One of many midstream use cases. This example focuses on compressor station performance and flow assurance, optimizing throughput while managing energy costs and equipment health.
Compressor stations consume enormous energy to move product. Operating them efficiently requires balancing throughput demands, equipment health, gas quality variations, and real-time energy pricing, all simultaneously.
Most operators run compressors based on historical setpoints and operator experience. The optimization opportunity is significant, but the variables are too numerous for manual analysis.
Traditional approaches were not built for the scale and complexity of modern operations.
Compressor maps are set during commissioning and rarely updated. Operating conditions drift, but setpoints don't.
Equipment is maintained on schedule, not on condition. This leads to both over-maintenance (waste) and under-maintenance (failures).
Compressor scheduling doesn't account for real-time grid pricing. Peak-hour operation costs significantly more.
When gas quality changes (BTU content, H2S, CO2), operators adjust manually. The lag between detection and response costs throughput.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents continuously monitor compressor efficiency curves, compare actual vs. design performance, and identify degradation trends. Every metric is computed from your SCADA data via SQL.
Every calculation is SQL you can verify. No black box.
Station CS-04 Compressor Unit B Performance Report: Polytropic efficiency: 78.2% (design: 85%) Degradation rate: 0.3% per month over last 6 months Surge margin: 12% (healthy) Energy cost at current efficiency: $4,200/day Energy cost at design efficiency: $3,600/day → Fouling-related efficiency loss costing $600/day ($219K/yr)
ALERT: Station CS-04 discharge temperature 12°F above trend line Vibration: 1x component increasing, possible rotor imbalance Flow: Throughput down 3% despite stable suction pressure → Pattern match: Early-stage fouling + bearing wear (78% confidence) → Recommend: Plan wash within 14 days to prevent forced outage
The Radar monitors across all stations simultaneously: vibration trends, temperature excursions, flow imbalances, and custody transfer discrepancies.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Throughput, maintenance, and energy agents reason together on scheduling, maintenance windows, and operational priorities.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Throughput Agent: CS-04 Unit B needs wash. Schedule during low-demand window (Tuesday 02:00-06:00) Maintenance Agent: Bearing vibration suggests 30-60 days to failure. Combine wash with bearing replacement Energy Agent: Grid pricing forecast shows $85/MWh peak Tuesday vs $42/MWh off-peak. Shift load to Unit A during maintenance → Consensus: Combined wash + bearing at Tuesday off-peak. Redirect flow through Unit A. Net savings: $18K vs. separate interventions.
Lumina addresses the four strategic problems that hold operators back.
Monitor every compressor, pump, and station parameter continuously without adding control room staff.
Optimize energy scheduling against grid pricing. Combine maintenance activities to reduce downtime and cost.
Equipment performance baselines, degradation patterns, and maintenance best practices are encoded, not dependent on operator experience.
Every efficiency calculation, degradation trend, and cost analysis is SQL-verifiable against your SCADA historian.
Specialized AI agents that power this workflow.
Operations Optimizer
Specializes in compressor performance monitoring, flow assurance, linepack management, and throughput optimization.
Energy Optimizer
Monitors real-time grid pricing, optimizes compressor scheduling against LMP, and tracks carbon intensity.
Data Scientist
Handles SCADA data integration, equipment performance baselining, and cross-station correlation.
This is just one of many use cases
Throughput & operations is one example of how Lumina reasons on operational data. Across Oil & Gas, every domain has use cases where AI agents can add value.
Not ready to commit? Stay up to date as we release new capabilities and industry-specific agents.
View all Oil & Gas use cases