AI for oil and gas
AI for oil and gas
Help production, reliability and engineering teams spend less time assembling information and more time deciding what to do. NeoBram develops focused AI workflows around your operating data, technical records and domain experts, beginning with one useful decision.
A practical first scope
Choose the first decision: review a production pattern, assemble equipment evidence or assess hardware lifecycle records.
- Possible output: a scoped production analysis, reliability investigation pack or hardware research record. Each requires different source material and tests.
- Your contribution: an asset or production specialist, approved histories and the operating context needed to interpret them.
- Quality and value: check relevance, source dates and missing evidence before comparing investigation effort. Production improvement or avoided failures need separate evidence.
- Next decision: select one asset group or operating question, assess the available evidence and agree the next spending decision.
Support upstream production decisions
Production teams may need a more useful forecast, a manageable well-surveillance list or evidence for reviewing artificial-lift performance. Machine learning can be assessed for these tasks using production histories, well tests and operating events. The intended output is a forecast, a ranked investigation or an engineer-reviewed recommendation with clear limits.
Start with the well or field decision and compare results against current practice. Production forecasts, equipment failure warnings and reservoir studies require different evidence and should have distinct project scopes.
Assemble a reliability investigation pack
For an illustrative compressor review, a workflow could align operating trends, alarm history, recent interventions and equipment documents around a period of unusual behaviour. A reliability engineer receives a compact evidence pack with source references and data gaps. Test whether the pack reduces repeated information gathering; diagnosis and intervention decisions remain with the responsible specialists. Comparable duty and operating conditions are essential to interpreting the trends.
- Align asset tags, time zones, units and maintenance-event dates before comparison
- Show normal operating changes alongside unusual readings to support interpretation
- Keep missing signals and uncertain event timing visible in the review pack
Give reliability teams earlier evidence to investigate
Pumps, compressors, turbines and ESPs may offer opportunities to connect condition signals with maintenance histories. A useful trial tests whether warning time can improve an inspection, spare-parts decision or maintenance window. Missed events, false alerts and engineering effort matter as much as detection performance.
Your reliability specialists define failure concerns and intervention rules. We assess what existing measurements can support before recommending additional instrumentation.
Find technical evidence and prepare work packs faster
A knowledge assistant can help engineers retrieve relevant procedures, manuals and technical records with source references and revision context. Document workflows can prepare extracts, compare revisions or flag missing information in turnaround work packs for review. The starting benefit to test is less time searching, copying and reconciling information.
Asset tags and document permissions must remain consistent. An answer based on an outdated procedure or a similarly named component needs to be caught before it influences work.
Assess specialist applications on their own merits
Reservoir modelling, history matching and drilling optimization require specialist expertise, appropriate models and their own validation. We confirm the available expertise, data and validation approach before accepting that work.
Leak-anomaly and emissions-review ideas also require reliable observations and a defined investigation response. Estimates must remain distinguishable from measurements and cannot substitute for required safety or compliance procedures.
Fit restricted and remote operating environments
We assess approved historian, SCADA and maintenance-system interfaces, as well as extracts suitable for an initial study. On-premises, edge and offline options depend on local data, hardware, licences and the update and support arrangements. Intermittent connectivity needs an explicit response, including visible data age and understood fallback behaviour.
Agree a measurable first project
Choose the workflow owner, representative records and the decision a better result would change. NeoBram scopes data preparation, model and application engineering, integration and technical evaluation with your specialists. Compare analysis time, decision quality and operational outcomes separately; savings require evidence from actual work.
A first evaluation should support a clear continue, adjust or stop decision. We agree who handles support, updates and day-to-day operation before work starts.
Make the investigation queue actionable
Agree how findings enter the existing reliability process, including who reviews them and which evidence is needed to progress an investigation. The delivery scope can include an asset mapping, reviewed event library, investigation template and test report. Measure time to a reviewed finding, nuisance-alert burden and whether the evidence changes a useful maintenance decision. A detected anomaly alone does not establish avoided downtime or the cause of an equipment problem.
- Test routine starts, stops and duty changes as well as known problem periods
- Compare review workload with the current surveillance or reliability method
- Record any claimed avoided expense separately, with the event and assumptions supporting it
Discuss one asset or operating workflow
Tell us what your team needs to notice, find or decide sooner, and which records they use today.
More clarity
Questions and answers
Do we need an in-house AI team?
No. You need available production, reliability or engineering experts and an accountable workflow owner. NeoBram provides the agreed AI engineering; your specialists assess operational relevance.
Can you work with our historian and maintenance system?
We assess approved interfaces, extracts, asset identifiers and permissions. The integration plan depends on the systems, data quality and access available for the project.
Can sensitive information stay inside our network?
A private or offline design may meet that requirement. We check all data paths, including model dependencies, updates, telemetry and support, before confirming the boundary.
Where should an oil and gas AI project start?
Choose a recurring decision with accessible evidence and a clear response. Production surveillance, equipment-health review and technical-document search have different readiness needs; assess each against the value of a better decision.
Will AI make operating changes automatically?
The starting pattern supports engineers and operators with information and reviewed recommendations. Any control integration needs a separately defined engineering and approval scope.
What if operating trends and maintenance records use different asset names?
Build and review an asset mapping before combining them. Ambiguous matches should be flagged for a knowledgeable owner. Incorrectly joining two assets can create a convincing but misleading investigation record.
Can we assess value before a rare equipment failure occurs?
You can measure evidence preparation, review effort and performance on suitable historical events. Those results support a workflow decision, but prospective failure detection and avoided downtime remain separate outcomes requiring sufficient operating evidence.
Start with one business problem