
According to experts from the Oxford Institute for Energy Studies, despite the sanctions pressure from Western countries, Russia's oil and petroleum product exports will remain at current levels until 2030.
The key region for future oil production growth in Russia is the north of the Krasnoyarsk Territory. The "Vostok Oil" project, promoted by Rosneft, is based on the production potential of 13 oil and gas fields on the Taimyr Peninsula and in the northern part of the Krasnoyarsk Territory. Some of these fields are already operational, such as those in the Vankor cluster, while others are new developments in the Payakha cluster.
According to Rosneft, the project is a large-scale enterprise that will lead to significant job creation (the total number of people involved in the project is estimated at 400,000, including 130,000 Rosneft employees and contractors) and a substantial increase in Russia's GDP due to both direct and indirect investments. This is a flagship project for Rosneft, with confirmed oil reserves of 6 billion tons (approximately 45 billion barrels) and expected total hydrocarbon production of 50 million tons by the mid-2020s in the first phase, based on the Vankor and Payakha clusters, and up to 100 million tons (2 million barrels per day) in the second phase, involving the development of the East Taimyr fields, which is planned for the early 2030s Vostok Oil Project // Official Rosneft website URL: https://vostokoil.rosneft.ru/....
Based on the expectations of Oxford experts regarding the performance of Russia's oil sector, I have developed three scenarios for the industry's development until 2030.
In contrast, the low forecast predicts a further decline in production from 2023, as the development of the Vostok Oil field takes longer due to financial and technical constraints and fails to meet expectations. A somewhat faster decline is observed in other unfinished fields. In this case, liquid hydrocarbon production will drop to just over 9 million barrels per day by 2030, meaning crude oil production will be around 8 million barrels per day.
Modern innovative trends in various sectors of the economy undoubtedly have a significant impact on the oil and gas industry as well. The successful implementation of modern ideas gives oil companies competitive advantages, increases productivity, and ensures sustainable development. The application of artificial intelligence (AI) is becoming one of the key factors shaping the future of the industry. The use of machine learning and big data in field exploration, production forecasting, logistics management, and equipment monitoring has already proven effective. For example, AI helps optimize well drilling, reducing costs and shortening field development time, while also improving safety by preventing accidents.
In the oil industry, AI can be used to accelerate the processing and classification of pipeline data, serving as the most advanced operating system for determining the quantity and quality of oil. Operators are provided with information about the mass and quality of the product, as well as measurements of oil density, temperature, and pressure. The Russian oil sector already employs AI technologies such as cognitive systems for selecting downhole operations.
Specialists at Gazprom Neft have created an automated system that processes seismic data and monitors results using machine learning technology. Seismic reports are delivered to experts faster and with greater detail, while errors can be quickly identified and corrected thanks to metrics developed to control the quality of results. This method reduces delays in production and simplifies decision-making regarding potential oil reserves.
Additionally, Russian oil companies are actively implementing digital technologies for remote monitoring and management, such as the "Smart Field" system. This significantly enhances production efficiency through optimization and data analysis.
Another digital solution being introduced in the industry is Rosneft's "RN-Digital Core" software suite. It enables virtual modeling of rock samples, predicts hydrocarbon content in reservoirs, and selects the most effective development methods to increase oil recovery.