Objectives. Human factors have been identified as the most common causes of catastrophic accidents in the oil and gas industry. Therefore, this study aims to analyze human causal factors of accidents in the oil and gas industry using the human factors analysis and classification system for the oil and gas industry (HFACS-OGI) framework. Methods. This study involved quantitative data collection for 184 accident cases in the oil and gas industry that occurred from 2013 to 2017 from the International Association of Oil and Gas Producers (IOGP) database. The causal factors of these accidents were coded using the HFACS-OGI framework. Accident data were analyzed using descriptive statistics and the χ 2 test. Results. Study findings reveal that 23% of all accidents were recorded in 2013. Thirty-two percent of accidents occurred in Asia, while 69% of accidents were recorded in onshore locations. Contractors were involved in 86% of accidents, while 28% of accidents occurred during drilling, workover and well services. The contractor's work environment was the main human factor in 90% of accident cases. Conclusion. The HFACS-OGI framework proves to be a vital tool for robust accident analysis of human factors in the oil and gas industry.

Human factors and process safety management (PSM) have become key factors in preventing exposure to both hazardous materials and major accidents. Therefore, comprehensive process safety management is required to address all aspects of human factors. Currently, there are several‐process safety management models all of which have some weaknesses with respect to the control of human factors inherent in the process industry. Moreover, there is as yet no universally accepted process safety management model that treats process safety management as an integral part of the management system. Therefore, a need has arisen to integrate human factors and the existing frameworks and models into a single integrated management system to ensure a holistic approach of control and a continuous learning system. This article identifies the missing human factors in the current system and describes an integrated process safety management system (IPSMS) model drawn from screening all existing PSM frameworks, while integrating the Human Factors Analysis and Classification System (HFACS). The model, which adopts the PLAN, DO, CHECK, and ACT framework, also outlines an implementation strategy. We conclude that IPSMS provides both a theoretical and a practical framework with which to manage, measure and analyse process safety management systems. © 2017 American Institute of Chemical Engineers Process Process Saf Prog 37:67–85, 2018

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Historically, fire incidents in high-rise buildings reveal that Fire and Rescue Services frequently rely on the stay-put tactic (i.e., occupants of high-rise buildings should remain in their apartments) during an inferno. Recent fire occurrences in high-rise buildings reveal that there are two opposing viewpoints on the stay-put tactic. First, the understanding that the stay-put tactic is a beneficial practice used to protect, control, and facilitate smooth evacuation of occupants during fire incidents. Second, the argument that the stay-put tactic is a misjudgement and futile strategy that leads to fatalities, particularly in high-rise buildings. The aim of this study was to provide awareness and understanding of fire and rescue services use of the stay-put tactic in high-rise buildings. We attempted to answer the questions: is the stay-put tactic a misjudgement or magnificent strategy? The study adopted phenomenological research strategies with various focus groups consisting of seasoned firefighters and survivors with first-hand accounts of stay-put instructions in high-rise buildings. The study also scrutinised three case studies of fire incidents in high-rise buildings in two countries. The study revealed that the stay-put tactic is obsolete; with the potential to cause catastrophic misjudgement, mostly during conflagrations in high-rise buildings. There is a need to advance research on the use of artificial intelligence communication systems and infrared image detectors camera to enhance quick and smooth fire evacuation in high-rise buildings.