Smart technologies can improve safety in the oil and gas industry
By Gaurav Sharma, Head of Industries Business, ANZ, Cognizant
Employee safety remains a key concern within the oil and gas sector. The nature of tasks and activities makes it a riskier industry than others. Workers’ fatigue is another factor increasing that risk.
However, the oil and gas industry is in a prime position to take advantage of technologies such as robotics, wearables, artificial intelligence (AI) and machine-learning (ML) to minimise some of the risks, as well as drive innovation.
The risks of fatigue
Fatigue is a major contributor to safety incidents in the oil and gas industry. An oil and gas worker’s job includes inherently risky routine and physically demanding tasks such as positioning heavy equipment, climbing on scaffolds, and physical inspections and maintenance.
Characteristics specific to the oil and gas industry are also factors increasing that risk, such as fly-in fly-out and shift work conditions, dealing with time zone changes and 24/7 operations, handling hazardous chemicals or working in extreme weather conditions.
Research indicates that the injury rate of Australian shift workers is two times higher than other workers, due to fatigue-related factors such as insufficient sleep, disrupted sleep patterns, and extended working hours.
Using digital technologies to predict fatigue risk
There is an increasing awareness across risky industries that digital technologies have an important role to play in improving work safety. Leading oil and gas providers are now looking to improve their current standards even further through data collection, monitoring, and automation.
However, there is still a long way to go to full adoption, as estimates suggest that just one percent of the data currently captured by operators is being used for improvement purposes.
These technologies can provide companies with better control on workplace hazards and better training, while improving onsite communications and overall workforce and business asset protection.
For instance, wearables measuring workers’ vitals with their informed consent can provide insights into their level of fatigue and avoid letting an exhausted asset manage risky processes. This understanding can also be used to improve existing control mechanisms and establish new proactive controls that further enhance worker health and safety.
There are many use cases where technologies can help improve working conditions and mitigate risk. Drones can help remotely monitor a situation and/or access dangerous places before involving humans in the process, like in the Notre-Dame incident. By enabling data-based insights into the most common causes of specific types of incidents, AI can help determine whether systemic conditions cause certain accidents.
Machine learning can help identify patterns or equipment failures, allowing a proactive, rather than a reactive, approach to anomalies. The use of augmented reality in field training can allow workers to get familiar with new equipment or environments without taking any risks.
However, before implementing any technology, organisations should embrace proactive risk management by developing a framework based on three key milestones: identification, analysis and recommendation.
The first step is to identify and categorise different types of incidents happening onsite and establish their impacts on costs and productivity. To do this effectively, organisations should develop an incident classification matrix tailored to their work environments.
The next step is to analyse the issues to determine root causes and potential gaps causing them. The specific risks will vary within the industry, so a tailored matrix is paramount. Finally, based on the analysis, the organisation should come up with actionable recommendations to contain the threats.
Managing workers’ fatigue within the oil and gas industry is not a simple process, but with the right approach, methods and tools, it can be more effectively controlled and monitored to help prevent incidents and risks.