Improving Safety and Security Measures for the Oil and Gas Industry with Digital Twins
04/01/2019

Safety and security have always been critical issues for the oil and gas (O&G) industry.

digital twin oil and gas

From common risks…

Fatality rate for the O&G industry workers is almost six times higher than the average rate of all U.S. workers. On an average, 21.6 out of 100,000 O&G workers die on the job. Some analysts opine that the firms undercount injuries by 30% to 60%, which means the fatality figure will go even higher if accurately captured.

The high fatality rate in the O&G industry is due to a range of factors including high fatigue level of workers, fires and explosions, machines operating at height (falling on workers below them), workers working at height, and adverse climatic conditions. In the last decade, more than 300 oil and gas workers died in highway crashes, often attributed to worker’s lower alertness due to high fatigue. In 1988, 167 workers died due to fires and explosions at the Piper Alpha oil field in the North Sea, United Kingdom. As per an industry estimate, equipment falling workers leads to around 22% of oilfield deaths.

Even though firms have made strict safety standards for their operations, these measures have failed to bring any substantial reductions in accidents. Sometimes it’s because workers do not follow safety norms, while other times, safety equipment itself does not work correctly, or engineers fail to notice the malfunctioning of machines.

oil gas risks

To newer threats

In recent years, as O&G firms go digital, cybersecurity has emerged as a new concerning area. Interestingly, ~59% of O&G companies in a survey by Motorola in 2016 did not monitor their infrastructure to spot cyber breaches. Imagine what would happen if hackers changed GPS coordinates of a floating rig or disabled leak detection functions of pipelines?

Experts advocate for more stringent inspections and real-time oversight of O&G operations to bring down the number of accidents and security breaches. However, frequent inspections and real-time monitoring are time-consuming, costly, and even impossible in some situations.

Digital Twins for Safe and Secure O&G Operations

Digital transformation in O&G operations has further accelerated in recent years. About 90% of energy firms will have fully deployed industrial internet of things (IoT) solutions, as per a recent survey by Inmarsat—a global mobile satellite company. The data generated by IoT systems can be used to improve the safety and security of O&G operations.

Digital twin, often referred to as immersive data visualization technology, is emerging as a default analytical platform to visualize real-time or streaming data and processes for the IoT-driven O&G operations. It is a 3D software replica of operations that includes drilling rigs, pipelines, refineries, and retail distribution points. It uses artificial intelligence, advanced machine vision, high-power GPU computing, and virtual and augmented reality game engines for visualizing and analyzing data.

digital twin oil gas

Digital twin provides complete situational awareness of the operations in a 3D environment mimicking actual activities on the production sites. The software gets data feeds from IoT sensors and cameras installed on its real-world physical twins. Users can simulate real and hypothetical scenarios on the digital twin, monitor operations, predict events and detect process abnormalities in real time. The twin model consists of machine-learning models that learn from newer data automatically to improve predictions.

Digital twins increase the compliance to safety protocols by automating compliance processes using sensor data. For instance, it can flag danger automatically when a worker is not using a helmet, or a wheel of a machine seems loose. It also gives insights on which functional area suffers the most accidents, under which managers, in which region, and under what climatic conditions for corrective actions. Managers can decide on the spot as they can access real-time digital twin data from anywhere—on smartphones, smart glasses, head-mounted devices, and laptops.

Detecting Fatigue Level

More than 40% of work-related deaths in the O&G industry are due to vehicle accidents, often associated with a high level of driver fatigue.  Twelve-hour shifts are not uncommon in the industry; changing shifts and irregular working hours add to the fatigue level of workers.  Digital twin can assess the fatigue level of a person behind the wheels or handling accident-prone equipment using its computer vision algorithm and inform employers in real time. Employers can create an apt fatigue management policy.

fatigue level oil gas

Reducing Fires and Explosions

Digital twin’s algorithms are trained with thousands of risky situations to detect an event that could lead to fires and explosions. Controllers connected with digital twins can stop machines or give early warnings to personnel about an impending risk (e.g., fire flakes emanating from an electrical circuit) before it becomes too big to control. It applies similar algorithms to detect a gas leakage and elevated temperature at facilities.

Reducing Collisions

Sometimes workers leave debris or machine parts in the middle of a path on rigs. Such careless acts could lead to accidents as other workers or moving equipment may collide. The always-on digital twin’s machine vision model detects such events in real time and alerts authorized personnel for remedial actions.

Making Fewer Visits to Hazardous Facilities

With digital twins, workers can conduct virtual inspections and remote management of rigs and other infrastructures to reduce their visits to hazardous environments. Even if workers have to go to an actual facility, digital twin enables better preparation by familiarizing them with its operations, spatial structure and operating conditions such as temperature, and pressure in different parts.

oil gas maintenance

Promoting Condition-Based Maintenance of Assets

Digital twin recommends condition-based maintenance of machines to avoid their malfunctioning that could lead to accidents. For instance, digital twin algorithm of a dragline excavator can recommend when its parts need replacement. If the digital twin gets integrated with the manufacturers’ IT systems, they can also be informed to keep replacement parts on hand. This helps an operator avoid taking a chance with a compromised machine to keep pace with the project schedules. Similarly, digital twin can detect unwanted changes in infrastructure such as a crack on a wall using data from drones and LIDAR systems.

Enabling Swift Disaster Management

IoT sensors at rigs or other infrastructures in the sea monitor climate conditions. Digital twins allow the simulation of operations in different climatic conditions. For instance, it can show the impact of storms of varying intensities on various parts of rigs. Planning for emergency response on the facilities’ 3D model is easier due to an improved spatial visualization. When managers have both real-time data and an intuitive tool to infer the repercussions, they can take swift and effective actions to mitigate risks to human lives.

Improving Cybersecurity

About 70% of oil and gas firms were hacked in 2016. The breaches resulted in confidential data leaks. Digital twin can help these businesses to manage network threats. It enables stakeholders to understand correlations between events and predefined metrics visually in a highly intuitive environment, thus making unusual patterns clear in a matter of seconds. Thus, the digital twin model helps firms predict IT security beaches and plan proactive measures to deal with them.

oil gas cybersecurity

Next Step: Deploy Digital Twins

The effective use of digital twin can promote safety-first culture and make Oil & Gas firms and their people more responsible. Most of these energy firms are already collecting the necessary data required to deploy digital twins. Hence, an investment in sensors and network infrastructure will be minimal. The main barrier to adopting digital twins is skillset or a lack of sophistication to develop suitable architecture, process a massive amount of data and visualize business operations on digital twins in real time.


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