Abstract
As in any shiftworking industry, fatigue poses a hazard to workers within the oil and gas industry, irrespective of job role and site location. In order to demonstrate adherence to health and safety regulations, operators can be expected to manage shift patterns and hours of work, such that they are appropriate and do not result in unnecessary levels of fatigue that may reduce the safety of the operation. Reliance on hours of work limitations (for example the European Working Time Directive) or industry normalised working patterns may no longer be considered sufficient to ensure that the risk posed by fatigue is appropriately managed.
This paper presents how a scientific approach can be applied and adapted to suit the context and the populations being studied in order to answer specific operational questions and provide tailored fatigue risk mitigations. It describes a method by which site and job role fatigue levels can be assessed, in order for appropriate controls to be implemented. It will use case studies to illustrate how data collection methods are tailored to reflect specific operational environments. Data collection is particularly important in parts of the industry where common shift arrangements differ from those which have historically been studied.
The method outlines an approach to rigorously assess contributors to fatigue and fatigue levels in an organisation following appropriate scientific methods. Both subjective and objective data are collected, using methods such as fatigue and sleepiness scales, sleep diaries and collection of objective sleep data using validated sleep tracking devices. The approach is specifically tailored to the population of interest – reflecting their shift pattern, and collecting further data on workload, task demand, and operational or location-specific factors (for example travel to site, onsite sleeping facilities, or sea sickness on floating platforms). The method also allows for inferences to be made about the impact of circadian misalignment and shift timing on sleep, performance and mood.
The method presented in this paper has been used in field data collection in two very different environments. These studies are used as case studies to examine how the methodology can be tailored to ensure that the collected data are appropriate to the operation being studied, and lessons learned to improve the methods in the future.