Development of integrated human reliability assessment methods for accident investigation in the oil and gas industry

  • Stephen Theophilus

Student thesis: Doctoral ThesisPhD by Publication


Significant progress has been made in the direction of “engineering out” technological causes of accidents that have characterised the technology age. Inherently safer designs have introduced several ‘layers of protection’ (LOP) or ‘lines of defence’ (LOD) focused on reducing or eliminating technological causes of accidents. It is arguable that human and organisational factors are significant factors behind the majority of the major accidents seen in the process industry today. The main aim of this PhD thesis is to develop a new Human Reliability Assessment (HRA) tool for the analysis of major accidents in the oil and gas industry. This tool will help oil and gas industry accident investigators make sense of accident reports and proffer appropriate remedial action that will stop or reduce reoccurrences. It will also help both employees and employers, policy decision-makers and regulatory agencies to learn lessons which in turn will result in potential cost and time savings. It has investigated all the aspects of human factors (HFs), human errors (HEs) and performance influencing factors (PIFs) related to major accidents in the in the oil and gas industries and proposed an integrated process safety management system (IPSMS) model. The research adopted sequential explanatory and exploratory mixed methods. Each research output began with a review of academic literature and published reports. Major accident reports published by The International Association of Oil & Gas Producers (IOGP), Health and Safety Executive (HSE), World Offshore Accident Database (WOAD),the Process Safety Incident Database (PSID)and the U.S. Chemical Safety Board (CSB) were used for this research. Quantitative data was also collected for internal and external validation of the newly developed Human Reliability Assessment (HRA) tool. SPSS version 25 and case studies were used for statistical and case study analysis respectively, to show the internal validation of the tool or the developed approach. In identifying and describing the different causal factors in 11 reviewed case study accidents, 54 categories of occurrences were identified with the HFACS, but a total of 80 were identified with HFACS-OGI. All industry specific categories which were difficult to identify with the HFACS were successfully identified using the HFACS-OGI. Whilst TRACEr proved to be flexible and adaptable for the oil and gas industry, it proved difficult to use for coding equipment failures. However, TRACEr-OGI was not only able to identify human errors aligned to job context, organisational/facility context and operator context, it was also able to capture equipment error. Numerous process safety management systems (PSM) and frameworks exist. However, no single management system is sufficient in addressing all human factor elements as outlined in HFACS. Consequently, this PhD thesis has identified the missing human factors in the current system and proposed an integrated process safety management system (IPSMS) model. Finally, a risk-culture-based implementation strategy was developed for this model, with recommendations on how to incorporate inherent safety culture and implement a Process Safety Management System in the process industry in a structured way, evolved from the various studies. With the new knowledge and innovative models developed in this PhD project, accident investigators and decision makers are expected to gain a better understanding of human factors (HFs), human errors (HEs) and performance influencing factors (PIFs), as well as process safety management systems in the oil and gas industry. This PhD thesis is a synthesis of theories and concepts about accident causation, a mixture of qualitative and quantitative accident analysis and accident analysis initiatives.
Date of AwardSept 2019
Original languageEnglish
Awarding Institution
  • Coventry University
SupervisorAugustine Ifelebuegu (Supervisor) & Andrew Arewa (Supervisor)

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