Research On The Method Of Detection Of Reused Oil Tubing Thread Tolerance Image

Oil tubing is the biggest consumables in oil drilling, the tubing’s quality plays an important part in the production and safety of oil industry. Reused tubing is one tubing which is used many times, but the quality of the tubing is poor, fastening, leakage, slippage and fracture phenomena frequently occurred in many oilfields, and cause great losses. According to statistics,95%tubing failure occurs in the thread connections. At present, the tubing thread parameters detection rely mainly on the artificial means, its detection efficiency is low, has more error, and easy to be influenced by human factors. Therefore, studying a kind of intelligence, efficient tubing thread parameters measure system has important significance.After analyzing the tubing thread parameters detection system at domestic and abroad, this paper studies a tubing thread sentenced to waste system based on digital image processing, and expounds the thread image acquisition and development of processing system and the realization of the technology. The linear array camera’s parameters are debugged through collecting large image. Adaptive filtering method are confirmed.In image enhancement aspects,the contrast enhancement,histogram strengthens and wavelet method are mainly analysed. Various edge detection operators are analyzed and compared. Thread taper, therad angle, thread pitch, thread height,and medium size are regarded as recognition of the characteristic parameters according to the API standard. The definition of various parameters and their calculation method are given, and this paper puts forward transformation method from screen pixel to the actual size.In intelligent sentenced to scrap,neural network is introduced,and its network parameters are obtained through experiments.This paper realized the image acquisition,image processing,parameters detection, sentenced to scrap, and other functions with Microsoft Visual Studio2005platform, using VB.net,SQL Server and MATLAB, and established the tubing thread database.