Artificial Intelligence in Oil & Gas
91% of Russian oil and gas specialists say they're ready to use AI in their work — and still don't fully trust it. Rystad Energy estimates digitalization and AI could unlock $500 billion in cumulative value for E&P companies between 2026 and 2030, and a similar system on a Chevron asset has already lifted production 6% above plan. Meanwhile, plenty of pilots at other companies never survived the jump from slide deck to production. Here's where AI's effect in oil and gas is already measured in numbers, where it's still a promise — and what it takes to close that gap.
AI in oil and gas isn't one technology — it's a dozen models at different stages, from seismic forecasts to NPV. Where data is abundant and the task is measurable — drilling, production, maintenance — the effect is already counted in percent and dollars. Where data is scarce, a pilot usually stays a pilot instead of turning into a return.
Where Does AI Already Deliver a Measurable Effect in Geology and Drilling?
Rystad Energy splits the potential value of digitalization and AI in production into four categories: asset development, operations and maintenance, exploration and reservoir development, and drilling, wells and production. Each moves at its own pace — some already run on hard numbers today, others remain a horizon stretching out to 2035.
Exploration is the most mature area: operators have spent years accumulating seismic data, drilling history and well logs, and that's exactly the data models handle most confidently. But the effect here is still mostly a forecast, not something measured after the fact.
«By 2035, forecasts suggest AI will automate 70-80% of seismic data analysis with reservoir-forecast accuracy of up to 85%, robotize 60-70% of drilling operations while cutting accidents and costs by 15%, and optimize oil and gas transport systems, refineries and gas processing plants, reducing costs by 10–15%.» — ANGI — AI development forecast for oil and gas through 2035 (in Russian)
In drilling, the numbers are no longer forecasts — they're measured on real wells. RN-Purneftegaz, a Rosneft subsidiary, deployed an automated drilling control system that works like an autopilot: it reads sensor data every 10 milliseconds, calculates the optimal penetration rate on its own, and halts the process when values turn critical.
«The new control system cut mechanical drilling time by an average of 11.7 hours per well. The average economic effect per well drilled is around 1.7 million rubles.» — Neftegaz.RU — Rosneft rolled out an AI system in its well-drilling process (in Russian)
- Mechanical penetration rate rose by an average of up to 8% in pilot industrial trials
- The system works with any type of drilling rig — no equipment replacement required
- The technology has been proposed for rollout across other assets in Rosneft's upstream portfolio
What Do the Numbers Show in Production and Equipment Maintenance?
In production, AI's job usually isn't to "find" but to "not lose": catching an anomaly on a well or a piece of equipment before it turns into downtime. That's where the effect is easiest to translate into percent and dollars — there's a baseline to compare against: the same well a month earlier.
«Examples demonstrate how an Integrated Operations Center as a Service (IOCaaS) model, powered by artificial intelligence, reduced costs by 5% and increased production by 6% in Canada.» — JPT (SPE) — Case Study: Field Deployments of AI-Based IOCaaS
Behind the aggregate percentages are specific assets. At Chevron's Kaybob Duvernay asset in Canada, the system cut lease operating expenses by 5% in its first year — mostly through lower fuel-gas use and fewer emergency callouts — while barrel-of-oil-equivalent output came in about 6% above plan. At ConocoPhillips' Montney asset, production rose 3-4% above forecast, costs fell by that same 5%, and the system helped avoid an estimated 71,000 BOE of deferred production.
The same logic applies to monitoring the equipment itself, not just the wells. Under an alliance between Shell, Baker Hughes, Microsoft and C3 AI, thousands of sensors were fitted to valves and compressors, and the system was gradually scaled up into refining.
«Some 10,000 sensors were connected to the equipment. … The system now processes more than 20 billion sensor readings a week.» — IA Devon — AI development forecast for oil and gas (in Russian)
Why Doesn't Every AI Project in Oil and Gas Pay Off?
A $500 billion headline number doesn't mean every project gets an equal share of it. Rystad is explicit about this: companies start from different baselines, and AI doesn't act as a magic bonus on top — it's a way to pull laggards up toward the leaders.
«A key structural finding across all four workflow categories is that AI, in general, does not necessarily raise the ceiling for the best operators; it lifts the rest of the industry toward the performance level that the best operator already achieves.» — JPT (SPE) — Rystad Puts Benefits of Digitalization, AI in Upstream Oil and Gas at $500 Billion
The second gap sits between pilot and production. A pilot runs on a carefully curated dataset built around one hypothesis; a production system has to run on the entire stream — noise, gaps and hundreds of wells at once. That's the last stretch that not every project survives.
