much better with a small amount of marked training samples.关键词
抽油机井/工况识别/多视角学习/logistic回归/Hessian正则化Key words
sucker-rod pumping wells/working condition recognition/multi-view learning/logistic regression/Hessian regu-larization分类
能源科技引用本文复制引用
周斌,王延江,刘伟锋,刘宝弟..基于Hessian正则化多视角学习的抽油机井工况识别新方法[J].中国石油大学学报（自然科学版）,2018,42(3):154-161,8.基金项目
国家自然科学基金项目(61671480) （61671480）
