AI for the Oil and Gas Industry: Real Uses, Tools and Limits

AI in the oil and gas industry is mostly used for four things today: predicting equipment failures, automating drilling, interpreting subsurface data to find and develop reservoirs, and detecting emissions such as methane leaks. Generative AI is now being layered on top through industry-specific platforms, but the biggest documented results still come from machine learning applied to sensor and seismic data.

Below is a practical rundown of what is in use, who is doing it, and how to evaluate the claims you will see. Everything here reflects public information as of October 2026; this field moves quickly, so check the linked sources for updates.

Where AI is used across the value chain

Predictive maintenance and reliability

This is the most mature application. Machine learning models watch sensor data from pumps, compressors and valves and flag degradation before a failure causes unplanned downtime.

Shell is the best-documented example. In 2022, C3 AI announced that Shell’s program covered more than 10,000 pieces of equipment across upstream, manufacturing and integrated gas assets. In a 2026 expansion, C3 AI said the program now monitors more than 13,000 pieces of equipment and that Shell is adding AI agent-based root cause analysis and remediation, moving beyond simply detecting anomalies. Note that these figures are vendor-reported.

Drilling automation

Autonomous and closed-loop drilling has moved from pilots to real wells. In an SLB case study published in 2026, SLB says ExxonMobil Guyana used its Neuro autonomous directional drilling and DrillOps automation on deepwater wells, with more than 93% of operations executed autonomously and monitored from an onshore center.

At OTC 2026, ExxonMobil’s John Ardill said the company had drilled its first fully autonomous well in Guyana, with AI handling drilling and geosteering through the reservoir section. He said it outperformed conventional approaches, but no independent verification of that performance has been published.

Exploration and subsurface interpretation

AI is increasingly used to process seismic and well data faster than human teams can. In August 2026, World Oil reported that ExxonMobil used AI to identify four new exploration opportunities in Guyana’s Stabroek Block, combining deep learning, machine learning, high-performance computing and advanced seismic imaging.

Aramco took a different route and built its own model. Launched in March 2024, Aramco Metabrain AI was reported at 250 billion parameters and trained on roughly 90 years of company data. Aramco said it would analyze drilling plans, geological data and historical drilling costs to recommend well options, and forecast refined-product prices downstream. Aramco also said it planned a larger version; we could not confirm whether it has shipped.

Emissions and methane detection

Methane leak detection is a strong fit for AI because leaks are often intermittent and hard to catch with periodic manual inspections. Researchers have applied computer vision to infrared gas imaging and deep learning to satellite imagery to find plumes.

A JPT (Society of Petroleum Engineers) analysis makes a useful point: AI helps cleaner operations only when it is tied to measurable outcomes such as reduced methane, lower energy use or better equipment reliability. Treat broad “AI makes us greener” claims with caution.

Platforms you will run into

Most operators do not build everything themselves. These are the kinds of offerings that come up repeatedly:

SLB launched Lumi in September 2024. SLB describes it as open and modular, built on the Open Group’s OSDU data standard, using Cognite Data Fusion for production data, and available on major cloud providers or on-premises. It combines large language models with SLB’s own domain foundation models, and customers can add and train their own models.

Pricing for these enterprise platforms is not published; it is negotiated per contract.

Why data is the real bottleneck

A recurring theme across sources is that the algorithm is rarely the hard part. SLB’s own launch announcement says AI’s potential is held back by the complexity of the industry’s data ecosystems. Seismic files, sensor historians, drilling reports and maintenance logs often live in separate systems with different formats.

That is why open data standards like OSDU matter, and why the Shell program took years to scale. If your data is scattered and poorly labeled, an AI project will spend most of its budget on cleanup.

How to evaluate an AI project for oil and gas

If you are scoping AI for an operator, contractor or service company, these steps keep you grounded:

1. Start with a measurable problem. Pick something with a clear metric: unplanned downtime on a compressor fleet, drilling days per well, methane detected per site. The JPT analysis argues this matters more than model sophistication.
2. Audit your data first. Check whether you have enough historical sensor, failure or drilling data, and whether it is accessible in one place or follows a standard like OSDU.
3. Ask where the numbers come from. Many efficiency figures online come from vendor blogs or press releases. Ask for the operator, the asset, the baseline and the time period behind any claim.
4. Check deployment and security options. Find out whether the system runs in the cloud, on-premises or both, and which cybersecurity standards it follows. SLB, for example, states that Lumi follows NIST cybersecurity standards.
5. Plan for people, not just software. Autonomous drilling in Guyana was still monitored remotely from an onshore center. Decide who reviews AI recommendations and who is accountable for acting on them.
6. Pilot, then scale. Shell’s program started in 2018 and grew over several years. Expect a similar staged rollout rather than an overnight transformation.

A word on market forecasts

You will find many “AI in oil and gas market size” figures online, and they disagree widely, varying by several billion dollars for the same year depending on the research firm. They use different definitions of what counts as AI, so use them only as a rough signal of interest, not as a planning number.

On the wider picture, the IEA’s Energy and AI report focuses mostly on how much electricity AI and data centers will need and what that means for energy security and emissions. It is worth reading if you want context on how AI affects energy demand, including natural gas, rather than how oil companies use AI internally.

The bottom line

AI in oil and gas is real and already running at scale in maintenance and drilling, with exploration and emissions monitoring close behind. The most credible results come from large operators with years of clean data and a narrow, well-defined target. If you are evaluating tools, focus on the problem and the data before the model, and ask hard questions about any performance figure that comes only from a vendor.