«Evolving from proof of concept on small projects to scale is a complex undertaking, and it’s the last mile that proves most daunting.» — JPT (SPE) — Scaling AI for Maximum Impact in Oil and Gas
Tellingly, Rystad's own estimate is spread across five years (2026–2030) rather than framed as a one-off jump: value builds up as companies invest in data and organizational maturity, not the moment a vendor contract gets signed.
What Does Adoption Actually Require — Data, Integration, Trust?
Three prerequisites keep coming up across industry reviews, whether the topic is geology, drilling or refining: structured data, integration with systems already in place, and — as its own line item — trust in a result that can't just be taken on faith.
«They can get facts wrong, "hallucinate," and depend heavily on the quality of input data and how a task is phrased. Many models also don't reveal the logic behind their decisions, which makes verifying the results difficult.» — ANGI — around 91% of specialists are ready to adopt AI but don't fully trust it (in Russian)
Below the data sits the infrastructure needed to collect it. On old and remote assets, that infrastructure is often simply missing.
«Hydrocarbon fields are often located in remote, poorly studied regions with undeveloped infrastructure — swamps, deserts, Arctic zones. That creates extra difficulty in collecting and transmitting data.» — IA Devon — AI development forecast for oil and gas (in Russian)
The practical takeaway is the same across every source: where nobody can check where a number came from, people don't trust it — and they're right not to. Tracing a calculation back to its data source and formula matters just as much as the model's raw accuracy.
How Does AI in Oil and Gas Work on the AVP AI Platform?
AVP AI — an AI platform for oil and gas asset evaluation — is built on exactly the principles described above: not one model for everything, but a coordinator and eight specialised agents — search, geology, reservoir simulation, surface facilities, economics, wells, business case and data standardisation. Each agent owns its part of the chain: the geology agent covers reserves and analogs, the reservoir-simulation agent covers the production profile, the economics agent covers NPV and IRR.
Together, the agents run 34 calculation tools. Every step is logged: what data was used, from which source, and what formula produced the result — so the final number can be checked, not just trusted. That's a direct answer to the trust risk from the section above: the platform doesn't hide the calculation behind an interface, it shows the whole thing.
For more on the agentic architecture itself, see agentic systems in oil and gas. For how AI actually estimates reserves by analogy before drilling, see AI in oil and gas exploration, and for how that differs from a scheduled data pull, see automating asset valuation. The fastest place to see the effect is license area screening — where the count isn't in weeks of manual work, but minutes per asset.
Frequently Asked Questions on AI in Oil & Gas
Wherever there's a clear before-and-after baseline on a specific asset. At Rosneft's RN-Purneftegaz, an automated drilling system raised penetration rate by up to 8% on average and saved around 1.7 million rubles per well. At Chevron's Kaybob Duvernay asset, a similar AI system cut costs by 5% and lifted production about 6% above plan. Exploration and refining still lean more on forecasts to 2035 than on a measured fact.
Most often because of the gap between a pilot and a production system. A pilot runs on carefully curated data built around one hypothesis, while scaling requires the same result across many assets at once — what JPT (SPE) calls "the last mile" that not every project survives. Rystad Energy adds that AI usually doesn't raise the ceiling for the best operators; it pulls laggards toward it, and that takes time and investment, not a one-off rollout.
Only if the result can be checked. According to a Kept survey, 91% of Russian oil and gas specialists are ready to use AI but don't fully trust it — because of factual errors, dependence on data quality, and models that don't explain their own reasoning. The right response isn't to drop AI, it's traceability: knowing where a number came from and what formula produced it.
A coordinator and eight specialised agents — search, geology, reservoir simulation, surface facilities, economics, wells, business case and data standardisation — together run 34 calculation tools. Every step is logged: what data was used and what formula produced the result, so the number can be checked rather than just trusted.
Sources
- Research JPT (SPE) ↗ Rystad Puts Benefits of Digitalization, AI in Upstream Oil and Gas at $500 Billion
- Industry media ANGI ↗ Around 91% of specialists in Russia's oil and gas sector are ready to adopt AI (in Russian)
- Industry media Neftegaz.RU ↗ Rosneft rolled out an automated AI system in its well-drilling process (in Russian)
- Industry media IA Devon ↗ Experts forecast how AI will develop in oil and gas (in Russian)
- Research JPT (SPE) ↗ Case Study: Field Deployments of AI-Based IOCaaS Advancing Artificial Lift and Flow Assurance
- Research JPT (SPE) ↗ Scaling AI for Maximum Impact in Oil and Gas
We will show an AI valuation on one of your assets — free of charge
Give us a license number or coordinates — the platform will pull the geology, estimate reserves, production and NPV in minutes instead of weeks.