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| Working conditions diagnosis of surface-driving progressive cavity pump wells based on support vector machine |
| Liu Guangdong |
| Dongsheng Jinggong Petroleum Development Co.Ltd., Shengli Oilfield, Dongying, Shandong 257000, China |
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Abstract The frequent faults of surface-driving progressive cavity pump(PCP)wells limit their further development. In order to improve the economic benefits and management level of surface-driving progressive cavity pump wells, a diagnosis method of working conditions of surface-driving PCP well based on support vector machine was proposed. The condition types of PCP wells were subdivided into 10 categories as outputs, and 8 variables were selected as inputs which can represent operation situation of oil wells. Sample sets were established based on the already existing failure oil wells of Jinjia oilfield in Dongsheng group company, and 45 classifiers were built using voting method. Best C and g were determined by three methods including grid optimization, genetic algorithm and particle swarm optimization. The Libsvm toolbox called by Matlab was used to establish and train the SVM model, and 15 PCP wells of Jinjia oilfield in Dongsheng group company were diagnosed to verify the SVM model, and the comparison between support vector machine and artificial neural network was conducted. The results show that the diagnosed working conditions of 14 PCP wells by the SVM method are in accord with their actual working conditions, with an accuracy of 93.33%. Compared to artificial neural network(88.9%), the SVM is more accurate, which is superior for small sample problems, and is a feasible diagnosis method for PCP wells.
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Received: 11 November 2013
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