Oil casing shaft casing damage image feature pattern recognition method

Abstract

The invention discloses an oil casing shaft casing damage image feature pattern recognition method, comprising the following steps: (1) inputting a casing damage image; (2) converting the casing damage image from the two-dimensional space to the three-dimensional space, and dividing data points in the three-dimensional space into data points with damage and corrosion and data points without damageand corrosion according to the data threshold value of data points in the three-dimensional space; and (3) judging the type of damage corrosion according to the set of data points with damage corrosion. In the invention, a two-dimensional image corresponding to the casing damage image is converted into an image in a three-dimensional virtual space, the image is formed into corresponding data setsin the three-dimensional space; and by constraint of the distance threshold between points and the numerical value threshold of points in the image, in combination with the limitation of the maximumperpendicular distance allowed by image search, the data sets are divided into sub-type data sets representing different corrosion pattern features, and different corrosion pattern features correspondto different corrosion shapes, so that the corrosion types of the sub-type data sets can be accurately evaluated and recognized.

Description

technical field [0001] The invention relates to a three-dimensional virtual space-based feature pattern recognition method for casing damage images of tubing and casing, in particular to a method for characterizing damage and corrosion of the inner wall of tubing and casing based on the data of multi-arm caliper imaging logging tools and magnetic wall thickness logging tools. The image and the image representing the remaining wall thickness of the oil casing, combined with the constraints of the distance measurement threshold between points and the point value threshold in the virtual three-dimensional space data set, match the standard feature patterns corresponding to the shapes of several corrosion types, so as to accurately identify and characterize the oil casing Effects of ring corrosion, line corrosion, sheet corrosion and spot corrosion in the image of damaged corrosion on the inner wall of the pipe and the image of the remaining wall thickness of the tubing and casing....

AI Technical Summary

Problems solved by technology

When evaluating the two-dimensional images of borehole diameter penetration rate and wall thickness remaining rate of the inner wall of oil and casing, foreign counterparts further distinguish the shapes of different damage and corrosion types, and judge the degree of damage and corrosion of different types of shapes, but Since it does not disclose the technology utilized, there is no reference to improve it

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