AI for Oil and Gas Operations
Agents, assistants and predictive models built on your SCADA, historian, maintenance data and field documents, with approvals where they matter.
AI Across Field and Office
Agents, assistants and models built on your field data, documents and systems.
Work Order Agents
Agents that turn alarms, inspection findings and field reports into draft work orders in your maintenance system, for a planner to approve.
- Work orders
- Approvals
- Audit trail
Procedure and Well File Assistant
Answers from procedures, well files and equipment manuals, with a link to the source document.
- Procedures
- Well files
- Cited sources
Document and Image Extraction
Data pulled from permits, run tickets, invoices and inspection photos into structured records.
- Permits
- Run tickets
- Inspection photos
Predictive Maintenance
Failure risk for pumps, compressors and rod lift equipment from sensor and maintenance history.
- Failure risk
- Condition monitoring
- Artificial lift
Production Optimization
Models that flag underperforming wells and suggest operating changes from production and sensor data.
- Well performance
- Rate forecasting
- Surveillance
Compliance Reporting
Emissions, incident and regulatory reports drafted from the data you already capture, reviewed before submission.
- Emissions
- Incidents
- Regulatory reports
From Use Case to Production
Three phases. Each one ends with something your team can evaluate before the next begins.
Phase 1
Assess
We pick use cases with a clear business case, check data quality and access, and agree on how success is measured.
- Use cases ranked by value and risk
- Data and access review
- Success criteria and evaluation set
Phase 2
Build and Evaluate
We choose a model for each task, connect it to your data and systems, and test it against real cases before go-live.
- Model choice by task, cost and data residency
- Retrieval and system integrations
- Evaluations and guardrails
Phase 3
Deploy and Monitor
We release with human approval where it matters, monitor quality and cost in production, and extend to more sites and workflows.
- Approvals and audit trails
- Quality, cost and drift monitoring
- Rollout across sites
Across the Value Chain
The same approach works from wellhead to refinery.
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Upstream
Well performance models and field alerts for drilling, completions and production.
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Midstream
Pipeline monitoring, leak detection and throughput forecasting.
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Downstream
Process monitoring, quality prediction and yield analysis in refineries.
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Remote Operations
Remote monitoring and edge models for unmanned sites.
AI for Oil and Gas: Common Questions
What oil and gas teams ask before starting an AI project.
Contact our teamCommon first projects are an assistant grounded in procedures and well files, extracting data from field documents, and predictive maintenance for artificial lift and compression. We start with the one that has the clearest business case.
Yes. We read from SCADA, DCS and historians through standard industrial protocols and connect to your maintenance system through its APIs.
They can draft them. You decide which steps need a planner or supervisor to approve, and every action is logged.
It does not have to. Models can run in your cloud tenant, on-premise or at the edge, and sensitive data can stay out of third-party APIs.
Models can run on edge hardware on site and sync results when a connection is available.
We agree on a baseline and a target measure in the assessment phase, then test the pilot against it before scaling.
Discuss Your Oil and Gas AI Use Case with Our Team
Tell us which assets or workflows you want to improve. We will review your data and propose a first phase.
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01Discovery
We review your processes, systems and objectives.
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02Proposal
Defined scope, timeline and investment for phase one.
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03Delivery
A production release, with a roadmap to scale.