Researchers Propose a Cost-Effective Approach to Subsurface Evaluation
Gas production begins with a digital model of a reservoir, a computer-based representation of the underground formation. Building one requires calculating numerous parameters: how porous the rock is, how easily gas flows through it, the pressure, the properties of the surrounding water, and the structure of the formations. These studies are expensive.
Preparing to develop any gas field, even a highly promising one, takes considerable time and requires substantial investments of labor and money. Researchers at Gubkin Russian State University of Oil and Gas have developed an approach that could reduce the cost of evaluating gas fields and improve production planning. The key is mathematical modeling.
Fewer Unnecessary Measurements
To make the process more efficient, the researchers used a computer model of a field to vary each parameter individually and examine how changes in porosity, pressure or water properties affected the production forecast. Some parameters have a major impact on the results, while others make little difference. The researchers then plot the findings on a diagram. The most influential parameters appear at the top, the least significant at the bottom, with a gradual transition between them. The resulting shape resembles a funnel or tornado. The top of the “tornado” represents the parameters that need to be measured with the greatest precision. The “tail” shows where measurement costs can be reduced.
The researchers tested the method using data from 66 actual gas accumulations in Western Siberia and found that the “tornado” looks different for different types of reservoirs. In shallow Cenomanian reservoirs, the most influential factors were formation-water properties and the way gas and liquids flow through the rock. In deeper Hauterivian reservoirs, accurate estimates of gas volume and an understanding of which parts of the formation are actually producing proved more important. This means that the same standard list of required studies cannot be applied to every field.
Identifying these differences in advance can help operators avoid measurements that have little bearing on production forecasts. The researchers estimate that the approach could save 30 million to 300 million rubles (approximately $370,000 to $3.7 million) per year at a single field.
A Strong Technology Pool
Russia's oil and gas industry has built up considerable experience in developing and using domestic computational tools to forecast how reservoirs will behave.
For example, Rosneft has used its RN-KIM reservoir simulator since 2014 to build field models and forecast well performance. By 2024, the simulator was being used to create 95% of the company's models for its assets, and savings in 2023 exceeded 500 million rubles (approximately $6.2 million). RN-KIM works with static models built in another software package, RN-GEOSIM, which is used to create 80% of the company's new geological models. The package automates model construction and enables rapid reserve assessments. Savings from avoiding third-party software are estimated at more than 700 million rubles (approximately $8.7 million) annually.
Gazprom Neft has GeoMate, a geological information system that aggregates and analyzes data from all the company's fields. The platform accounts for the influence of more than 200 geological parameters and brings together about 80% of the company's operations for analyzing geological and geophysical information, including seismic data, well data and core samples. GeoMate also incorporates artificial intelligence tools for seismic interpretation and geological modeling.
Researchers at Skoltech have developed an approach that combines machine learning, artificial intelligence and multi-objective optimization to identify efficient drilling parameters in complex carbonate formations. For one well, the model estimated that the rate of penetration could increase by as much as 68.4%, while reducing the load on equipment.
Accuracy and Efficiency
Under its energy development plans through 2050, Russia aims to increase natural gas production by 34.3% from its 2024 level, reaching 920 billion cubic meters. Production forecasting will play an important role in achieving that goal. Russia can now draw on its own digital solutions, which are competitive with foreign alternatives in both speed and accuracy.
Digitalization is changing the way companies evaluate the subsurface. In the past, improving forecast accuracy was often associated with conducting more studies. Today, computational methods make it possible to get more value from existing data and identify which additional measurements are genuinely necessary.
