Evaluation of Uniform Corrosion Defects of Oil-Gas-Water Gathering Pipeline Based on Radial Basis Function Artificial Neural Network Prediction Model

The uniform corrosion rates of oil-gas-water gathering pipeline under different working conditions were measured by a multiphase dynamic corrosion detection device. A radial basis function artificial neural network prediction model was established using the data from above mentioned experiment as samples. The average corrosion defects of the pipeline were also evaluated. The results show that 9-29-1 radial basis artificial neural network had a reasonable structure and good accuracy when hydrogen sulfide content, carbon dioxide content, water content, calcium ion content, magnesium ion content, chloride ion content, temperature, pressure and flow rate were used as input signals and uniform corrosion rate was used as output signal. A mean square error of 0.000 9 lower than the specific convergence tolerance of 0.001 0 was obtained after 4 450 iterations. The coefficients of determination of linear fitting in training, verification and testing stages were 0.993, 0.973 and 0.969, respectively, demonstrating high relevance between the predicted values and the desired values. Meanwhile, according to the model, 9 sections of pipelines with uniform corrosion risk were found.