Neural Networks Drive Smarter Oil Production at Rosneft
Russian oil giant Rosneft is deploying neural‑network modeling to boost efficiency at one of the world’s largest fields, signaling a broader shift toward digital transformation in the extractive sector
AI‑Powered Optimization in the Field
Samotlorneftegaz, a Rosneft subsidiary, has developed proprietary software that uses neural‑network modeling to optimize production performance across hundreds of mechanized wells. Trained on operational data from 2019 to 2025, the system analyzes well behavior, performs engineering calculations, and generates recommendations to improve output and reduce downtime. The solution has been formally registered as a digital product, and in 2025 alone delivered an economic effect of roughly $1.1m.
The software was built jointly with Rosneft’s corporate research institute in Samara and is currently deployed at the Samotlor field, Russia’s largest and one of the biggest globally. Located in the Khanty‑Mansi Autonomous Area, Samotlor has been in development since 1965, making the need for advanced optimization tools especially critical as the field matures.
Scaling AI Across Rosneft’s Portfolio
Rosneft plans to expand the neural‑network model to additional assets; data collection for training is already underway at new fields. The initiative forms part of the company’s long‑term Rosneft‑2030 strategy, which emphasizes technological leadership as a key competitive factor. By investing in breakthrough digital tools, Rosneft aims to deepen sectoral innovation and build capabilities that were previously unavailable in the domestic market.
Such corporate projects are reshaping the industry itself. Digital intangible assets—algorithms, datasets, and AI platforms—are becoming increasingly important. This shift is expected to stimulate Russia’s broader IT ecosystem, including data analytics, software engineering, and applied machine learning.
A Global Trend in Predictive Operations
Since 2019, international operators have been experimenting with AI for predictive maintenance of pumps, compressors, and drilling systems. Rosneft was among the early adopters, integrating predictive tools for Arctic and Far Eastern projects to offset declining production at aging resources.
By 2013, the company had launched IoT technologies across LNG facilities, refineries, and petrochemical plants. At Samotlor, digital field‑development tools have been in place since 2018, with digital twins created for ten production clusters. These tools helped raise daily output by up to 0.5 percent, producing an economic benefit equivalent to $2.4m at the time.
Rosneft’s broader “Intelligent Field” initiative, introduced in 2013, envisioned full remote monitoring and control of field operations. Subsequent innovations included machine‑learning models that improved steam‑injection planning for heavy‑oil wells, increasing oil recovery by about 3 percent.
A Sector Shifting Toward Data‑Driven Production
Rosneft’s approach demonstrates how data‑centric tools can influence not only maintenance efficiency but also overall reservoir performance. By optimizing completions, improving predictive modeling, and enhancing the recovery of hard‑to‑extract reserves, AI becomes a lever for long‑term production stability.
The company’s leadership sends a strong signal to the market: digital technologies are now a strategic priority for major producers. In the medium term, the domestic AI and software sector is expected to grow alongside demand from the energy industry. Over time, a new industrial cluster—IT plus extraction—may emerge, with oil companies acting as major technology customers.
