Abstract
Accidents on petroleum installations can have huge
consequences; to mitigate the risk, a number of safety barriers are devised. Faults and unexpected events may cause barriers to temporarily deviate from their nominal state. For safety reasons, a work permit process is in place: decision makers accept or reject work permits based on the current state of barriers. However, this is difficult to estimate, as it depends on a multitude of physical, technical and human factors. Information obtained
from different sources needs to be aggregated by humans, typically within a limited amount of time. In this paper we propose an approach to provide an automated decision support to the work permit system, which consists in the evaluation of quantitative measures of the risk associated with the execution of work.
The approach relies on state-based stochastic models, which can be automatically composed based on the work permit to be examined.
consequences; to mitigate the risk, a number of safety barriers are devised. Faults and unexpected events may cause barriers to temporarily deviate from their nominal state. For safety reasons, a work permit process is in place: decision makers accept or reject work permits based on the current state of barriers. However, this is difficult to estimate, as it depends on a multitude of physical, technical and human factors. Information obtained
from different sources needs to be aggregated by humans, typically within a limited amount of time. In this paper we propose an approach to provide an automated decision support to the work permit system, which consists in the evaluation of quantitative measures of the risk associated with the execution of work.
The approach relies on state-based stochastic models, which can be automatically composed based on the work permit to be examined.