Hessian-regularized weighted multi-view canonical correlation analysis for working condition recognition of sucker-rod pumping wells

Abstract

In order to more accurately recognize and understand the working condition of sucker-rod pumping wells so as to maximally reduce the cost and increase the profit, a large amount of data has been collected during oil production with sucker-rod pumping wells. In view of the sucker-rod pumping production system in big data and IOT (Internet of things) of oil-gas production, to solve the limitations in the existing working condition recognition research and further improve the recognition accuracy and practicality with fewer labelled working condition samples by utilizing the measured parameters from multiple information sources effectively, in this paper, a novel working condition recognition method based on Hessian-regularized weighted multi-view canonical correlation analysis is proposed. Firstly, the features of the measured ground dynamometer cards, electrical power, wellhead temperature and wellhead pressure data are extracted as four different feature views based on the prior information, empirical knowledge and mechanism analysis. Then a model based on Hessian-regularized weighted multi-view canonical correlation analysis and cosine nearest neighbour multi-classification algorithm is established. The proposed method is applied to the recognition of eleven kinds of working conditions from sixty sucker-rod pumping wells in a certain block in Shengli Oilfield, China. In the case where there are small number of labelled training samples, based on cosine nearest neighbour classification method, the recognition rates are increased by 3.44% and 1.5% compared with traditional recognition methods based on measured ground dynamometer cards and electrical power data, respectively. In contrast to methods based on traditional multisources of feature connection, multi-view canonical correlation analysis as well as the unweighted Hessian-regularized multi-view canonical correlation analysis, the recognition rates are increased by 4.46%, 2.21% and 1.62%, respectively.

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.…”