Algorithms Cut Infrastructure Optimization Timelines from Five Months to One Week
Researchers at Novosibirsk State Technical University have developed algorithms for optimizing the energy infrastructure of oil and gas facilities, reducing calculation times from three to five months to as little as one week by automating data collection, processing, and modeling.
An objective assessment shows that the energy infrastructure supporting Russia's fuel and energy sector requires modernization and optimization. The pace of new capacity additions is struggling to keep up with demand. The challenge extends beyond financing and physical resources. Upgrading existing assets and designing new infrastructure requires processing vast amounts of data.
Many oil and gas fields and exploration areas operate with excess energy infrastructure, much of it aging. Forecasting future loads for these assets is a mandatory component of oil and gas operations and requires substantial engineering and analytical resources.
One of the industry's key questions is whether to build new facilities on top of existing infrastructure or develop systems from the ground up. Both approaches carry advantages and risks. AI can now provide data-driven recommendations and generate multiple viable options for consideration. In practice, these algorithms are designed to become part of a digital twin environment. They draw on a broad range of inputs, including geological data such as reservoir characteristics and formation pressure. They also incorporate production and power-supply information, including infrastructure condition and energy consumption levels. The system continuously monitors infrastructure performance in real time and adjusts power delivery to changing operating conditions.
The NSTU development significantly reduces the time required for data processing, modeling, documentation preparation, and energy-system design.
Saving Time and Resources
The technology has potential applications at mature fields, during greenfield and brownfield project planning, and as a component of broader industry digital platforms. In particular, the idea of creating a digital twin of Russia's oil industry by 2050, known as KiberTEK (CyberTEK), was discussed at CIPR 2025. The initiative is intended to model and optimize decisions at the national level.
At present, KiberTEK is being considered as an additional tool for improving efficiency across the fuel and energy sector through greater operational transparency, more flexible taxation mechanisms, optimization, and rapid dissemination of best practices and technologies. Its implementation is expected to increase production volumes, export revenues, reliability, and the efficiency of supplying domestic markets with energy resources and power.
The concept emerged from discussions between Russia's Ministry of Energy and Ministry of Finance on the use of digital twin technologies to determine tax regimes for oil and gas fields. The project is expected to begin as a pilot program. If the results prove successful, the technology could be expanded across the industry. Meanwhile, dozens of strategically important projects involving Russian industrial software for the oil and gas sector are being pursued through the Industrial Competence Centers initiative. Together, these efforts are creating an institutional foundation for the deployment of advanced algorithms and digital twins.
The NSTU development could become a foundational technology for projects undertaken by oil and gas companies, university research programs, and the Industrial Competence Center for Oil and Gas, Petrochemicals, and Subsurface Resources, a consortium of Russian energy companies focused on transitioning the oil, gas, and petrochemical sectors to domestic industrial software solutions.
Industry Experience
Digital twin technology first gained significant attention in Russia's oil sector in 2021, when Gazprom Neft created a digital twin of the A. Zhagrin field. It became one of the country's earliest examples of using a digital twin to manage field development and improve production efficiency.
In 2024, work began on a digital twin for two sections of the Urengoy field. The joint project between Tsifra Group and Achim Development covered the entire production chain, from the reservoir to gas processing. The platform demonstrated the ability to analyze scenarios, select operating modes, and adapt to new data.
Earlier this year, Rosneft developed a digital twin of a major oil pumping station. Built entirely on domestic software, the model includes more than five million 3D elements and is used for information modeling of a complex infrastructure asset.
Overall, if the NSTU algorithm confirms its ability to reduce calculation cycles from months to a single week while delivering measurable economic benefits, it could become part of larger digital oilfield platforms.
