Upstream Oil & Gas
AI that reduces NPT, prevents incidents and keeps regulators satisfied
You get AI agents for drilling optimisation, production monitoring, predictive maintenance, and HSE documentation — all running on your cloud with full audit trails and governance built in from day one.
Built on your AWS, Azure or GCP tenant. BSEE and HSE compliant. Your data stays in your environment.
Proof of concept
To measurable ROI
AWS, Azure or GCP
In upstream, AI adoption is accelerating — but 56% of operators have not yet deployed it at scale
44% of exploration and production companies are already using AI. The ones that are not are losing ground on drilling efficiency, production uptime, and HSE compliance — while the cost of catching up grows each quarter.
The barrier is not the AI itself — it is deploying it in a way that your safety management system, your data governance policy, and your regulators can accept. That is the problem we solve.
of E&P companies already using AI in operations
AI in oil & gas market size in 2025, growing to $25B by 2034
reduction achievable with AI drilling optimisation agents
cut in unplanned production outages with AI predictive maintenance
From wellhead to compliance record — in one workflow
We connect your drilling data, production SCADA, seismic systems, and HSE platforms to AI agents that optimise operations, predict failures, monitor safety obligations, and generate the documentation your regulators and insurers require — all on your cloud
SCADA, drilling, seismic and production systems
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Well records
- HSE reports
- Regulatory submissions
What we deliver for upstream oil and gas operators
From exploration through production — governed, compliant, enterprise-ready
Drilling optimisation
Flag stuck pipe, kick, and circulation risks in real time.
ROI
Process
Ingest drilling telemetry
Real-time ROP, WOB, mudflow, torque, and downhole sensor data streamed to AI agent
Detect drilling hazards
ML models flag stuck pipe, kick, and loss-of-circulation signatures against historical patterns
Recommend parameters
Optimised drilling parameters delivered to driller's console with documented decision trails
Sources
Originally published: Azure Case Study — Shell; AWS Case Study — Equinor — reproduced for illustrative purposes
Seismic interpretation acceleration
Accelerate seismic interpretation and geological feature ID.
ROI
Process
Load seismic volumes
2D/3D seismic datasets ingested from interpretation workstations into cloud-hosted AI pipeline
Identify geological features
Deep learning models detect faults, horizons, and stratigraphic anomalies across the volume
Deliver interpreted outputs
Contextualised geological interpretations returned to geoscientists for validation and refinement
Sources
Originally published: GCP Case Study — TotalEnergies; Industry Data — Saudi Aramco — reproduced for illustrative purposes
Production monitoring and optimisation
Monitor gas lift and ESP performance; recommend adjustments.
ROI
Process
Monitor production systems
Gas lift, ESP, separator, and surface equipment data collected via SCADA and IoT sensors
Identify inefficiencies
AI compares actual vs optimal operating parameters and flags production losses
Recommend adjustments
Parameter optimisation recommendations delivered within operating envelopes for operator approval
Sources
Originally published: Azure Case Study — Chevron; GCP Case Study — BP — reproduced for illustrative purposes
Predictive maintenance
Predict compressor and subsea equipment failures weeks ahead.
ROI
Process
Ingest equipment telemetry
Vibration, temperature, pressure, and flow data from compressors, separators, and subsea assets
Predict failure windows
ML models forecast equipment degradation 3–6 weeks ahead with confidence scoring
Schedule maintenance
Prioritised work orders generated with parts requirements and documented risk justification
Sources
Originally published: C3.ai / Azure Case Study — Shell; AWS Case Study — Petrobras — reproduced for illustrative purposes
HSE monitoring and incident prevention
Real-time gas detection, alerts, and escalation trails.
ROI
Process
Monitor HSE parameters
Gas detection, safety-critical readings, and personnel location data streamed in real time
Detect unsafe conditions
AI flags parameter breaches, gas exceedances, and unsafe personnel movement patterns
Alert and escalate
Automated alerts with documented escalation trails and regulatory submission support
Sources
Originally published: Azure Case Study — BP; Industry Data — DNV — reproduced for illustrative purposes
Methane and emissions tracking
Detect leaks from satellite and drone data; automate reporting.
ROI
Process
Capture emissions data
Satellite imagery, drone surveys, and ground-based sensors fused into unified emissions dataset
Detect and quantify leaks
AI identifies methane plumes, quantifies emission rates, and pinpoints source locations
Generate regulatory reports
Automated emissions reporting structured for OGMP, EPA, and ESG disclosure requirements
Sources
Originally published: AWS Case Study — ExxonMobil; GCP Case Study — Equinor — reproduced for illustrative purposes
Well integrity management
Flag well barrier concerns before loss-of-containment events.
ROI
Process
Monitor well barriers
Annular pressures, temperature profiles, and barrier test results collected continuously
Assess integrity risk
AI models score barrier degradation against well integrity standards and flag concerns
Trigger intervention
Risk-prioritised alerts with recommended actions delivered before loss-of-containment events
Sources
Originally published: Azure Case Study — Equinor; GCP Case Study — Woodside — reproduced for illustrative purposes
Regulatory documentation
Auto-generate well reports for BSEE, HSE, and NORSOK filing.
ROI
Process
Collect operational data
Well reports, production records, and HSE events aggregated from operational systems
Structure to standards
AI formats data into BSEE, HSE, and NORSOK-compliant document templates
Review and submit
Draft submissions generated for human review before regulatory filing
Sources
Originally published: Azure Case Study — Aker BP — reproduced for illustrative purposes
Reservoir and production intelligence
Update reservoir models with production and pressure data.
ROI
Process
Integrate reservoir data
Production rates, pressure surveys, and fluid samples unified with geological models
Update reservoir models
AI-assisted history matching and model updates with explainable parameter adjustments
Support field decisions
Recovery forecasts and development scenarios delivered with confidence intervals for planning
Sources
Originally published: GCP Case Study — Saudi Aramco; Azure Case Study — Chevron — reproduced for illustrative purposes
VP, Wells & Subsurface Digital, bp
Chris Coley
"With AWS, we built a Model DevOps Framework that makes AI models for subsurface interpretation and drilling optimisation reusable and deployable at scale across our global well portfolio. What used to take months of custom engineering per asset now takes days — and every model we deploy builds on the one before it"
Originally published: AWS Customer Stories — bp — reproduced for illustrative purposes
How we work in upstream operating environments
Upstream AI must work within your safety management system, your data governance policy, and your existing SCADA and drilling information infrastructure. We map those constraints before we design anything.
Every agent runs on your cloud tenant. Your well data, your production data, your HSE records — none of it leaves your environment. Your safety management system, your data governance team, and your regulators can inspect the system directly.
01
Operational mapping
We identify your highest-value use case, map it to your safety management system, and define the data access and governance approach.
02
30-day proof of concept
A working AI agent on your cloud, integrated with your drilling or production data, with demonstrable output in 30 days.
03
Governance by design
Human-in-the-loop controls, explainable outputs, and audit trails built to satisfy your safety case and regulatory obligations.
04
Scale in 12–18 weeks
From one validated workflow to measurable operational ROI — with HSE documentation and compliance records your legal team can own.
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