A case study on fuzzy logic-based risk assessment in oil and gas industry

Risk assessment is a process of categorizing and measurement of risk related outcomes from a specific incident and in a particular scenario. While risk itself is considered as the combination of likelihood and severity of the consequences of hazards. Typically, the qualitative approach of risk based inspection (RBI) is applied in oil and gas industries to measure the risk levels of hazards. But with this qualitative approach sometime the risk ranking ties among the different factors can lead to problem in selecting the most critical factor. To address the problem, this study aims to develop a fuzzy logic-base risk assessment model using a quantitative approach of RBI that will assist to mitigate the risk ties in risk ranking process of hazard. In this proposed model, fuzzy membership functions and ranges have been assigned for likelihood, severity of consequences and for total risk levels. A case study on ammonia hazard is presented to demonstrate the vitality of the proposed fuzzy risk assessment model with samples of four categories (people, environment, asset and reputation) from an oil and gas industry. The outcomes of this study indicate that the developed model has a strong potential application in oil and gas industry in assessing the severity levels of risk, and resolving risk ranking ties. © 2006-2016 Asian Research Publishing Network (ARPN).