Calgary AI + SCADA Oil & Gas Playbook (2026)

Calgary oil and gas sites get the fastest AI value by targeting compressor trips, separator instability, and alarm overload. Practical success means AI improves operator decisions without bypassing deterministic control layers.

Priority Use Cases

• Predict separator upset 10-20 minutes ahead using pressure, temperature, and flow patterns.
• Rank nuisance alarms by production impact to cut alarm flood during transient operations.
• Generate shift-level troubleshooting summaries from SCADA events and operator notes.

Data Readiness Checklist

• At least 6-12 months of historian data from stable tag naming and units.
• Event alignment between alarms, trips, and maintenance work orders.
• A clear mapping between process area, equipment IDs, and control strategy names.

System Integration Pattern

• Deploy AI services read-only beside SCADA/historian; no direct write path to control logic.
• Expose model confidence and top contributing signals in operator screens.
• Route recommendations through supervisor acknowledgement before action.

Governance and OT Safety

• Define AI output owner per unit (operations + controls) with escalation matrix.
• Segment model services in OT DMZ and log every recommendation payload.
• Require rollback mode that disables AI recommendations within minutes.

KPIs to Track

• Unplanned trip frequency per month
• Alarm flood minutes per shift
• Mean time to diagnose process upsets
• Operator intervention count for repeat faults

30-60-90 Day Plan

• Day 1-30: baseline trip and alarm metrics, validate data quality, lock use-case scope.
• Day 31-60: run shadow-mode predictions and compare to operator decisions.
• Day 61-90: enable supervised recommendations in one area and review weekly outcomes.