Quantitative diagnosis method of the sucker rod pump system based on the fault mechanism and inversion algorithm. (August 2021)

• Record Type: Journal Article
• Authors: Lv, Xiaoxiao; Feng, Long; Wang, Hanxiang; Liu, Yanxin; Sun, Bingyu
• Is Part Of: Journal of process control. Volume 104(2021)
• Page Start: 40
• Page End: 53
• Publication Date: 2021-08
• Subjects: Diagnosis -- Sucker rod pump -- Multiple fault diagnosis -- Dynamometer card -- Optimization inversion; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.06.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Computer-aided fault diagnosis based on the dynamometer card (DC) of the sucker-rod pumping system (SRPS) is a crucial technology to reduce operating costs and increase yield. Currently, the conventional method to implement this technology is the pattern recognition of the DC features. However, the training set of DC that determines the diagnostic accuracy of the method is difficult to obtain because of the differences between oil wells. Moreover, this method can only obtain the type of single fault without quantitative analysis, which may affect the formulation of the adjustment measures. Therefore, in the present study, a quantitative diagnosis method independent of the training data is proposed. In order to obtain the operation process of SRPS under the comprehensive conditions of the fault, a simulation model involving fault effects is established according to the fault mechanism. Subsequently, the framework of the optimization inversion method is established to determine the fault parameters by minimizing the difference between the measured DC and the DC generated by the fault-mechanism model. Then, the strategy of the partition parallel optimization is utilized to improve the stability and efficiency of the inversion algorithm. Meanwhile, fifteen indicating parameters that directly reflect the type and degree of faults are defined. Finally, the proposed diagnosis method is verified experimentally through the data of 20 actual wells. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the types of single and coupling faults, as well as predicting the production rate. Highlights: A novel fault diagnosis process different from pattern recognition and machine learning is presented based on fault-mechanism model and inversion algorithm. A quantitative diagnosis method of sucker rod pump system without training set of dynamometer cards is proposed. The strategies of partition parallel optimization is adopted to improve the computational efficiency of the inversion algorithm. The proposed method is capable of obtaining not only the types of single fault and coupling faults, but also the fault degree and output performance of rod pump system.