Gazprom Neft has disclosed a pilot project using artificial intelligence to design chemical molecules intended to improve hydrocarbon recovery.
The verifiable English-language material was published by AK&M and explicitly states that it is based on information supplied by Gazprom Neft. The company material is dated July 25, 2026, at 6:17 a.m., rather than July 26.
The first samples were developed for a mature field in Russia’s Yamalo-Nenets Autonomous Okrug. Artificial intelligence modeled approximately 6,000 combinations of chemical compositions and selected a priority candidate molecule. Gazprom Neft says it can be produced using Russian feedstocks and technology.
Development compressed to three months
According to the company, the molecule was created in approximately three months, compared with at least two years for conventional laboratory development of similar products.
Laboratory testing involving Kazan Federal University and Tyumen State University reportedly confirmed the effectiveness of the synthesized material.
Reagents based on the molecule are intended to function as surfactants that help displace residual oil from reservoirs, supporting mature-field production and more complex deposits.
AI narrows the search rather than replacing testing
The principal advantage of the system is its ability to process data on available raw materials, feasible synthesis processes and targeted surfactant properties across thousands of possible compositions.
The algorithm identifies priority candidates, but laboratory synthesis, stability testing, reservoir compatibility and field validation remain necessary.
Gazprom Neft’s technology partner is Kemprofet, a startup that participated in the INDUSTRIX accelerator. Further work is expected to target large fields in Western Siberia and the Orenburg region.
Commercial performance has not yet been demonstrated publicly
The available disclosure does not provide long-term field production data, treatment cost, injection scale or a complete comparison with existing chemical-enhanced-recovery products.
The most accurate conclusion is therefore that the candidate has passed synthesis and preliminary laboratory validation—not that it has already delivered large-scale commercial production gains.
