How Video Analytics Gives Eyes and Insight to Red Zone Monitoring
“In the ever-volatile world of oil & gas, some areas are too dangerous to leave to chance.”
Let’s be honest, the oil rigs don’t exactly come with wide-open floor plans.
A single misstep on an offshore rig can mean injury, shutdown, or even worse. In oil & gas, red zones are high-risk areas where people and machines share dangerously tight quarters.
Traditional safety methods like signs, spotters, and manual logs often can’t keep up.
Red zones are the industrial equivalent of a “Do Not Disturb” sign—except that disturbing them can lead to actual danger. These high-risk pockets on rigs and drilling platforms require precise monitoring and sheer vigilance.
But oil rigs are evolving. Today, intelligent video analytics is changing the equation.
Beyond the Blind Spot: Making Red Zones Visible
In oil & gas sites, red zones are experiencing close human-equipment interaction, hazardous materials, or operational hazards that demand limited or no human access. Traditionally, these zones were mapped manually, often based on historical incidents or SOPs.
But with video analytics, that approach in workplace safety is shifting to a dynamic, data-led one.
AI-powered video analytics uses historical footage on similar sites, the behavior patterns of workers, and operational data to define red zones more precisely.
For instance, a rig in the Persian Gulf recorded near-miss data for six months. Using AI-based video analytics, the system redefined the red zones based on patterns of unintentional human proximity to rotating equipment, leading to a 38% reduction in their Total Recordable Incident Rate (TRIR).
What makes this technology further indispensable in red zones is its ability to detect intrusions in real-time and trigger alerts within milliseconds, much faster than traditional sensors. This is where the real power of video analytics lies – giving decision-makers a constant, real-time lens into places where no human should have to stand and watch.
In a North Sea rig scenario, such an AI module, implemented using the existing CCTVs, reduced unauthorised intrusion into red zone response time from an average of 18 seconds to under 2 seconds, helping avert potential injuries during operations.
Safety in Motion: Where AI Tracks More Than Just People
Red zones are not static. Their risk profile changes by the hour. When heavy machinery operates, the radius of danger expands. When lifting is paused, it contracts. This is where video analytics does what static systems can’t — it understands the context.
Using work area heatmaps from data-driven footage stored, operations teams on a Malaysian offshore platform could visualize unsafe congregation points during maintenance cycles. This led to a 27% improvement in task rescheduling, directly reducing crowding during high-risk periods.
Video analytics also helps to ensure PPE compliance within red zones. On an Oman site, repeated violations from workers entering red zones were observed. They often enter the high-risk tasks without wearing flame-resistant coveralls or face shields.
This resulted in two benefits – eliminating reliance on manual supervision and improving PPE adherence by over 45% within 2 months.
The purpose of red zone monitoring isn’t just about keeping people out. It is about understanding why they go in, what they wear, and what happens if they do.
Predict Before You React: Smarter Equipment, Safer Zones
One overlooked application of video analytics is in detecting equipment malfunctions that can turn ordinary areas into red zones in seconds.
A leading drilling contractor operating across five oil & gas sites in Southeast Asia and the Middle East used AI video analytics for red zone monitoring to study months of lifting operations across all locations.
By comparing near-miss patterns, blind spot intrusions, and ineffective barricade setups, the system uncovered consistent red zone violations.
This cross-site insight revealed that over 68% of red zone entries were avoidable, often due to poor path design or inconsistent safety protocols. The team redesigned pedestrian walkways and dynamically adjusted red zone boundaries based on real-time data.
As a result, the contractor achieved a 70% reduction in unnecessary exposure time in high-risk areas and a 21% drop in lifting-related near misses — all by letting AI learn across sites and lead smarter safety decisions.
Even more critically, an appropriate and much-needed use of these systems is now being done to augment visibility in visually complex environments like offshore rigs at night, where lighting, smoke, or steam obscures human observation.
AI-enhanced vision systems maintain full awareness of movement patterns, helping night crews stay alert to hazards that are nearly invisible to the naked eye.
Rethinking Risk in the Red Zone
Red zones in the oil & gas sector are no longer just physical boundaries – they are dynamic, data-driven spaces that require constant interpretation. Video analytics is shifting the way safety is managed in these areas.
Whether it’s mapping risk through heat signatures, catching the smallest PPE lapses, or predicting failure before it disrupts operations, the value lies not in watching more, but in understanding better.
As the sector advances, one thing is clear: it’s no longer about reacting to accidents — it’s about removing their opportunity to occur.