Bayesian-lopa methodology for risk assessment of an LNG importation terminal

摘要

LNG (Liquefied Natural Gas) is one of the fastest growing energy sources in theU.S. to fulfill the increasing energy demands. In order to meet the LNG demand, manyLNG facilities including LNG importation terminals are operating currently. Therefore,it is important to estimate the potential risks in LNG terminals to ensure their safety.One of the best ways to estimate the risk is LOPA (Layer of Protection Analysis)because it can provide quantified risk results with less time and efforts than othermethods. For LOPA application, failure data are essential to compute risk frequencies.However, the failure data from the LNG industry are very sparse. Bayesian estimation isidentified as one method to compensate for its weaknesses. It can update the generic datawith plant specific data.Based on Bayesian estimation, the frequencies of initiating events were obtainedusing a conjugate gamma prior distribution such as OREDA (Offshore Reliability Data)database and Poisson likelihood distribution. If there is no prior information, Jeffreysnoninformative prior may be used. The LNG plant failure database was used as plantspecific likelihood information. The PFDs (Probability of Failure on Demand) of IPLs (Independent ProtectionLayers) were estimated with the conjugate beta prior such as EIReDA (EuropeanIndustry Reliability Data Bank) database and binomial likelihood distribution. In somecases EIReDA did not provide failure data, so the newly developed Frequency-PFDconversion method was used instead. By the combination of Bayesian estimation andLOPA procedures, the Bayesian-LOPA methodology was developed and was applied toan LNG importation terminal. The found risk values were compared to the tolerable riskcriteria to make risk decisions. Finally, the risk values of seven incident scenarios werecompared to each other to make a risk ranking.In conclusion, the newly developed Bayesian-LOPA methodology really doeswork well in an LNG importation terminal and it can be applied in other industriesincluding refineries and petrochemicals. Moreover, it can be used with other frequencyanalysis methods such as Fault Tree Analysis (FTA).