Russian Geologists Assessed the Prospects of AI Implementation in Exploration
The introduction of artificial intelligence (AI) in Russian geological exploration is transforming the industry, as it is used as a powerful tool that helps optimize processes and, in the future, will improve the accuracy of forecasts in exploration activities. However, key decisions must remain with humans, RIA Novosti was told by the Karpinsky Institute.
“It is important to understand that AI still remains only a tool in the hands of a geologist. Algorithms can identify hidden patterns but are not capable of interpreting them from a geological perspective. AI does not understand context, it only sees correlations. The geologist creates the model, and AI helps to verify it, but it cannot replace an expert in analyzing unique geological processes,” said Vasily Leontyev, Deputy Director of the Center for Predictive Metallogenic Studies at the Karpinsky Institute.
In the near future, the use of AI by geologists will significantly accelerate data processing, improve the accuracy of interpretation, and help make more informed decisions, the scientists noted. For example, in the oil and gas industry, automation of analytics and the use of deep learning models will help minimize drilling risks and reduce operating costs, making exploration projects more economically viable.
However, AI also has a number of limitations, including training on datasets based on already known and explored deposits. As a result, algorithms often search for similar areas and may overlook fundamentally new types of mineral deposits. In addition, well-studied regions often receive positive forecast assessments, while less explored areas remain overlooked due to a lack of data, despite their potential resource richness.
To overcome these limitations, the Institute uses a combined approach, integrating two methods to obtain an objective picture: the geological-genetic principle, which allows forecasting processes regardless of the level of exploration of a territory, and the empirical-statistical method, which identifies hidden patterns in mineral formation.
“Exploration work is already underway in a number of promising areas identified by the Institute’s specialists based on this approach, and during the 2026 field season, verification of several areas is planned, which in the future, with a high degree of probability, may become new deposits,” said Pavel Khimchenko, General Director of the Karpinsky Institute.
Another issue with algorithms in geological exploration, according to the scientists, is their limited ability to work with heterogeneous and unstructured data. Working with such data is a key task for geologists at the early stages of exploration.
“For example, the diagnosis of rocks and ores in field conditions requires an expert approach. Each deposit is unique, and it is impossible to train AI for every potential object — this may lead to errors,” the Karpinsky Institute added.
Meanwhile, Russian companies are already actively implementing AI in exploration. In February, Alrosa successfully completed an experiment on the use of AI in geological work, which helped identify promising subsoil areas for the search of kimberlite pipes. In December last year, Gazprom Neft also reported successful implementation of AI in exploration, which helped accelerate the start of field development by approximately one year.
