Intelligent Detection Of Casing Damage Image Of Oil And Gas Well Based On Cluster And Regression

The casing damage of oil and gas Wells is urgently needed to solve in the process of oil and gas well development one of the important problems,as the growth of the exploitation of fixed number of year,all the fields of oil and gas well casing in China due to the time factor,engineering and geological factors,squeezed by high frequency and high strength,corrosion and wear and lead to casing damage is serious,the case of damage.However,the sample distribution of the image data set obtained by visible TV logging technology is unbalanced,and the detection method is often manual detection,which leads to poor detection accuracy and low efficiency.In this paper,machine learning theory and image intelligent detection technology are introduced into the image detection and recognition of casing damage in oil and gas Wells,which has a certain guiding significance for image detection of casing damage in oil and gas Wells.(1)Based on the research of machine vision theory,this paper uses machine vision technology to realize the gray scale,noise reduction,binary value and feature extraction of casing images in oil and gas Wells.An improved adaptive threshold binarization algorithm is proposed to solve the problems that the neighborhood size of the adaptive threshold binarization algorithm needs to be manually selected and the smoothing region is poorly handled.The algorithm introduces the aspect ratio of the original image to achieve self-adaptability of the neighborhood.By setting parameters to judge the smooth area and the detail area for binary processing,the algorithm achieves the purpose of self-adaptation of the size of the neighborhood and different processing of the detail area of the smooth area.(2)Based on data set on the basis of the statistical analysis of sample distribution information,for the conventional sampling algorithm is affected by outliers lead to fuzzy classification boundary problems,combined with the density of outliers detection algorithm,is proposed based on improved density detecting outliers comprehensive minority class sampling algorithm,to adjust balance,improve the accuracy of classification of data sets.(3)In this paper,related theories of casing damage detection of oil and gas wells are studied in depth,problems such as unbalanced data sets and low detection efficiency in casing damage detection of oil and gas wells are analyzed in detail,and an improved clustering regression based intelligent detection algorithm for casing damage of oil and gas wells is proposed.By filtering and deleting the unlabeled samples,the algorithm can automatically classify and recognize the data set,and realize the intelligent detection of casing damage images of oil and gas wells.