Oil pumping well semi-supervised fault diagnosis method based on curvelet transformation and kernel sparsity

Abstract

The invention relates to an oil pumping well semi-supervised fault diagnosis method based on curvelet transformation and kernel sparsity. The oil pumping well semi-supervised fault diagnosis method comprises the steps that data of a plurality of indicator diagrams are obtained to serve as training samples; the indicator diagrams are converted into downhole pump indicator diagrams, and all the pump indicator diagrams are converted into grey images; all the pump indicator diagrams are subjected to curvelet transformation to obtain a coefficient matrix; feature vectors of all the pump indicator diagrams with labels are used as a dictionary, and sparse coefficients of all unlabelled pump indicator diagram feature vectors are evaluated; virtual tags of all the pump indicator diagrams with no label are calculated by means of the sparse coefficients; the feature vectors of all the pump indicator diagrams in the training samples are used as a dictionary; feature vectors of all to-be-diagnosed test samples are calculated to evaluate sparse coefficients; and fault types are diagnosed by calculating virtual tags of the to-be-diagnosed samples by means of the sparse coefficients. The oil pumping well semi-supervised fault diagnosis method can precisely describe features of the pump indicator diagrams, and a semi-supervised sparse expression classifier based on the method can effectively utilize information of unlabeled data and has a low requirement for the number of labeled samples.

Description

technical field [0001] The invention relates to a fault diagnosis technology for pumping wells, in particular to a semi-supervised fault diagnosis method for pumping wells based on curvelet transform and kernel sparseness. Background technique [0002] In the actual oil extraction process, the production environment of the commonly used rod pump oil well system is relatively harsh, the failure rate is high, and it often fails to work normally. Various failures can lead to serious consequences such as reduced oil production, well shutdown and even equipment damage. In traditional oil production, it is generally relied on experienced technicians to manually analyze the collected uphole dynamometer diagram or the transformed downhole pump dynamism diagram to judge whether the system is currently working normally. This method is inefficient and subject to subjective influence, and cannot meet the production needs of enterprises. Therefore, it is a very meaningful technology to...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies in the fault diagnosis method of the rod pump oil well system in the prior art, such as a large amount of unused unlabeled data, which cannot be well combined with the actual production situa

Images