Research On Analyzing The Cone Bit Wear Condition Based On Wavelet Transformation

Drilling quality, drilling efficiency and drilling cost are closely linked with the drill bit wear condition. However, because of the complexity of the well bottom, it is difficult to accurately judge the wear condition of bit. If the bit is taken out ahead of time, it will cause waste of manpower and resources. And if too late, it will seriously affect the drilling efficiency. Therefore, it is necessary to accurately and timely judge the wear condition of bit, in order to avoid drilling accidents, improve drilling quality and drilling efficiency, reduce drilling costs.Aiming at the problem of the drill string vibration signal containing a large number of time-varying, short-term impact, sudden nature noise signal, the paper presents the principle of wavelet transformation denoising and time-frequency feature extraction method. Firstly, using the wavelet packet denoising method denoise the collected drill string vibration signal, then making use of the time-frequency method extract the characteristics of the denoised vibration signal, extracting the bit wear sensitive feature set, as the fit inputing feature vector of the BP network then input BP network to get finally result after the analysis of the network.Based on Lab VIEW platform, I developed the tri-cone bit wear degree diagnosis system, realizing the tri-cone bit wear degree diagnosis. The system includes signal input module, the signal processing module, the BP network diagnostic module and record query module, introducing the process of design, operation instruction, and interface of each function module detailedly. Through on-site data processing and analysis, it shows that the system can meet the needs of test and diagnostic drill vibration signals, providing an important theoretical basis for the determination of the trip time, and providing important theory and practice foundation for the use and management of tri-cone bit.