Gazprom Neft has unveiled an AI agent capable of planning drilling layouts at a staggering speed-designing 1,000 well placement options in just one hour. This cuts down what typically takes a team of engineers at least a week to accomplish. The technology has already been tested on three oil fields in Russia’s Khanty-Mansi and Yamalo-Nenets regions.
How Gazprom Neft’s AI agent designs well layouts
The system sifts through millions of data points, including geological characteristics, reservoir physics, drilling capabilities, and project economics. It autonomously decides the quantity and type of wells, then generates multiple simulation scenarios. Compared to traditional methods, it speeds up calculations by a factor of five.
At the core lies reinforcement learning. Instead of following strict instructions, the program explores strategies on its own, running simulations, analyzing mistakes, and refining its models. To train the AI, Gazprom Neft fed it terabytes of hydrodynamic simulation data from real wells.
”Designing well layouts means evaluating billions of combinations. The challenge is that wells don’t operate in isolation: pressure changes in one area impact nearby zones. That’s why we had to blend machine learning with hard hydrodynamics and reservoir physics laws,” explains Denis Prikhna, developer at Gazprom Neft’s Digital Solutions division.
Test results and AI advancements in oilfield drilling
The AI-generated drilling plans for the three test sites outperformed conventional designs in both accuracy and efficiency. An interactive interface was also created to present digital models of the fields to users.
”The era of ’easy oil’ is over. Today we face complex, hard-to-reach reserves. Engineers now design multikilometer horizontal wells where mistakes are costly. Traditional approaches-where engineers spend weeks testing options in simulators-can’t keep pace with business demands,” says Sergey Bazhukov, head of the Competence Center for Integrated Asset Modeling at Gazprom Neft’s Scientific and Technical Center.
Gazprom Neft’s AI-driven approach addresses a bottleneck in oilfield development planning. While global industry players like Schlumberger and Halliburton have invested heavily in AI-enhanced reservoir modeling and drilling automation, Gazprom Neft’s solution stands out by integrating physics-based constraints directly into a self-learning framework. This helps ensure the models remain realistic and actionable, not just optimized mathematically.
Expect this AI well layout design technology to accelerate as oilfields become increasingly complex and the push for efficiency intensifies. The next challenge will be integrating real-time data from drilling operations to enable dynamic adjustments-turning AI-driven planning from a batch process into continuous optimization on the rig.