Post on 18-Dec-2021
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Application of Machine Learning for Subsea Pipeline and Riser Systems Integrity Management
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Technology Week 2018
Partha Sharma
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Digital Twin: A Data Driven Integrity Management Approach for Offshore Assets
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Key Driver 1: IOT Sensors and Digital Inspection Technologies
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Raw
Data
Advanced
Calculation
Accurate
Result
RWT
Courtesy
NDT Global
RBP
RWT
Corrosion Rate & Remaining Life
New approach
DNV-RP-F101 Appendix D
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Key Driver 2: Low Cost Advanced Computing Resources
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Key Driver 3: Hi-Fidelity Physics Based Models
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Key Driver 4: Open Source Machine Learning Tools
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Case Study: Machine Learning Application Steel Catenary Risers Fatigue
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▪ Extremely complex physics governing fatigue
▪ Real time damage accumulation monitoring from site
motions measurements
Failure Mode Cause
Wave Fatigue Vessel MotionsWaves
VIV Fatigue (Riser Pipe)
VIV due to current profile, loop / eddy current
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Artificial Neural Network for SCR Fatigue
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Heave
Surge
Tension
Moment
Artificial Neural Network (ANN)
Time consuming FEA numerical simulations are
usually performed to assess the stress range
occurring in SCRs and deduce the fatigue damage
Once trained the ANN can produce the time series
of Tension and Moment at a fraction of the time
taken by FEA, thus actual field measurements can
be used to compute fatigue
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Case Study
▪ Vessel Type: Semi Submersible
▪ Region: GOM
▪ Water Depth: 4300 ft
▪ Pipe OD: 8.625 in
▪ WT: 1.13 in
▪ Steel Grade: X65
▪ SN Curve: D Curve
▪ Hang off: TSJ
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ANN Training Methodology
Run Orcaflex and export time series of vessel motions and Tension
and BM
Import Data into Tensor Flow and compute the weights for the ANN
Compare Orcaflex and ANN predictions for multiple wave seeds
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Physics ModelMachine Learning
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Results: Sample Time Series Comparison
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Orcaflex ANN
Mean 1565.5 1565.5
Std. dev 36.3 36.3
Min 1431.6 1431.5
Max 1697.6 1698.8
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Results: Sample Time Series Comparison
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Orcaflex ANN
Mean -0.1 -0.1
Std. dev 10.0 10.0
Min -39.8 -38.1
Max 38.7 37.0
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Results: Sample Time Series Comparison
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Orcaflex ANN
Mean -3.1 -3.2
Std. dev 19.8 19.7
Min -82.9 -78.3
Max 72.6 68.7
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Fatigue Life - All Sea states (Different Seeds)
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ANN Deployment Methodology
Collect wave measurement and
vessel motions
Run ANN on
Raspberry Pi
Compute fatigue and email results
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Integrating with Subsea Robotic Inspection
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Pictures Provided by Sonomatic
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Integrating With Robotic Inspection
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Hi- Fidelity Assessment Approaches
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Pictures Provided by Sonomatic
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Machine Learning Application for Corrosion
Failure Mode Cause Safeguards ITPM
Corrosion MIC, CO2, H2S Design to API-2RDMMS
Biocide InjectionCorrosion InhibitorCorrosion AllowanceCorrosion Coupon TopsideIntelligent Pigging
Biocide Injection Availability (%)Corrosion Inhibitor Availability (%)UT Topsides Piping (WT)Monitor Corrosion CouponMonitor WT (SS)Corrosion ModelInline Inspection and wall thickness measurement
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Machine learning to leverage the collected patterns
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Optimized
ILI frequency
Better Corrosion
Management
Predictions
Better
Corrosion
Developmen
t
Predictions
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Detailed Assessment Approach
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ILI Report
List
of
Defects
Simplified
EvaluationConservative
Result
Traditional approach
Raw
Data
Advanced
Calculation
Accurate
Result
RWTCourtesy
NDT Global
RBP
RWT
Corrosion Rate & Remaining Life
New approach
DNV-RP-F101 Appendix D
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Conclusion
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Utilize Big Data
technology
Combine data
scientists with
pipeline experts
Develop digital
deliverables
Enhance customer
experience
Increase
customer
interaction
Streamline
analysis work
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