Predictive maintenance that cut unplanned downtime for an oil & gas operator

The situation

For our client, an oil & gas operator, unplanned equipment failure was the expensive problem. Maintenance ran on a fixed calendar — inspect on schedule, replace on schedule — but equipment doesn’t fail on a schedule. Failures happened between inspections, and each one meant lost production, emergency call-outs, and real safety risk.

They had years of sensor data. What they didn’t have was anything turning it into a warning before something broke.

What we did

Advise. We started by working out which failures actually justified predicting — the ones that were both costly and, in the data, foreseeable. Not everything is, and chasing the ones that aren’t just adds noise.

Build. We built models on the operator’s own historical sensor data that recognise the signatures of a developing fault — vibration, temperature and pressure patterns that precede failure by days. The system flags a specific asset, explains why, and gives maintenance time to plan rather than scramble.

Run. Crucially, we didn’t hand over a model and leave. We run it — monitoring for drift, tuning as new failure modes appear, and keeping the alerts trustworthy so the team acts on them instead of ignoring them.

We designed the whole thing around being sometimes wrong: it shows its reasoning, keeps a human in the loop, and fails loudly rather than silently — the way any system people stake safety and production on has to.

The outcome

Unplanned downtime fell by 35 per cent as failures were caught days ahead and handled on planned maintenance windows. Maintenance costs dropped 18 per cent as work shifted from emergency call-outs to scheduled intervention — and the safety picture improved with it.

We continue to operate the platform under agreed reliability targets, and report on them each month.