Russian Oil Producer Rolls Out AI Monitoring Across Production Sites
Drones and autonomous monitoring stations are now tracking industrial safety at extraction facilities.
Russian oil companies are continuing the digital transformation of their production operations. Across all oil and gas production units of Tatneft-Dobycha, a corporate photo and video analytics platform powered by artificial intelligence is being rolled out. The company’s press service told IT-Russia about the deployment.
Digital “Eyes” in the Sky and on the Ground
According to company specialists, unmanned aerial vehicles are used to carry out regular aerial inspections of facilities, assessing conditions from above. Data also flows in from autonomous monitoring stations on the ground. All collected materials are automatically analyzed by computer vision algorithms. The neural network can identify more than 50 types of deviations, including violations of occupational safety requirements, infrastructure damage, and other issues. If an incident is confirmed, the AI generates a task in a dedicated system, assigns responsible personnel, and sets deadlines for remediation.
The introduction of digital technologies has already demonstrated tangible results. Production processes have become more transparent, and violations are detected more quickly. Safety levels have also increased significantly, which is particularly important for hard-to-reach sites. In addition, the accumulated data is used to further train AI models, making the recognition of risky situations more accurate over time.
A Closed Digital Loop
Deploying such a platform creates what the company describes as a closed digital loop. Continuous digital monitoring supports faster, more informed management decisions and helps set consistent standards for efficiency and safety.
Earlier, IT-Russia reported that researchers at Perm National Research Polytechnic University developed an intelligent energy management system for oil wells that cuts power costs by 10–12 percent by optimizing equipment operation. The system is based on neural network algorithms and mathematical modeling. It analyzes well parameters, forecasts energy consumption, and then automatically adjusts electric submersible pumps by selecting optimal operating modes.
