AI Detects Leaks on Oil Pipelines
Researchers at Perm Polytechnic University have developed an intelligent leak-detection system for oil pipelines that combines hydraulic localisation, pressure sensors and a neural network trained to distinguish actual emergencies from routine operations such as switching procedures and maintenance work.
Scientists at Perm National Research Polytechnic University, or PNIPU, have developed an intelligent system for detecting leaks on oil pipelines. The platform combines hydraulic localisation technology, pressure sensors and a neural network capable of distinguishing an actual accident from normal operating procedures, including switching operations and repair work.
According to PNIPU, field tests conducted on an operating oil pipeline in Perm Krai showed that the system identified the location of a controlled leak with an error margin of 76 metres. That is roughly four times more precise than the current industry benchmark of 300 metres. The sensors can operate autonomously for up to three years, while the software runs on the Russian Astra Linux operating system.
An oil pipeline is classified as high-risk infrastructure. Pipelines deteriorate not only because of aggressive internal operating environments, but also due to external factors such as temperature fluctuations and difficult terrain conditions. Conventional monitoring methods do not always detect leaks quickly enough, creating risks not only for oil producers, but also for the surrounding environment.
Researchers at Perm Polytechnic developed the new system as a response to challenges faced across the hydrocarbon extraction industry. According to industry estimates, the technology could potentially save billions of rubles by preventing expensive accidents that are both difficult and costly to eliminate.
Russia’s total pipeline network exceeds 70,000 kilometres and transports approximately 90% of the country’s oil production. Most pipelines have been operating for more than 30 years, while many routes pass through remote and difficult-to-access areas. In practice, reaching the site of an accident can be as difficult as eliminating the leak itself. That makes the development particularly relevant for the industry.
Reducing False Alarms
Operational experience shows that analytical reliability is critical. If a monitoring system repeatedly generates false alarms, dispatch personnel eventually stop responding to the signals.
The new method is based on two complementary principles. Hydraulic localisation sensors continuously measure pressure inside the pipeline, reliably identifying the suspected location of a leak. The neural network then filters incoming data, separating genuine threats from routine technological operations.
Importantly, sensor installation does not compromise the integrity of the pipeline system because the equipment is mounted externally rather than inside the pipe itself. The process also does not require pumping operations to be halted. Dispatch operators receive a full digital map display where an emergency signal appears automatically if a leak occurs. Emergency crews can then be sent to the precise location within a margin of several metres instead of searching along the entire pipeline route. Successful testing was carried out on an operating oil pipeline in Perm Krai.
The wireless system requires no maintenance and operates fully autonomously for years at a time. That makes it a promising solution for the harsh operating environments of the Arctic, Siberia and the Russian Far East. Protection against external cyber and infrastructure threats is provided by Russian-developed software fully compliant with the security requirements for critical information infrastructure.
The Extraction Industry Takes Notice
Russia operates the world’s largest pipeline infrastructure network, giving the deployment-ready accident-prevention system a clear commercial niche. Importantly, the product integrates seamlessly with dispatch systems used by oil and gas companies through Russian software platforms that have increasingly replaced foreign IT components in critical infrastructure since 2022.
The project did not emerge in isolation. It was preceded by growing industry demand for integrated systems capable of detecting both leaks and illegal pipeline taps. Over several years, operators tested a wide range of approaches, including parametric systems, pressure-wave analysis, material-balance methods, infrasound technologies, fibre-optic sensors and both acoustic and visual inspection systems.
PNIPU itself developed a neural-network platform in 2024 for detecting defects in pipelines and power transmission lines. That project involved software capable of identifying damage on oil and gas pipelines, power lines and other extended infrastructure assets using photo and video data collected from cameras, drones and unmanned systems. Similar to the current project, the technology used AI for industrial defect detection and accident prevention.
Another notable project was presented in 2025 by Innopolis University and Gazstroyprom. Their AI-based pipeline defect analysis system uses digital radiography to detect weld defects with 94% accuracy while accelerating inspections by a factor of 30 compared with manual analysis. The system has also been added to Russia’s State Register of Measuring Instruments.
If the current system confirms its stated performance characteristics across a larger number of industrial facilities, it could become part of the standard digital monitoring toolkit used throughout oil and gas infrastructure.
