Condition intelligence for the rotating equipment across upstream, midstream and LNG operations.
SibylCor helps oil and gas operators understand developing conditions across critical rotating equipment earlier—so they can reduce unplanned downtime, improve operational reliability, and optimize performance in high-value, high-risk environments.
An unprecedented wave of new liquefaction assets is approaching service.
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330+ bcm/yr
of new LNG export capacity set to come online between 2025 and 2030, from projects already under construction.
Source: IEA, Global LNG Capacity Tracker (updated October 1, 2026) (opens in a new tab)
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30%
more gas-turbine and reciprocating-engine capacity ordered in 2025 than OEMs could deliver, on record orders of 100 GW.
Source: Rystad Energy, Gas Turbine Report H2 2026 (opens in a new tab)
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10+ GW
of new island-mode power capacity planned by one LNG exporter, alongside 4.5 GW of gas-fired generation already supporting its operations.
Source: Venture Global announcement (October 15, 2026) (opens in a new tab)
Oil and gas facilities depend on compressors, pumps and turbines. Every new facility adds assets; every operating facility creates a continuing reliability obligation.
Detection creates warning. Diagnosis creates time.
Detection creates warning; diagnosis and prognosis create time; planning converts that time into avoided disruption.
Detection
- Tells you something moved
- Cannot tell you what, or how sure it is
- Inspection and downtime supply the answer
- Emergency work after a trip
Diagnosis and prognosis
- Failure mode, component, evidence and confidence
- Degradation, risk and remaining useful life
- Work window, people, parts and work orders
- Integration with the site’s CMMS, EAM and ERP systems
Constrained recovery
- 6 years gas-turbine lead times, with order books sold through 2027 and hot-section components the critical bottleneck
Compressors, turbines, processing trains and pumps
High-consequence production assets, using the sensors, historians, controls and maintenance systems already in place. No proprietary hardware.
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Compressors
Gas, fuel-gas and refrigerant compressors across processing, liquefaction and compression stations
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Pumps
Process, transfer and utility pumps across production and processing trains
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Turbines & Drivers
Gas turbines and electric motors driving compressors, pumps and generators
A small valve fault should not become a forced shutdown.
Physics-based equipment models calculate parameters that cannot be directly measured and reconcile them with measured operating data. Machine learning and diagnostic reasoning evaluate those relationships to identify the failure mode and quantify diagnostic confidence.
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Observed
No sensors are in alarm, but a motor-driven compressor’s power consumption slightly increases, and the model cannot reason it away.
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Reconciled
Heat and mass balance checks the drift against the physics of the compression system.
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Ranked
Competing causes across valve, compressor and drive train are ranked by probability and evidence.
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Recommended
Maintenance is scheduled for the next planned window, before the condition becomes a failure.
What this changes
- Shutdown planned, not forcedThe work happens in a scheduled window instead of after a trip.
- Reliability maintainedThe fault is corrected before it cascades from valve to compressor to turbine.
- Parts and specialists readyThe likely repair is known before the window opens.
With SibylCor, the work is planned before the chain starts.
From condition to planned work.
The output is actionable maintenance planning: priorities, scheduled interventions, required skills, inventory checks, long-lead parts ordering and work-order coordination.
The risk is not just the price of a machine. It is lost output, scarce repair capacity and delays in restoring redundancy.
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Reduce unplanned downtime
Developing faults in compressors, pumps and drivers are diagnosed before they trip the train.
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Improve operational reliability
Every recommendation carries its failure mode, supporting evidence and confidence.
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Optimize performance
Reliability, productive capacity, risk and maintenance expenditure are optimized together.
SibylCor is designed to compute the Total Cost of Risk for each monitored asset: process unavailability, degraded efficiency, emergency procurement and missed operating windows.
“Mechanical integrity failures rarely arise from a single, isolated cause. Instead, they typically arise when equipment degradation, inspection effectiveness, maintenance quality, or original design assumptions fail to keep pace with aging facilities or evolving operating conditions.”
Independent industry context. Quotation does not imply endorsement of SibylCor.
Who we’d like to talk with
LNG and midstream operators
Liquefaction trains, compression stations and the drivers behind them.
Upstream and offshore operators
High-consequence production assets where access and downtime are expensive.
Engineering and EPC firms
The right sensor package specified at design, so every train is ready for failure-mode diagnosis.
We’d welcome a conversation about the equipment and maintenance challenges that matter most to your operations.
Or email info@SibylCor.com
Start with one critical asset class
One critical asset class, one narrow engagement—starting where the consequence of failure is already understood, with a low-risk path to validate SibylCor.