Turn Oil & Gas Operational Uncertainty into Performance and Uptime Advantage
From wells and drilling rigs to LNG trains and refinery units, every deviation carries a cost. Spector.ai turns those signals and opportunities into foresight and optimal set points.
AI agents continuously understand how your assets and processes behave, predict degradation and operational upsets before they impact production, diagnose root causes, and determine the next best action. They continuously optimize operating conditions as equipment health, feedstocks, constraints, and market demands change. The results:
One Agentic Twin™ powers end-to-end production intelligence.
Oil and gas operations lose money two ways: equipment fails without warning, and assets or systems run below their optimum for months without anyone noticing. We find both while there is still time to act.
Well production optimization
Production maximized well by well, including enhanced oil recovery and gas lift.
Asset reliability across the chain
ESPs, drilling rigs, upstream production, LNG trains and refinery equipment.
Refinery yield optimization
What-if analysis and RTO to process varying crudes and meet shifting product demand.
Loss opportunity prediction
Abnormal heater coking and catalyst degradation caught before margin is lost.
Measured on real units.
Refinery FCC Unit Optimization
Design-era operating set points left the FCC unit operating below its true production, yield, and profit potential as feedstock, demand, and plant behavior changed.
A physics- and data-driven plant predictive model, combined with an AI optimizer, identifies optimal operating targets within real plant constraints. Recommendations are delivered to the DCS engineer every four hours and after significant operating changes.
Added $8–9M annually for a 105 kbpd FCC unit, while enabling rapid what-if analysis for feedstock and demand changes.
LNG AGRU Performance Analysis, Troubleshooting & What-If
An LNG producer needed monitoring and analysis of KPIs and parameters for the acid gas removal unit of an LNG train, troubleshooting with RCA and recommendations on real-time operations, and what-if analysis.
Hierarchical KPIs with alerting, a GenAI Troubleshooting Agent for conversational analysis, and what-if analysis for real-time scenarios such as processing higher CO₂ gas while adjusting unit parameters to see the impact on throughput and the CO₂ specification to the cold box.
2.5% increase in profit across production, energy and efficiency, with scenarios tested before changes reached the train.
Centrifugal Compressor Predictive Maintenance
Repeated gas-compression shutdowns, most of them unplanned, caused significant production losses, emergency maintenance, and reduced asset availability.
Equipment-specific anomaly and predictive models identify developing issues, including process-related causes upstream of the machine. GenAI-enabled failure-mode analysis, remaining-time-to-fail predictions, and what-if recommendations support proactive maintenance.
Up to $7.5M saved per avoided shutdown, with 100% asset availability targeted and shutdown costs of up to $210K per hour avoided.
Midstream Pumping Systems Reliability
Frequent trips across more than 100 critical product-transfer pumps caused unplanned downtime and emergency maintenance, with root causes often unclear.
Fleet-wide anomaly detection, predictive failure models, GenAI-based FMEA and root-cause diagnosis, remaining-time-to-fail prediction, and pre-emptive maintenance recommendations.
More than $320K downtime reduction per predicted failure, up to two weeks’ advance warning, and 42% lower mean time to repair.
Hydrocracker Performance Monitoring, Analysis & Troubleshooting
A complex cracking refinery needed a way to monitor and analyze KPIs and parameters of the hydrocracker unit, and to run troubleshooting tasks: information extraction, RCA and actionable recommendations on real-time equipment, process and plant performance.
Plant hierarchical KPIs with an alert system for any off-track KPI or parameter, plus a GenAI Troubleshooting Agent that answers in the format the question needs: text, tables, correlation heat maps, charts or causal analysis.
3% increase in profit across yield, production, energy and efficiency, with faster decisions at plant level or drilled down to any level of the hierarchy.
Enhanced Oil Recovery with Gas Lift Riser Optimization
Enhanced oil recovery on a deepwater subsea tieback depends on how gas lift is injected at the riser base. Operators had no real-time view of the optimum injection rate, and slug formation in the gas lift riser interrupted flow and forced conservative operation well below the production the system could sustain.
Three models run against live SCADA data: an EOR Predictive Model relating oil production to reservoir parameters and choke valve outlet pressure, an AI and physics-based simulation model covering choke outlet through the riser base and gas lift riser to the host platform, and a host platform model for separation and gas processing. A Slug Prediction Model flags slug formation early, identifies the cause, and switches the AI Optimizer’s objective from oil production maximization to gas lift flow while the upset is mitigated. The optimizer manipulates outlet pressure, gas lift flow, host separator pressure and temperature, compressor outlet pressure and other wells’ gas lift flow within gas processing capacity and operating limits, and optimum set points are implemented in SCADA.
2.8% more oil production, slug events predicted and mitigated before they interrupt flow, and injection decisions made from live data rather than periodic sampling at remote locations.
Start small, prove value, scale on ROI.
Two to six assets
One unit or system, one solution. Implementation fee at cost, so the first step is not a capital decision.
Validation on live data
Measured against success criteria agreed before the pilot starts, on live plant data.
Facility, country or group
Multiple solutions on the same twin. Economies of scale plus an annual SaaS subscription.
Value-based commercials. Customers routinely reach 15x+ ROI at scale, and savings from early use cases fund the next ones.