Oil and Gas Global Network

Ask a generative engine a narrow operational question — who handles sour-gas treating on a compressed turnaround, who has run managed-pressure drilling in a specific basin, who recycles produced water on pads with almost no surface area — and the answer often names a company with forty people instead of the company with four thousand. The larger firm has more revenue, more projects, more press coverage, and a much older domain. It still loses the answer. That looks like a bug in the model.

Most energy companies have spent the last two years asking whether AI belongs in the reserves process. That question is already behind them. Machine learning sits inside the decline-curve software, the seismic interpretation stack, the type-curve libraries, and the analog-well selection tools that feed a modern reserves estimate. It arrived through vendor upgrades and license renewals rather than through a board decision, which is exactly why nobody logged it.

Most energy operators can now tell you which AI tools they use. Far fewer can tell you what those tools decided last quarter, who reviewed the output, and on whose authority the company acted on it. That second question is the one that matters in 2026, because it is the one a regulator, a partner, or opposing counsel will actually ask. “Defensible AI” is the shorthand for being able to answer it.

Almost every operator and service company now has an AI pilot running somewhere. A model that reads drilling reports. A copilot for the maintenance team. A tool that summarizes contracts or triages tickets. The demo went well, the room nodded, and everyone agreed it was the future. Then the thing quietly stalls in a corner of the business, never quite trusted enough to run a real decision. If that describes a project at your company, you are not behind. You are exactly average.

Frontline crews who keep energy and infrastructure running tell the moment the system broke — and the call that turned it around. Hosted by Andrea Tsakanikas. Sponsored by Crew Logistics – Workforce lodging, run as one operation.

Ask ChatGPT, Claude, or Gemini who a buyer should hire for a job in oil and gas. A completions program. Produced-water handling. A gas-processing dedication. Power for a data center. You get back the same handful of big names almost every time, and most of the companies actually fighting for that work never come up. I got curious enough about this to stop guessing and measure it. A few weeks of running real buyer questions past the assistants and reading what came back.

A procurement lead at an operator has a short list to build. Ten years ago she’d have called three people she trusts and run a Google search.

Hosted by Kailey Euceda. The Energy Basket Podcast focuses on industry problems within a systemic framework. The weave of the basket carries its distributed load. The hinge points are the cross hatches where unmitigated circumstances can overwhelm interconnected systems.

Drive across northern Pennsylvania today and it’s easy to believe the Marcellus Shale has settled down. The endless lines of water trucks are fewer, drilling rigs don’t dominate the skyline the way they once did, and the daily headlines announcing another wave of permits have largely faded. To someone watching from the outside, it might appear the industry has reached its peak and is slowly winding down. That assumption couldn’t be further from the truth.

Somewhere in your organization, a capable manager is about to put an AI tool into a workflow that touches a material decision — a reserves narrative, a maintenance call, a trading input, a hiring screen — and the approval for it will be an email thread, not a board decision. That is the gap. Most energy companies have a rigorous process for approving a new drilling program or a capital lease, and almost no process for approving software that quietly participates in the same judgments.