In recent years, the oil industry has faced the need to develop complex and hard-to-reach fields. Most of the easy reserves have already been tapped, while new deposits are hidden deep underground in heterogeneous formations. Over millions of years, geological processes have turned underground reservoirs into a tangled network of isolated traps, making their exploration and development significantly more complicated.
Traditional exploration methods do not provide a complete picture of oil distribution underground. Specialists are forced to make decisions based on limited and indirect data, which leads to high risks and potential financial losses. Errors in assessing a field’s structure can result in lost investments and reduced production efficiency.
Virtual twins and new approaches
To improve forecasting accuracy, engineers use three-dimensional subsurface modeling. These digital replicas of fields take into account numerous parameters, from formation geometry to rock properties. However, even the most advanced software can’t always account for every nuance, such as hidden faults or unexpected structural changes.
To minimize the effect of unknown factors, specialists create dozens or even hundreds of 3D model variations for the same asset. Yet choosing the most reliable version remains a challenge: current selection methods often rely on simplified criteria and do not allow for an objective comparison of all possible options.
Breakthrough by Russian Scientists: The Occol Comprehensive Criterion
Researchers at Perm National Research Polytechnic University (PNRPU) have proposed an innovative method for selecting the most realistic oil field models. Their approach is based on a comprehensive assessment of digital twins using four independent parameters, significantly improving the accuracy of forecasts.
During the experiment, the scientists gathered a vast dataset from one of Russia’s oil fields: drilling results, seismic studies, and production history. Using this data, they generated nearly three hundred variants of 3D models. For analysis, they deployed an advanced software module that incorporated new validation criteria.
The key element was the development of a special metric—Occol—which quantitatively assesses how closely each model matches real-world conditions. This criterion takes into account layer thickness, consistency with historical data, reserves volumes, and the geological plausibility of rock distribution.
Results of Implementation and Economic Impact
Applying the new methodology significantly narrowed the range of oil reserves estimates. Whereas previously the spread in values reached hundreds of thousands of tons, after introducing the comprehensive criterion, uncertainty was nearly halved. The number of models requiring detailed analysis also dropped by more than 80%.
Testing on real data showed that models with the highest Okkol values most accurately reflect the actual production dynamics of recent years. This opens up new opportunities to optimize operational decisions and reduce costs in field development.
The development has already attracted interest from Russian oil companies, who are seeking effective domestic solutions for digital modeling amid import substitution efforts. Negotiations are underway to implement the software module at industrial facilities.
Prospects and international cooperation
The new technology could gain demand not only in Russia, but also abroad. It improves the accuracy of reserve assessments, reduces risks in drilling planning, and increases returns on investments in the oil sector.
The project was implemented with the support of regional scientific programs and in cooperation with international experts. In particular, Professor Li Wang of China University of Petroleum (Qingdao), who has extensive experience in geophysical well research, was involved in the work.
The introduction of this improved methodology opens up new horizons for the development of complex fields and may become an industry standard in the coming years.
In case you weren’t aware, Perm National Research Polytechnic University (PNIPU) is one of Russia’s leading technical universities, founded in 1953. The university plays an active role in scientific and educational projects related to the oil and gas industry and collaborates with major industrial companies across the country. PNIPU’s structure includes the Mining and Petroleum Faculty, which trains specialists to work at some of Russia’s largest fields and internationally. In recent years, the university has focused especially on the development of digital technologies and the implementation of innovations in geological modeling. Among its partners are leading research centers in Europe and Asia, as well as major oil corporations. The university regularly hosts international conferences and forums on energy and resource management. Thanks to its dynamic research activity and the adoption of new technologies, PNIPU consistently holds strong positions in Russian technical university rankings. The university supports young scientists and runs internship programs for students at top research labs around the world. In 2025, PNIPU continues to expand its international cooperation and integrate advanced technologies into its educational process.
