Diagnosis of sucker rod pumping based on dynamometer card decomposition and hidden Markov model

Abstract

In oil field production, dynamometer card is the key source of information to analyze the down-hole operating conditions of sucker rod pumping. However, under different operating conditions, most of the existing diagnostic technologies are incapable to extract features from dynamometer cards automatically and comprehensively. Based on the mechanism analysis of dynamometer card, a useful diagnostic method with novel feature extraction method is proposed for diagnosing the operating condition of sucker rod pumping. A novel barycentric decomposition strategy is applied to divide the dynamometer cards, which can automatically adjust divided regions according to the shape change of dynamometer card. The curve moments are extracted from the parts of divided results to obtain the comprehensive features for the follow-up algorithm. Subsequently, the hidden Markov model with mixture density function is designed as a classifier to map the relationship between the operating condition and the features of dynamometer card. This technique is successfully carried out in a fault dynamometer card atlas that is collected from productive field. Finally, the experimental results confirm the validity of the proposed diagnosis approach.

Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

“…He also compared the results with the random forest and K-nearest neighbors (KNN) algorithms. In 2018, Zheng and Gao [ 23 ] diagnosed downhole cards via decomposition and hidden Markov model; Zhang and Gao [ 1 ] used the fast discrete curvelet transform as dynamometer cards descriptors and sparse multi-graph regularized extreme learning machine (SMELM) as the algorithm; Zhou et al [ 24 ] proposed a classification model based on Hessian-regularized weighted multi-view canonical correlation analysis and cosine nearest neighbor multi-classification for pattern detection; finally, Ren et al [ 25 ] highlighted successful results when proposing root-mean-square error (RMSE) for card classification.…”

Fault Diagnosis of Sucker Rod Pump Based on Deep-Broad Learning Using Motor Data

“…Zhang and Gao [10] designed a transform matrix to transfer DC data from different wells to the same subspace. Zheng et al [11] explored the characteristic parameters of the typical fault DCs, and employed the hidden Markov model for sucker rod pump system diagnosis. Li et al [12] adopted an online sequential extreme learning machine (OS-ELM) to update parameters in real time, and realized continuous monitoring of downhole conditions.…”