AI Designs a Thousand Wells: Gazprom Neft Deploys Drilling Planning Agent
PJSC Gazprom Neft has developed an AI agent for planning drilling operations at oil fields. In one hour, the system can generate up to a thousand well placement scenarios, a task that previously required about a week of engineering work.
AI is now widely used in heavy industrial engineering, where the stakes are measured in capital expenditures, project timelines and recovery efficiency. The rollout of an AI drilling planning agent also advances technological sovereignty in oil and gas software and digital tools. Globally, this also signals that Russian companies are capable of building competitive, domain-specific AI solutions not only for workflow automation but also for complex engineering optimization.
The technology has already been tested at three production sites in the Khanty-Mansi Autonomous Okrug and the Yamalo-Nenets Autonomous Okrug. In real time, the system analyzes millions of parameters – including geology, reservoir physics, drilling constraints and project economics – and proposes optimal well placement schemes and designs. The solution is based on machine learning trained on terabytes of hydrodynamic simulation data.
Five Times Faster
According to the developer, the system independently determines the number and types of well designs, offering multiple design scenarios. Compared with conventional methods, AI-driven calculations are five times faster. Users interact with the system through an interface built around digital field models.
Rather than following rigid instructions, the system identifies optimal strategies by simulating scenarios, analyzing outcomes and refining results. Training relied on terabytes of hydrodynamic simulation data from real wells.
Industry experts note that the era of easy oil is over. Operators are increasingly working with complex and hard-to-recover reserves. Against this backdrop, traditional approaches – where engineers iterate through scenarios in simulators over weeks – can no longer keep up with the pace of modern operations.
In well design, the challenge goes beyond evaluating billions of combinations. Adjustments to one well affect pressure behavior in neighboring wells. This requires integrating machine learning with fundamental physical laws of reservoir hydrodynamics.
Looking Ahead
The oil industry is steadily moving toward more complex and hard-to-recover reserves. That shift demands expertise in horizontal well placement scenarios, multi-stage hydraulic fracturing and a range of production regimes. In this context, the AI agent is no longer a supporting tool but a core element that accelerates the entire engineering cycle – from modeling to investment decisions. This is particularly critical for Western Siberia, Arctic projects and mature assets, where well spacing optimization directly impacts field economics.
The product serves as both an oilfield services solution and a platform capability. In 2023, Gazprom Neft signed an agreement with Uzbekneftegaz to deploy its engineering and digital solutions for drilling optimization. A year later, a drilling control center developed with Russian participation was fully commissioned in Uzbekistan, overseeing drilling operations across 23 rigs at multiple fields.
In the future, the AI agent could become part of a broader “digital field” ecosystem, integrating with geomechanical modeling, digital twins, drilling control centers and production monitoring systems. This reflects where the global market is heading, placing Gazprom Neft’s development within a wider industry trend.
Peers Keep Pace
In 2022, Messoyakhaneftegaz deployed AI-driven tools in drilling operations at the Vostochno-Messoyakhskoye field. Industry 4.0 solutions increased drilling speed by more than 10%, while digital drilling technologies improved safety and reduced human involvement in critical operations. At that stage, AI supported well construction; it is now taking on large-scale design tasks.
What stands out is that domestic AI systems can operate in one of the most complex engineering environments – where geology, physics, economics and real-world operational constraints must all be considered simultaneously. This reflects a shift in both the scale of engineering work and the approach to field development.
