Your engineers don't trust the AI. They're right not to.
General-purpose AI tools don't know what a GOR trend means, can't reconcile PRODML production volumes, and can't explain why today's allocation looks wrong. So your team ignores them and goes back to spreadsheets. That's not an adoption problem. It's a fit problem.
Built for daily production surveillance, ESP anomaly detection, decline analysis, and exception routing across PRODML, OSI PI, WITSML, historians, and operator workflows.
Before you evaluate any AI platform, answer these.
If these feel familiar, your team is not resisting AI. It is rejecting tools that do not fit the field.
When an ESP goes into pump-off, who builds the story?
How many hours pass before a field engineer has context, not just another alert in the queue?
How often does trust require someone to reconcile PRODML by hand?
Count the moments last quarter when AI output was ignored because production volumes, tests, downtime, or allocations could not be traced.
Has your data team spent months teaching a horizontal model petroleum basics?
If any of these hit home, the issue is not your team or your data. It is the stack.
Start with the production day your engineers are actually living.
STRATUM agents are anchored to operational moments, not abstract capabilities.
Your reservoir engineer is running Arps in Excel at 11pm.
The AI gave her a number she could not explain to her manager, so she rebuilt the decline curve herself. The Reservoir Agent was built for that engineer.
- Decline curve analysis with source evidence
- GOR, water cut, and rate trend interpretation
- Explainable assumptions before recommendations
Your production engineer is reconciling yesterday's numbers before trusting today's alarm.
The alert says something changed. It does not say whether the production volume, well test, downtime code, allocation logic, pump behavior, or bad telemetry deserves attention first.
- PRODML-native production volumes, tests, and allocation context
- ESP and artificial lift anomaly checks
- Rate, pressure, and downtime exception triage
Your drilling team is stitching together WITSML data after the fact.
By the time a horizontal tool understands the well plan, the crew has already made the call. The Drilling Agent starts from the language of the rig.
- WITSML-native event context
- NPT, deviation, and exception pattern review
- Human signoff where judgment matters
The question is whether your engineer will stake a morning report on it.
STRATUM validates agent output against deterministic petroleum engineering formulas so your team can audit the answer before acting on it.
Vogel, Arps, and nodal checks are not trivia.
They are the difference between generated confidence and operational confidence when a production engineer has to defend the call.
Production data is not just a time series.
PRODML volumes, well tests, allocations, downtime, equipment relationships, historians, and asset context are first-class inputs because surveillance breaks when production semantics are an afterthought.
Trust still needs the operator security envelope.
Deploy inside the customer cloud boundary using approved models, proprietary simulators, and operator-defined rules layered on top.
Start where the pain is already visible.
Deployment speed, security controls, and broader platform scope matter later. First, prove one workflow where generic AI has already disappointed the team.
PRODML-backed production surveillance
Prove the system can explain the exceptions your engineers already review every morning, with the production data trail attached.
Live production data workflows
Move from spreadsheet reconstruction to PRODML, historian, asset-context, and WITSML-adjacent review where drilling context matters.
Operator system of action
Expand only after the team trusts the reasoning, the evidence trail, and the escalation model.
Tell us where the workflow breaks.
Share the tools your team evaluated, where trust broke down, and what the PRODML-heavy exception workflow looks like today.