Optimizing Gas Turbine Reliability & Efficiency
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Company Background
Chevron Thailand Exploration and Production, Ltd. is a major offshore upstream operator in the Gulf of Thailand, producing natural gas that supports Thailand’s national energy supply. Operations span multiple remote wellhead platforms where rotating equipment reliability is directly tied to production continuity and national energy security.
Gas turbines driving pipeline compressors are among the most critical and energy-intensive assets in this offshore network.Challenges
Gas turbines were responsible for a significant portion of platform fuel gas consumption. Operations relied on:
- Fixed, conservative compressor discharge pressure setpoints
- Time-based maintenance strategies
- Basic vibration monitoring without predictive insight
- Limited cross-asset visibility across offshore historians
- Excess fuel gas consumption
- Elevated CO₂ emissions
- Risk of catastrophic turbine failure due to undetected degradation
- Reactive troubleshooting rather than predictive intervention
- Manual data extraction from multiple process historians
Solution
Chevron implemented a unified analytics framework using Seeq to connect and contextualize data from multiple process historians deployed across offshore assets.
1.Historian Integration
Operational data from various historians (e.g., turbine parameters, vibration signals, pressure, temperature, fuel flow) was connected and streamed into Seeq in real time. This eliminated siloed analysis and manual data stitching.
1.Historian Integration
Operational data from various historians (e.g., turbine parameters, vibration signals, pressure, temperature, fuel flow) was connected and streamed into Seeq in real time. This eliminated siloed analysis and manual data stitching.
2.Self-Service Analytics
Using Seeq Workbench, engineers and reliability specialists performed their own advanced analysis without dependence on data scientists. Capabilities included:
Using Seeq Workbench, engineers and reliability specialists performed their own advanced analysis without dependence on data scientists. Capabilities included:
- Dynamic standard deviation monitoring on vibration signals
- Fuel gas consumption modelling
- Differential pressure trending
- Forecasting degradation trends
- Creating threshold-based early warnings
3.Operational Optimization
Separator and compressor scrubber pressure stability was analysed to replace fixed back-pressure control with adaptive dynamic control. Compressor discharge pressure was safely reduced from conservative levels while maintaining system stability. Predictive alerts were deployed to provide early detection of abnormal vibration behaviour and mechanical degradation.
Separator and compressor scrubber pressure stability was analysed to replace fixed back-pressure control with adaptive dynamic control. Compressor discharge pressure was safely reduced from conservative levels while maintaining system stability. Predictive alerts were deployed to provide early detection of abnormal vibration behaviour and mechanical degradation.
Benefits
Operational Efficiency
- Reduced fuel gas consumption by optimizing discharge pressure
- Improved turbine load management
- Reduced unnecessary maintenance interventions
- Early detection of abnormal vibration patterns
- Reduced risk of catastrophic turbine failure
- Shift from reactive to predictive maintenance
- Lower fuel gas usage
- Reduced CO₂ emissions from turbine operation
Organizational Impact
- Engineers independently built analytics models
- Eliminated manual historian exports and spreadsheet analysis
- Established a repeatable reliability analytics framework
