Cognitive Models for Human Reliability Analysis Applications to the Oil & Gas Industry

Abstract

Abstract In the quantitative risk analysis (QRA) of O & G equipment, it is necessary to consider the application of cognitive models (in use in the nuclear industry), which can very effectively predict human performance. First generation Human Reliability Analysis (HRA) methods are used in O&G primarily in an emergency scenario QRA and second-generation methods for design and normal operation. These methods can be inefficient. First generation HRA models do not have a cognitive architecture capable of processing human information. Second-generation models consider mechanisms of cognitive errors. Most of third generation HRA models are dynamic simulations of cognitive tasks, coupled with an event/accident simulation code, often designed more for human-machine interface assessment than for human error analysis. There are also cognitive psychology models designed to assess human performance without the intention of serving as HRA tools. These models have a detailed understanding of the cognitive mechanisms of human error, which is the key to a complete HRA model. One of these models, ACT-R, contains adequate quantification to serve as an HRA model. This article discusses cognitive psychology models and their internal structures in human information processing to provide human error probabilities that arise from the architecture itself and are not dependent on Performance Shaping Factors (PSFs), which are quantified by HRA current methodologies.