Research on leakage detection technology of natural gas pipeline based on modified Gaussian plume model and Markov chain Monte Carlo method

Abstract

Natural gas pipeline leakage can cause significant economic losses and even jeopardize public safety. Therefore, leakage detection technology is crucial. In this study, the pipeline source term estimation method is proposed to identify the source of natural gas pipeline leakage. The method combines the modified Gaussian plume model, the unmanned aerial vehicle inspection technology equipped with laser methane sensors, and the Markov chain Monte Carlo method based on Bayesian inference. Different from traditional leak detection techniques, this method can simultaneously obtain the location and rate of pipeline leakage. The accuracy of the method is validated by using simulated data from the modified Gaussian plume model, which can be employed to replace experimental tests. Convergence diagnosis and model evaluation are performed on the inversion model in this method, verifying its rationality and feasibility. The impact of system errors on the pipeline source estimation method is analyzed, revealing that the accuracy of inversion can be improved by using prior information and ensuring the reliability of the forward gas dispersion model and monitoring data. The pipeline source term estimation method proposed in this paper can offer technical support for the emergency treatment of natural gas pipeline leakage accidents.