Sucker rod pumping diagnosis using valve working position and parameter optimal continuous hidden Markov model

Down-hole operating condition diagnosis based on dynamometer card is a key subject for sucker rod pumping in oil extraction engineering. In this technology, feature extraction and diagnostic model are two indispensable elements. To accurately and automatically diagnose the operating condition by computer, a novel diagnostic method for sucker rod pumping is proposed. The first novel idea is to extract seven geometric features, which are obtained from dynamometer card using barycentric decomposition algorithm and valve working position. The second novel idea focuses on the use of continuous hidden Markov model (CHMM) to create classifiers for diagnosing the down-dole operating conditions and then clonal selection algorithm (CSA) is used to optimize the selection of initial parameters for CHMM. Finally, the proposed method is tested on an oil field dynamometer card set. Furthermore, this technique is compared with some other existing approaches. The simulation results demonstrate that the performance using the method proposed in this paper is satisfactory.