One of many upstream use cases. This example focuses on emissions monitoring and power optimization, helping operators track GHG inventories and optimize energy costs against real-time grid pricing.
CCIR, ECCC, and EPA regulations require accurate GHG quantification. Fugitive emissions from LDAR programs generate thousands of records. Flare and vent data arrives from multiple sources. Your environmental team is building compliance reports manually.
Meanwhile, operators with power generation assets are leaving money on the table by not optimizing against real-time grid pricing (LMP). The data exists. The reasoning layer doesn't.
Traditional approaches were not built for the scale and complexity of modern operations.
Fugitive emission records are collected in the field and entered into spreadsheets. Analysis is quarterly at best.
Emission factors are applied uniformly. Equipment-specific performance and operating conditions aren't factored in.
Power generation and consumption decisions are made without real-time visibility into locational marginal pricing.
Flare volumes, vent records, equipment runtime, and weather data live in separate systems with no reasoning across them.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents quantify GHG emissions by correlating equipment runtime, fuel gas composition, flare efficiency, and ambient conditions. Every calculation maps to regulatory methodology (CCIR, Subpart W).
Every calculation is SQL you can verify. No black box.
Calculating Pad 12 Q3 emissions... Flare efficiency: 98.2% (operating normally) Fugitive components surveyed: 2,340 | Leaking: 47 (2.0% leak rate) Top emitter: Tank 12-B vent: 840 tonnes CO2e/yr → Total Pad 12 Q3: 4,200 tonnes CO2e → 12% above Q2, driven by Tank 12-B vent increase
ALERT: Facility-level emissions trending 8% above annual allocation with 3 months remaining Root cause: Compressor station C runtime increased 22% (unplanned maintenance bypass) LMP Analysis: Grid pricing at $45/MWh, facility self-generation cost $38/MWh → Recommend: Prioritize compressor C repair (emission reduction) + sell excess power at current spread
The Radar monitors emission trends, equipment performance degradation, and regulatory threshold proximity. It alerts before compliance deadlines, not after.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Environmental, operations, and commercial agents deliberate on decisions that balance emissions, production, and cost. Each position is data-grounded.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Environmental Agent: Tank 12-B vent contributing 840 t CO2e/yr. VRU installation eliminates 95% Operations Agent: VRU installation requires 3-day shutdown of Tank 12-B: 2,100 bbl deferred production Commercial Agent: Carbon credit value of 800 t CO2e at $65/t = $52K/yr + gas recovery value $28K/yr → Consensus: VRU payback in 8 months. Schedule installation during planned turnaround in 6 weeks.
Lumina addresses the four strategic problems that hold operators back.
Track emissions across every piece of equipment, every facility, with automated regulatory calculations.
Optimize power costs against real-time grid pricing. Quantify carbon credit opportunities.
Emission factors, equipment performance baselines, and regulatory methodologies are encoded, not in someone's spreadsheet.
Every emission calculation maps to specific regulatory methodology with full audit trail.
Specialized AI agents that power this workflow.
Environmental Analyst
Specializes in GHG quantification, LDAR analysis, flare efficiency monitoring, and regulatory reporting (CCIR, Subpart W).
Energy Optimizer
Monitors grid pricing (LMP), facility power generation costs, and optimizes buy/sell decisions for power assets.
Data Scientist
Handles cross-system correlation, emission factor databases, and weather data integration.
This is just one of many use cases
ESG & power management is one example of how Lumina reasons on operational data. Across Oil & Gas, every domain has use cases where AI agents can add value.
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