JoWUA

Abstract

One of the conditions for the petroleum (gas and oil) industry's continued existence in the future may be a continuation of the trend towards fewer personnel at offshore installations and more data flow between the two locations. Malicious attacks, also known as security attacks, often target offshore gas and oil installations. These attacks can trigger a series of events such as the issue as well as spread of harmful materials and/or fires, blasts as well as energy, resulting in harm to people, the environment, and property. These effects might be just as devastating as those from big accidents caused by more traditional safety-related factors. For evaluating the achievement of physical security attacks probability, the current research uses a Bayesian Network (BN) technique that combines Natural Language Processing (NLP). The process also considers the single structure of the offshore gas and oil sector. In order to dynamically conduct risk assessments for safety and security, Bayesian networks are quickly becoming a popular tool. These networks adjust the previous disaster probability values to account for original data. Another benefit of Bayesian networks is their ability to describe conditional dependency between events, as well as their handling of variables with many states.