Fault diagnosis method of sucker-rod pump well

Abstract

The invention relates to a fault diagnosis method of sucker-rod pump well; the method comprises the following steps: acquiring ground dynamometer cards of the known and to-be-diagnosed sucker-rod pump wells; converting the ground dynamometer card of the suck-rod pump well into a suck-rod pump well dynamometer card; redrawing a boundary chain code of a normalization processed pump dynamometer card by 16-direction chain codes; zoning the pump dynamometer card which is redrawn with the boundary chain code, and extracting the characteristic vector of each zone; clustering the characteristic vectors of the quantified pump dynamometer card by spectral clustering algorithm based on particle swarm optimization, so as to finish the fault diagnosis of the suck-rod pump well; the fault diagnosis method of sucker-rod pump well uses the number of the 16-direction chain codes to quantify the characteristic vectors of each pump dynamometer card, so as to effectively and precisely describe the pattern characteristics; the fault diagnosis is achieved by clustering the quantified pump dynamometer card characteristic vectors with spectral clustering algorithm based on particle swarm optimization, without depending one training samples, thus increasing the performance of the diagnosis.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of pumping wells, and in particular relates to a fault diagnosis method for rod pump pumping wells. Background technique [0002] Rod pump pump dynamometer diagram plays a very important role in oil field production, and it is the main basis for judging the downhole working status of the pumping system. The pump dynamometer diagram can be used for fault diagnosis of downhole production conditions, and can also be used to calculate the fluid output of the wellhead, and can also be used to calculate the position of the downhole dynamic fluid level. In traditional production, technicians generally rely on manual analysis of the collected pump dynamometer diagrams to judge the actual working status of the pumping system. This manual method has a long working cycle and is greatly affected by subjective factors. It is increasingly unable to meet the needs of enterprises to continuously improve production...

AI Technical Summary

Problems solved by technology

This supervised learning method mainly has the following two problems: first, the accuracy of diagnosis largely depends on whether the sample feature vector of the training sample set can accurately reflect the typical characteristics of the sample; second, this supervised method It mainly relies on artificially dividing the training sample set, which is easily affected by subjective factors.

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