EnKF-based estimation of natural gas release and dispersion in an underground tunnel

Abstract

With the rapid industrialization and urbanization in China, underground tunnels, as an effective way to construct and maintain energy supplies, have been widely utilized to transport natural gas in urban chemical plants and industrial parks. Natural gas transport in an underground tunnel really facilitates the repair and maintenance of gas pipelines, but could pose a high risk of causing fire or explosion in case of natural gas pipeline leakage. The reasonable estimation of natural gas leakage and subsequent spatiotemporal distribution of natural gas dispersion is of great importance for effective emergency response and loss prevention. In this study, a numerical gas transport model in conjunction with the ensemble Kalman filter (EnKF) was proposed to realize the inversion of natural gas leakage rate and predict gas dispersion in a ventilated underground tunnel. The EnKF-based model can improve gas release estimation and dispersion prediction by assimilating observation data into simulation results. The proposed model-data assimilation system was firstly validated by twin experiment, and furthermore the sensitivity of the initial-guess release rate was analyzed to verify the practicability of the proposed model. Moreover, the reasonable estimation of dynamic natural gas release rate was realized and strategic decisions on methane sensors arrangement in tunnel were provided. The reasonable results indicate that the proposed EnKF-based model is an effective method for estimating natural gas release rate and predicting real-time natural gas spatiotemporal distribution in case of natural gas release accident in an urban underground tunnel.