One of many upstream use cases. This example focuses on well optimization and decline curve analysis, turning daily production data into actionable surveillance intelligence.
Your production engineers monitor hundreds of wells across multiple pads. Daily volumes arrive in spreadsheets. Decline curves are modeled in siloed tools. When a well underperforms, the question isn't whether you have data. It's whether anyone noticed in time.
The gap between measurement and action costs operators millions in deferred production annually. Not because the signal isn't there, but because no one had the bandwidth to look.
Traditional approaches were not built for the scale and complexity of modern operations.
Arps models in Excel are static. They don't auto-update, don't flag deviations, and can't correlate across wells or reservoirs.
Deferred production is often identified weeks after the event. By then, the well may have been offline for days without intervention.
SCADA, production accounting, lab data, and well tests live in different systems. Correlating them requires a data engineer and a ticket.
Each investigation is a custom project. The analysis isn't reusable, and the knowledge leaves when the engineer moves on.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Lumina agents ingest daily production volumes, fit Arps models (exponential, hyperbolic, harmonic), and flag wells deviating from expected decline. Every calculation runs as SQL against your data. No black box.
Every calculation is SQL you can verify. No black box.
Analyzing Well PAD-07-12... Fitting hyperbolic decline: Qi=850 bbl/d, Di=0.12, b=0.8 Current rate 620 bbl/d vs expected 710 bbl/d → 12.7% underperformance detected → Correlating with water cut trend: WC increased from 45% to 62% over 30 days
ALERT: Cluster of 4 wells on Pad 07 showing simultaneous GOR increase (+18% avg over 7 days) Hypothesis: Gas cap breakthrough in shared drainage area Confidence: HIGH (p=0.003 via correlation analysis) → Recommend: Reservoir team review pressure communication study
The Radar continuously scans production data for anomalies: sudden rate changes, water cut excursions, GOR spikes, ESP current deviations. It surfaces issues before they become incidents.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Production, reservoir, and facilities agents deliberate on underperforming wells. Each brings domain-specific reasoning: decline analysis, reservoir pressure, and surface constraints.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Production Agent: Well PAD-07-12 underperforming 12.7%, recommend workover Reservoir Agent: Offset well PAD-07-11 shows same trend, this is drainage, not mechanical Facilities Agent: Separator pressure increased 15 PSI last week, backpressure effect → Consensus: Adjust separator pressure first (low-cost). Monitor 7 days before workover decision.
Lumina addresses the four strategic problems that hold operators back.
Monitor hundreds of wells per engineer. Agents run surveillance 24/7, flagging only what matters.
Catch deferred production days earlier. Reduce unnecessary workovers by correlating root causes across domains.
Decline models, well behavior patterns, and operational playbooks are captured in the system, not in someone's head.
Every rate calculation, decline fit, and anomaly score is backed by SQL you can verify.
Specialized AI agents that power this workflow.
Production Analyst
Specializes in decline curve analysis, rate forecasting, deferred production accounting, and well test validation.
Reservoir Surveillance
Monitors water cut trends, GOR behavior, pressure communication, and drainage patterns across well groups.
Data Scientist
Handles data ingestion, schema detection, unit conversion, and cross-system correlation.
This is just one of many use cases
Production & reservoir surveillance 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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