By: Eric Warntjes (U.S. Energy Development Corp.) and Brandon Brown (ROAM-AI).
For decades, the oil and gas industry has approached ESP optimization the same way. This account comes from two vantage points: the technology side that builds these systems and the operations side that deploys them. From both sides, the conclusion has been the same.
Eric Warntjes’ Inside View at U.S. Energy:
When we began evaluating this approach at U.S. Energy Development Corp., my reaction was measured. We had worked with AI tools before, and the pattern was familiar: strong results in a controlled environment, then friction when the system met real field variability. My concern was not whether the model could identify opportunities. It was whether we could act on them fast enough to capture value, and whether the system would stay within operating boundaries our team could trust.
We activated the system across 12 Permian wells beginning in December 2025. What followed was not the dramatic transformation AI vendors tend to promise. It was something more useful: a steady, consistent improvement that built over time. The system executed thousands of adjustments across our well inventory, changes that previously would have required individual engineering review and manual execution for each one. Instead, every adjustment was evaluated against our defined operating envelope and executed automatically when it fell within the approved range.
Read the full article here: The Bottleneck Isn’t the Model. It’s the Execution. | Cover Story | Magazine
