13:41, 17 января 2026

AI Is Now Watching Over Safety at Russia’s Oil and Gas Fields

Tatneft has rolled out an AI-powered computer vision system that autonomously detects safety violations and technical failures at extraction sites, covering up to 90 percent of the most common industrial risks.

Artificial intelligence is moving deeper into Russia’s oil and gas sector. Across all of its oil and gas production units, Tatneft has deployed a corporate platform that analyzes photo and video data to identify threats and violations at extraction facilities. Built on computer vision and machine learning, the system automatically detects more than 50 types of anomalies, from equipment malfunctions to breaches of safety regulations.

According to the company, the platform already covers up to 90 percent of the most common production risks, significantly reducing the need for manual inspections.

A Self-Learning Safety System

As reported by the industry outlet Devon, the process begins with aerial surveillance. Drones equipped with thermal imaging cameras, along with stationary cameras installed at industrial sites, continuously collect visual data. That information is automatically uploaded to a centralized storage system, where AI models trained for specific tasks analyze the footage.

The algorithms determine both the location of the inspection and the nature of any detected anomaly, then automatically send alerts to dispatchers responsible for resolving the issue.

“The system helps prevent theft, monitor compliance with safety requirements, and check the condition of equipment,” said Robert Rizvanov, a representative of Tatneft-Dobycha. “Deviations that people might miss—especially in hard-to-reach areas or during floods or heavy snowfall—are detected in a timely manner. This reduces labor costs and allows teams to respond quickly to alerts.”

Toward Fully Autonomous Analytics

According to Ilya Fedorov, head of the AI and robotics project group at Tatneft’s industrial automation center, the company is continuing to train its models with the long-term goal of removing humans from the analytics loop entirely.

The team is building what it describes as a self-learning, adaptive ecosystem, one that evolves alongside the company’s operational needs and becomes more accurate as it processes new data.

A “Single Window” for Incidents

“Previously, information about deviations came through an electronic document management system and had to be entered manually,” said Rasul Muradymov, head of the safety analysis and monitoring department. “All operational tasks were handled by people. The new system has automated these processes. Data now goes directly to the person responsible for fixing the issue.”

The photo and video analytics platform is only one part of Tatneft’s broader digital push. The company has also implemented a centralized incident management system based on a “single window” principle, allowing different units to respond faster to safety issues.

At the same time, Tatneft is expanding its aerial monitoring program. Each year, drones inspect tens of thousands of kilometers of pipelines and industrial facilities, while also updating digital maps of oil fields spread across vast territories.

Taken together, these technologies are setting a new benchmark for how AI, automation, and remote sensing are reshaping industrial safety in Russia’s energy sector—turning oil fields into environments where algorithms, not inspectors, are increasingly the first line of defense.

Extractive industry