Predictivemaintenance
Predictive maintenance and downtime analytics for mid-size oil and gas operators.
Unplanned downtime is one of the largest and most measurable costs in operations. Before you buy a platform, find out which failures cost you most and whether your data can predict them.
The problem
Sound familiar?
- Failures are recorded in the maintenance system, but not connected to the production they cost.
- Alarms are so noisy that the team has stopped trusting them.
- A predictive-maintenance platform was quoted, but nobody knows whether the data supports it.
How it works
Narrow scope, measured value, then hand-over
A fixed-price diagnostic of under a month, with the baseline agreed up front.
- 1Rank equipment and failure modes by the production and cost they take away.
- 2Agree the baseline: downtime days, failures, cost per failure.
- 3Check whether your sensor, SCADA and maintenance data can predict the failures that matter.
- 4Build or specify the model for the top cases, then hand it over to your team.
What you get
- The failure modes that cost you most, in production and money
- Whether your data can predict them, and what is missing
- A fixed-price proposal with its KPI and baseline, or a clear “not yet”
Industry evidence
What operators report
- $120Msaved by Equinor since 2020 with predictive maintenance on more than 700 rotating machines.Equinor, 2026
- <20%of AI deployments track the value they deliver, which is why we agree the baseline first.McKinsey, 2026
Published by the sources linked; not Raffa Group results.