Reliability assessment on natural gas pressure reduction stations using Monte Carlo simulation (MCS)

Abstract

Gas pressure reduction stations play a key role in the timely and safe supply of natural gas (NG) to residential, commercial, and industrial customers. Accordingly, system reliability analysis should be performed to prevent potential failures and establish resilient operations. This research proposed a reliability assessment approach to natural gas pressure-reducing stations using historical data, statistical analysis, and Monte Carlo simulation (MCS). Historical data are employed to establish the probability distributions of the system and subsystems in gas stations. Then the Kolmogorov-Smirnov test is conducted to assess the goodness-of-fit for the developed distributions. Bayesian network (BN) is utilized to develop a system failure causality model. Finally, we performed MCS to precisely predict the failure rate and reliability of stations and all subsystems, such as the regulator, separator and dry gas filters, shut-off valves, and regulator. This research provided numerical findings on the reliability indicators of pressure reduction stations which can be used to improve system performance and, subsequently, the resilience of NG pipelines.