Research on Apparent Resistivity Imaging of Transient Electromagnetic Method for Oil and Gas Pipelines Based on GA-BP Neural Network

Abstract

Transient electromagnetic apparent resistivity imaging technology is one of the more promising methods for external inspection of metallic oil and gas pipelines. Through the research on the transient electromagnetic response and imaging technology of pipelines, it is found that the accuracy and real-time performance of the apparent resistivity calculation are the key to its application. To achieve fast imaging, a three-layer BP neural network is designed with the kernel function of the secondary field as the input and the transient parameter value as the output; the nonlinear equation of transient response is fitted by the neural network to solve the apparent resistivity, and inversion depth is calculated based on smoke ring theory. Aiming at the shortcomings of the traditional BP network, such as slow convergence rate and the ease of falling into local minima, the genetic algorithm is designed to optimize the initial weight and threshold of the network. In the model pipeline experiment, the measured data are brought into the trained GA-BP network, and calculation time is greatly shortened. The obtained sectional image can directly and accurately reflect the pipeline shape. The validity and practicability of the transient electromagnetic apparent resistivity imaging technology based on the GA-BP neural network are verified, which is expected to be a powerful tool for real-time evaluation of pipeline corrosion detection.