Russia’s Oil Sector Takes to the Skies with Drones

An All-Seeing Eye

The primary tasks of these systems include monitoring production facilities, pipelines, and security zones, detecting abnormal conditions, and rapidly notifying operations teams. Compared with traditional monitoring methods such as foot patrols and ground inspections, drones provide more frequent and far more detailed coverage of oil production areas.

AI algorithms automatically process images and video, identifying anomalies such as leaks, equipment damage, and irregular operating conditions. As a result, reliance on human judgment is reduced and operational safety improves, especially in remote or hazardous locations.

Automated analysis of large data volumes shortens the time between problem detection and operator response, enhances personnel and infrastructure safety, and contributes to stronger environmental protection.

Russian Industry Experience

One prominent example is Tatneft, which is expanding its aerial monitoring system. Initial trials began in 2016. In 2019, a dedicated unit was established at the Process Control Systems Center of Processsing Center LLC in Almetyevsk, a Tatneft subsidiary. The unit focuses on pipeline protection zones and local oil production facilities. Drones equipped with thermal imaging cameras spend nearly 3,000 hours in the air each year, surveying more than 21,000 kilometres of pipelines and over 42,000 oil production assets. Rapid incident notification makes it possible to mitigate risks at an early stage. In addition to detecting operational incidents, the system identifies heat losses, while environmental monitoring includes tracking methane concentrations in the atmosphere.

The same unit also updates digital field maps annually across an area exceeding 1,500 square kilometres, supporting surveying work and production planning. To ensure safe placement of infrastructure in complex and densely populated areas, terrain digitisation is continuously maintained. The result is not just mapping, but the creation of full 3D models of company assets.

All collected data is processed on a corporate digital platform. However, developers are not limiting the technology to visual inspection alone. Acoustic detection is now being tested, allowing equipment wear to be diagnosed based on abnormal noise patterns. According to experts, these technologies will soon move beyond monitoring to include logistics and cargo delivery functions.

We see strong potential in using drones for environmental oversight at industrial facilities to prevent accidents. For example, at Norilsk Nickel’s tailings storage facility, we monitor the condition of dams and hydraulic structures, analyse beach deposition dynamics, and track the volume of dam and filtration prism construction work. This approach not only improves workplace safety, but also removes personnel from hazardous environments while increasing the frequency of geodetic monitoring. Drones equipped with thermal imaging cameras are already in use, for example at landfill sites to analyse methane emissions. Impact-resistant drones have also been developed. They can operate in confined spaces within a protective spherical frame, inspecting bridges and overpasses and identifying potential issues even in locations that are difficult or dangerous for humans to access

Artur Khasiyatullin

Regional Director at TraceAir

One factor slowing the development of drone-based logistics is regulatory complexity surrounding airspace use. Currently, UAVs operate under the same legal framework as manned aircraft. Wider adoption is expected once aviation regulations are simplified.

Looking Ahead with UAVs

Even today, technologies allow major Russian oil companies, including Gazprom Neft, to integrate AI-driven monitoring into their digital ecosystems, improving oversight of oilfields. In the near term, AI and IoT are expected to enable predictive diagnostics of infrastructure condition and automated response to emergency situations.

Because AI- and drone-based monitoring systems have clear commercial value on international markets, there is strong potential to export proven Russian solutions to foreign oil and gas operators. This is reinforced by the fact that the technologies align with global digitalisation trends in energy and with increasingly stringent environmental reporting and monitoring requirements.

One of the most promising solutions is the drone-in-a-box concept, designed for round-the-clock autonomous monitoring. It includes a docking station that shelters the drone and enables automated launch and recharging.

Experiments and Global Context

Last year, reports emerged of AI-powered drones being used by Chevron and other US operators to detect methane leaks in the Permian Basin, a major sedimentary gas province. AI algorithms identified emissions in real time, issuing confirmed alerts with precise geolocation, timestamps, environmental data, and exposure duration. This allowed operators to respond faster and with greater precision.

In 2024, Saudi Aramco reported testing combined AI and drone systems for scanning and leak detection at its largest fields. A year earlier, the first integrated applications of AI in oil and gas moved beyond data analysis into predictive diagnostics. The initial deployment of AI-based visual inspection systems for infrastructure dates back to 2022.

The adoption of AI-enabled drones in the oil and gas industry is clearly an evolutionary step toward fully digital control of production assets and infrastructure. The emergence of ready-to-deploy modular solutions tailored to sector needs, including those of small and mid-sized operators, now appears to be only a matter of time.