MOSCOW, March 19 – The Karpinsky Institute reports that artificial intelligence is transforming geological exploration in Russia, serving as a powerful tool to optimize processes and improve forecast accuracy. However, institute experts stress that final decisions must remain with human geologists.

"It's vital to see AI as just another tool for the geologist," said Vasily Leontyev, deputy director of the Institute's Center for Prognostic and Metallogenic Research. "Algorithms can find hidden patterns but cannot interpret them geologically. They see correlations, not context. A geologist builds the model; AI helps test it. It cannot replace expert analysis of unique geological processes."

The near-term benefit, scientists note, is speed. AI will significantly accelerate data processing, refine interpretation, and support better-informed decisions. In oil and gas, automated analytics and deep learning models are expected to minimize drilling risks and cut operational costs, making projects more economically viable.

Yet AI has clear limits. Trained on data from known deposits, algorithms often seek similar sites and may miss fundamentally new types of ore bodies. Well-studied regions receive positive forecasts, while underexplored territories—potentially rich but data-poor—are overlooked.

To bridge this gap, the Institute employs a combined approach: a geological-genetic method, which predicts processes regardless of a territory's study level, and an empirical-statistical method to uncover hidden patterns in mineral formation.

"We are already conducting exploration on several prospective sites identified using this approach," said Pavel Khimchenko, the Institute's General Director. "During the 2026 field season, we plan to verify a number of sites that have a high probability of becoming new deposits."

Another challenge is AI's struggle with heterogeneous, unstructured data—the geologist's core task in early-stage exploration. "Diagnosing rocks and ores in the field requires an expert eye. Each deposit is unique; training AI for every potential object is impractical and risky," the Institute added.

Russian industry is not waiting. In February, Alrosa completed a successful AI experiment that identified promising areas for kimberlite pipes. Last December, Gazprom Neft reported that its AI implementation accelerated field development by roughly a year.

Source: RIA Novosti