About Energy AI Center

Built for the gap between
AI hype and upstream reality.

Energy AI Center is an independent executive education platform focused on Artificial Intelligence in upstream oil & gas. We teach decisions, systems, and value creation — not tools, demos, or vendor narratives.

Why We Exist

The industry has a structural AI literacy problem.

The Gap
AI has moved beyond experimentation in energy. Decisions about data, models, and operating models now shape capital allocation, asset performance, and safety outcomes. Yet most executives making these decisions have no structured framework to evaluate them. Generic AI courses ignore upstream realities. Vendor training is biased. Academic programs are too abstract.
Our Answer
A program built specifically for upstream — with the analytical rigour of top consulting firms, the domain depth of operational experience, and the decision-centric logic that separates real value from AI theatre. Every section answers one question: where does this change a real upstream decision, and what is the stake?
Founder

Built by an upstream practitioner.

Founder photo · Oleg Kofanov · 280×320px
Oleg Kofanov
Founder, Energy AI Center · AI Strategy & New Business, Gazprom Neft Shelf
Founder bio text — 3–4 paragraphs · upstream experience, AI focus, why this program
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Our Approach

What makes Energy AI Center different.

Decision-centric
Decisions, not demos
Every module answers one executive question. Every section maps to a real upstream decision with a real stake. No tool walkthroughs without business context.
Evidence-based
Claims, not claims
Every hard claim carries a source or is marked as inference. No motivational padding. No generic AI case studies unrelated to asset-heavy industries.
Upstream-specific
Built for the domain
All examples, frameworks, and cases are adapted to upstream reality: physical assets, long lifecycles, safety-critical operations, CAPEX-intensive decisions.

"Energy AI Center exists to give upstream leaders the language, logic, and tools to make the right decisions about AI — before capital is committed, before pilots fail to scale, and before credibility is lost."

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