Abstract
The dynamometer card (DC) combined with computer-aided intelligent diagnosis technology (pattern recognition) is an essential and useful means to monitor and diagnose working conditions of oil wells. However, the difficulty to collect the faulty DC album and the property differences between wells results in the decrease of diagnosis accuracy. In this paper, a novel approach regarding generating DCs is proposed based on the analysis of the mechanism of a sucker rod pump at normal and several faulty scenarios. According to the simulating DCs and Back Propagation Neural Network (BPNN), a self-evolution diagnosis method is presented to obtain higher diagnosis accuracy. The results of field application show that the average accuracy of the self evolution diagnosis method is improved from 84.6% to 93.1% compared with the original diagnosis method. And the new method is more effective for oil wells with high pumping speed or containing flexible sucker rods.
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