Predictive maintenance AI for oil and gas
Predictive maintenance AI for oil and gas
Give reliability teams a clearer basis for deciding which equipment to inspect and when. NeoBram combines suitable condition data and maintenance history in a focused trial, with your engineers defining the failure concerns and the actions a useful warning would support.
Choose equipment where warning time can change a decision
Start with one pump, compressor, turbine or comparable asset group and a recurring reliability concern. An earlier warning may help plan an inspection, arrange a spare or align maintenance with an available window. Your reliability specialists identify the failure modes, current monitoring practice and practical response time.
Rotating-equipment analytics needs the context of load, speed, starts, stops and operating regime. An unusual signal during a planned transition should not automatically become a failure warning.
Follow an illustrative equipment-health review
A reliability team is reviewing changing behaviour on a compressor or an artificial-lift asset. The workflow brings suitable condition signals together with operating state, interventions and maintenance history. It prepares an evidence pack for the engineer, distinguishing an unexplained change from missing data or a known operating transition.
The engineer decides whether to seek further inspection, continue observation or plan work through existing procedures. For remote assets, the practical lead time includes personnel, transport, permits and spares; an alert is useful only if it can change a feasible decision.
- Identify the asset, duty and failure concern before selecting signals.
- Show when the change appeared and which observations support it.
- Record the reviewer’s disposition and any later inspection findings.
- Check the time and workload required to turn a warning into an approved action.
Connect condition signals with maintenance evidence
Relevant inputs may include vibration features, temperatures, pressures, motor current, process conditions, inspection findings and CMMS or EAM work orders. We check asset identities, timestamps, sensor quality and what happened before and after a repair. A closed work order is not always a confirmed failure label.
When failures are rare or poorly recorded, anomaly detection may offer a useful first step. It identifies behaviour worth checking without claiming a known failure cause or date. Additional instrumentation is considered only where the failure mechanism and decision justify it.
Assess ESP failure warnings and workover planning
For electric submersible pumps, candidate work can combine operating history with installation, intervention and failure records. The question is whether a warning could help an engineer investigate deterioration or prepare for a workover. Changing well conditions and equipment configurations must be accounted for in the assessment.
Remaining useful life estimates need sufficiently comparable run histories and meaningful end-of-life evidence. Where those are missing, a ranked condition review may be more defensible. Lift-performance optimization and production settings belong in a separate upstream analysis.
Test warning quality against current practice
Evaluate missed events, false alerts, warning lead time and time spent investigating. Historical tests must avoid using information that would only have become available after the event. Where feasible, a shadow trial lets engineers review new warnings before those outputs influence maintenance decisions.
The commercial test is whether better information changes an inspection or maintenance plan usefully. Downtime avoided needs supporting intervention evidence; alert counts alone cannot establish savings. Track planning time and confirmed cost changes separately.
Fit the output into the maintenance workflow
A useful alert includes the equipment tag, relevant measurements, changed conditions and the reason for review. We agree who receives it, how findings are recorded and when repeated alerts are suppressed or escalated. Any connection to work-order systems is scoped with their owners.
Remote or restricted sites may need local processing and store-and-forward data handling. We assess connectivity, licences, updates and support arrangements before confirming an offline design. Maintenance engineers retain intervention decisions, and analytics does not replace protection systems or required inspections.
Assess a bounded asset group before a wider rollout
A first scope can provide a signal and event inventory, data-quality findings, a baseline comparison and an alert-review prototype. Check planned shut-ins, changed operating modes and post-maintenance periods. A test dominated by ordinary operation may say little about rare failures, so state the uncertainty rather than promising a universal warning rate.
Measure useful lead time, missed events and unnecessary investigations alongside review effort. Distinguish an equipment-health warning from an estimate of remaining life. Any avoided downtime or workover claim needs an agreed comparison and evidence of the action taken. Site owners approve interventions; the application remains within the agreed advisory boundary.
More clarity
Questions and answers
Can AI predict every equipment failure?
No. Some failures have little observable warning. Missing signals, new failure modes and changing operation can also limit performance. Tests must include missed events as well as successful warnings.
Do we need new vibration sensors?
Not necessarily. We assess current signals against the failure mechanism first. Some questions need higher-quality or more frequent measurements; others can start with existing historian and maintenance records.
Can we start without labelled failure data?
Sometimes. Anomaly detection can highlight unusual behaviour for review. Predicting a specific failure mode or remaining life usually needs stronger event histories and validated labels.
How do you avoid overwhelming engineers with alarms?
Set thresholds and review rules against the team’s response capacity. Evaluate false-alert burden, recurring conditions and investigation effort during the trial, then adjust within the agreed acceptance criteria.
Will the system create work orders automatically?
That is a separate workflow decision. The initial output can be an engineer-reviewed list. Any approved CMMS integration needs agreed permissions, evidence fields and responsibility for issuing work.
Can the same model be used on every asset?
Not without evidence. Equipment design, duty, instrumentation and operating practices can differ. Start with a defined group, examine its limits and test before extending to other assets or fields.
How should remote-site response constraints affect the project?
Define the warning time needed to arrange safe access, personnel and materials. Include investigation workload and unavailable inputs in acceptance testing so the output fits an actionable maintenance process.
Start with one business problem