One of many upstream use cases. This example focuses on real-time drilling optimization, using WITSML data to reduce non-productive time and improve rate of penetration.
Non-Productive Time costs the drilling industry an estimated $25–$50 billion annually. Your drilling engineers watch screens filled with real-time WITSML data: WOB, RPM, torque, standpipe pressure, but the sheer volume makes it impossible to catch every signal.
When a kick develops or bit performance degrades, the cost of a delayed response is measured in hours of rig time at $50,000+ per day.
Traditional approaches were not built for the scale and complexity of modern operations.
Threshold-based alarms fire constantly. Operators learn to ignore them, and real signals get lost in noise.
Most drilling analytics happen after the well is drilled. By then, the NPT has already been incurred.
Lessons from previous wells exist in final reports, not in a system that can reason across them in real time.
Mechanical Specific Energy is calculated in spreadsheets, not continuously monitored and correlated with lithology changes.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents continuously compute MSE, detect torque-drag anomalies, and compare current parameters against offset well performance. Every metric is calculated via SQL on your WITSML data.
Every calculation is SQL you can verify. No black box.
Analyzing Stand #47 (8,200-8,230 ft)... MSE: 42,000 PSI (vs formation benchmark 28,000 PSI) → MSE efficiency: 67%, bit is drilling inefficiently Torque trend: +8% over last 3 stands → Recommend: Check for bit balling, consider flow rate increase
ALERT: Standpipe pressure dropped 150 PSI while pit volume increased 3 bbl over 4 minutes Pattern match: Pre-kick signature (89% correlation with historical events) Mud weight: 12.4 ppg, Formation pressure estimate: 12.6 ppg → IMMEDIATE: Flow check recommended
The Radar monitors drilling parameters for patterns that precede kicks, packoffs, and stuck pipe events. It correlates across multiple channels simultaneously.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Drilling, geology, and mud agents reason together. When parameters shift, each agent evaluates from its domain perspective before a consensus recommendation is issued.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Drilling Agent: ROP dropped 40%, suspect bit wear Geology Agent: LWD gamma shows formation change at 8,215 ft, entering harder interval Mud Agent: ECD within limits, no evidence of formation damage → Consensus: Formation change is the primary cause. Adjust WOB/RPM for new interval before pulling bit.
Lumina addresses the four strategic problems that hold operators back.
Continuous monitoring across every parameter, every stand, every well, without adding headcount to the rig.
Reduce NPT by catching issues in real time. Optimize bit runs by correlating MSE with formation changes.
Offset well performance, formation benchmarks, and drilling playbooks are encoded in the system for every future well.
Every MSE calculation, pressure trend, and anomaly score is SQL-verifiable against your WITSML data.
Specialized AI agents that power this workflow.
Drilling Engineer
Specializes in MSE optimization, torque-drag analysis, bit grade evaluation, and ROP benchmarking.
Geology & Formation
Monitors LWD data, formation tops, and lithology changes to contextualize drilling performance.
Data Scientist
Handles WITSML ingestion, real-time data streaming, and cross-well correlation.
This is just one of many use cases
Drilling performance 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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