Research On Some Key Problems Of Oil And Gas Pipeline In-Line Inspection

Oil and gas pipeline in-line inspection is an important mean to ensure pipeline transmission safety. There are many principles of pipeline in-line inspection, and Magnetic flux Leakage (MFL) and ultrasonic method are used widely. Rising electromagnetic acoustic (EMA) technique, as a type of ultrasonic with special excitating principle, attracts highly attention in Nondestructive Testing (NDT) field, because of its need-no-couplant merit. MFL and EMA techniques are researched in this dissertation.Concentrating on the difficult problems that pipe defects are not evaluated quantitatively and intelligently by MFL inspection, pipeline defects MFL inspection technology is analyzed systematically, by theoretical analysis and experiments or testing. Firstly, based on MFL inspection principle, this dissertation presented dipole pipeline defect model based on analytical method, and had analyzed finite-length rectangle defect, infinite-length rectangle defect, and cone defect magnetic flux field distribution. Aimed at the lack of magnetic dipole model, finite-element method (FEM) is applied to defect leak magnetic field analysis, and the distribution of ordinary defect MFL field is simulated. The affecting factors of MFL signal features, such as defect geometry patameters, lift-off value, the speed of testing device, pipe magnetization degree, permanent magnet shape, operating pressure, are researched, then got some important law and offer theorical direction for defect signal compenstation. Research induce coil MFL inspection signal wavelet de-noise method, and explain the way to wipe off the affect of bad channel signals by interpolation. The pattern recognition method of pipe MFL signals is put forward, then girth welds, straight welds and spiral welds recognition is researched based on the feature of typical pipeline accessories practical signals. At the same time, by virtue of recognition of defect parameters, the maximum safe working pressure method is adopted to rank defects. MFL inspection data analysis software for the using pipeline inspection device is designed in this dissertation, to display MFL data and identify, quantize and evaluate pipeline defects and welds. This dissertation researched pipeline defect inspection quantitative analysis problem, applied neural network and pattern recognition methods to MFL defect inspection, and established the defect feature sample store by experiment and simulation method. The application of BP neural network is discussed firstly to extract defect feature, and established a net mapping...