Refineries are highly complex installations and a potential source of major hazards. Due to the large volumes of flammable and toxic substances present, an accident in a refinery may have multidimensional consequences. This includes severe property damages, injuries to personnel, toxic releases of chemicals causing adverse health effects on nearby residents and the environment, and large business interruption losses that may lead to company bankruptcy. This paper looks at the risk profile of refineries from an insurers’ perspective. A top down approach is employed to derive key performance indicators (KPIs) for two types of events historically known as main causes of major accidents in refineries, i.e. fire and vapor cloud explosion. Bayesian Belief Networks (BBNs) are used to develop a probabilistic model for quantifying risk indication of refineries for fire and explosion events via a structured approach to elicit and synthesize available knowledge from domain experts. Three types of KPIs are modelled as BBN nodes: quantitative, qualitative and directional indicators linked to technical, human and change trend factors, respectively. The approach proposed has a twofold practical use: i) to support insurers to assess which plants have low potential risk exposures; and ii) to inform the refineries about their own risk profile, thus supporting them with the assessment and the implementation of risk reduction measures. To ensure applicability across the industry, the systematic development of the BBN is detailed and extension via the inclusion of modules accounting for further KPIs is discussed.

炼油厂是高度复杂的设施，是重大危险的潜在来源。由于存在大量易燃和有毒物质，炼油厂发生事故可能会产生多方面的后果。这包括严重的财产损失、人员受伤、化学品的有毒释放对附近居民和环境的健康造成不利影响，以及可能导致公司破产的大量业务中断损失。本文从保险公司的角度审视炼油厂的风险状况。采用自上而下的方法为历史上称为炼油厂重大事故的主要原因的两类事件（即火灾和蒸汽云爆炸）得出关键绩效指标 (KPI)。贝叶斯信念网络 (BBN) 用于开发概率模型，通过结构化方法从领域专家那里获取和综合可用知识，以量化炼油厂火​​灾和爆炸事件的风险指示。三种类型的 KPI 被建模为 BBN 节点：分别与技术、人力和变化趋势因素相关的定量、定性和方向指标。所提议的方法有双重实际用途：i) 支持保险公司评估哪些工厂的潜在风险敞口较低；ii) 告知炼油厂他们自己的风险状况，从而支持他们评估和实施风险降低措施。为确保适用于整个行业，