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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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