Company Background – Chevron
Optimizing Gas Turbine Reliability & Efficiency
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
Operational Inefficiencies and Reliability Risks
Relying on siloed data, basic vibration monitoring, and fixed pressure setpoints left operations stuck in a costly, reactive maintenance cycle. This lack of predictive insight not only drove up fuel consumption and CO₂ emissions, but also left critical offshore infrastructure vulnerable to catastrophic compressor trips and cascading production losses.
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
This resulted in:
- Excess fuel gas consumption
- Elevated CO₂ emissions
- Risk of catastrophic turbine failure due to undetected degradation
- Reactive troubleshooting rather than predictive intervention
- In offshore environments, even a single compressor trip can cascade into major production losses.
Solution
Connecting Offshore Data with Seeq
Chevron implemented a unified analytics framework using Seeq to connect and contextualize data from multiple process historians deployed across offshore assets.
01
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.
02
Self-Service Analytics
Using Seeq Workbench, engineers performed advanced analysis without data scientists. Capabilities included: dynamic monitoring, fuel gas modelling, pressure trending, forecasting trends, and creating early warnings. This shift enabled rapid iteration and validation by domain experts.
03
Operational Optimization
Analyzed separator and compressor scrubber pressure stability to replace fixed control with dynamic control. Reduced compressor discharge pressure while maintaining stability. Deployed predictive alerts for early detection of abnormal vibrations.
Benefits
Realizing the Benefits
Operational Efficiency
- Reduced fuel gas consumption by optimizing discharge pressure
- Improved turbine load management
- Reduced unnecessary maintenance interventions
Asset Reliablility
- Reduced risk of catastrophic turbine failure
- Early detection of abnormal vibration patterns
- Shift from reactive to predictive maintenance
Sustainability
- 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
