API RP 2A Compliant DNV-RP-C203 Validated BSEE Accepted
A major oil and gas operator required a comprehensive fatigue life assessment for a 40-year-old fixed platform in the Gulf of Mexico. The platform was approaching its original design life, and the operator needed to determine whether continued operation was safe and economically viable.
Traditional fatigue assessment methods would have required 16+ weeks of engineering effort and could not incorporate the 15 years of operational monitoring data available from the platform's structural health monitoring system. We developed an ML-enhanced approach that reduced assessment time to 6 weeks while improving prediction accuracy by 23% compared to conventional S-N curve methods.
BSEE required a complete fatigue life assessment within 8 weeks to approve continued operations. Traditional methods would take 16+ weeks, creating regulatory risk.
The platform had 15 years of strain gauge data from 48 monitoring locations, but no established methodology existed to incorporate this operational data into fatigue predictions.
Preliminary assessments using standard S-N curves indicated several critical joints would exceed allowable fatigue damage factors, potentially requiring expensive underwater repairs or platform abandonment.
Ingested 15 years of strain gauge data (2.3 billion data points), metocean records, and operational logs. Automated quality control identified and corrected sensor drift, data gaps, and anomalous readings.
Trained gradient boosting models on validated FEA results for 120 representative load cases. The ML models learned the relationship between global platform response and local hot spot stresses at critical joints.
Used the trained models to reconstruct stress histories at all 847 fatigue-critical joints from operational monitoring data--something that would be computationally impossible with FEA alone.
Applied rainflow counting and Miner's rule to the reconstructed stress histories. Validated results against physical inspection findings and compared to conventional S-N predictions.
The prediction model used an ensemble of XGBoost regressors, with separate models trained for each structural zone. Key features included:
| Metric | Traditional FEA | ML-Enhanced | Improvement |
|---|---|---|---|
| Load cases analyzed | 50 (representative) | 52,560 (hourly for 6 years) | 1,051x more cases |
| Computation time per joint | 4-8 hours | 12 seconds | 1,200-2,400x faster |
| Total assessment duration | 16+ weeks | 6 weeks | 85% reduction |
The methodology was designed for regulatory acceptance:
ML-enhanced predictions showed 23% better correlation with actual inspection findings compared to conventional S-N curve predictions. The improvement was most significant for joints experiencing variable amplitude loading.
Incorporating operational data revealed that actual loading was less severe than design assumptions for most joints. 12 joints previously flagged as critical (damage factor > 0.8) were reclassified as acceptable (damage factor < 0.5) based on actual operational history.
By demonstrating that underwater repairs were not required for the 12 reclassified joints, the operator avoided approximately $2.1M in inspection and repair costs. The platform was approved for 10 additional years of operation.
| Joint Category | Traditional S-N | ML-Enhanced | Inspection Result |
|---|---|---|---|
| Jacket leg joints (n=48) | 0.45 - 0.92 | 0.31 - 0.68 | No cracks detected |
| Horizontal braces (n=156) | 0.22 - 0.78 | 0.18 - 0.55 | 2 minor cracks |
| Conductor guides (n=24) | 0.65 - 1.12 | 0.48 - 0.76 | 1 crack (repaired) |
| Splash zone joints (n=36) | 0.55 - 0.95 | 0.42 - 0.71 | No cracks detected |
FEA: SACS (Bentley) for global analysis, ANSYS for local stress analysis
ML Framework: XGBoost, scikit-learn
Data Processing: Python (pandas, numpy), Apache Spark for large dataset handling
Visualization: Plotly for interactive reports
Fatigue Analysis: Custom Python library for rainflow counting and Miner's summation
Reporting: Automated HTML report generation with Jinja2 templates
We help operators extend asset life through AI-native structural assessment.
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