TECHNICAL PAPER - Global Offshore Engineering & Riser Design

TECHNICAL PAPER

Emerging Solutions for Offshore

Asset Integrity Challenges

R. Thethi

SPE Workshop September 2020

SPE WORKSHOP

Emerging Solutions for Offshore Asset Integrity Challenges

10 – 11 February 2020Hilton Kota Kinabalu, Malaysia

Learn more at www.2hoffshore.com

SPE WORKSHOPEmerging Solutions for Offshore Asset Integrity Challenges

Implementation of Advanced Digital Technologies in Subsea Asset Life Extension

Ricky Thethi

Global Director

2H Offshore Engineering


3

Introduction

• Riser structural monitoring is a proven method for early detection of anomalies allowing Operators to address ahead of time and reduce remediation costs.

• Structural monitoring can also be used to accurately determine remaining life and justify life extension beyond the original service life

• Instrumenting the riser with sensors can be costly activity requiring interface data from and access to the host vessel

• An alternative lower cost approach is to use Machine Learning technology to develop a real time structural digital twin of the risers using the finite element model to provide training data


www.acteon.com4

Physical asset Real Time Digital Twin

• Wave, current and vessel motion stimuli

• Riser incurring fatigue damage over time

• Any corrosion will accelerate fatigue damage and reduce remaining life

• Bend stiffener region is the fatigue hotspot

• Steel tensile and pressure armour layers within flexible x-section are critical areas

Input• Vessel motions• Vessel GPS and heading• Environmental loads• Riser internal pressure

Response Model• Hot spot loads and stresses• Tensile and pressure layers• Wire stresses• Fatigue damage accumulation

Output Visualisation• Fatigue tracking GUI• Dashboard and alerts• Remaining life status• Impact of changing operational

parameters• Hosted on cloud• Web accessible


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Key Benefits• Significant lower cost versus instrumented system

• Can be applied to all risers for life of field monitoring

• Proactive and continuous approach

• Early anomaly detection to minimize RISKEX

• Eliminates conservatisms in design from assumptions and simplifications

• Data used to justify and enable life extension

• Data used to justify integrity decisions such as a costly remediation or no remediationLearn more at www.2hoffshore.com

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Cost Benefit Potential

• $20MM+ to avoid replacement cost of risers or mooring systems by demonstrating sufficient fatigue life for life extension target

• $50MM++ to rapidly detect and mitigate a structural issue that would have led to significant production downtime (i.e. > 6 months)

• Actual operating condition worse than design

• Excessive corrosion and interaction with structural strength and fatigue margins

• Gaps in maintenance during operating life


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Hang-off(Bend Stiffner)

Bottom ofSag Bend

Top of Hog Bend (Buoyancy)

Touch Down Zone

Input Data6 DOF 1st and

2nd Order Vessel Motion & GPS Position Data

Output DataTensile and

pressure armourlayer stresses and fatigue damage

Field Inputs and Desired outputs


9Twin (using ML) the conventional FEA process as it is too slow to use real time with measured field data

www.acteon.com9

BFLEX Model of Flexible Cross Section

BFLEX Hysteresis Loop

Global Response Calculation

BFLEX Flexible Layer Stress Calculation

API-17J Strength Check

Armor Fatigue Lives

2HBFlexRain SN Curve Application


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300

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1,000

800 900 1000 1100 1200 1300 1400

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nsil

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rmo

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a)

Time (seconds)

Tullow - Jubilee Flexible Riser Eng Support

TARGET Vs OUTPUT TIMETRACES COMPARISON

Tensile Armour Layer Stress, Fatigue Seastate 4 (High Damage,

Low Error), 10, 10 Delays, 10 Hidden Layers

Target Output

Error in Maxima=0.20%Error in Minima=3.89%Error in Mean=0.76%Error in StDev=0.02%

ML Algorithm Excellent Amplitude and Phasing PredictionLearn more at www.2hoffshore.com

12ML Algorithm within +/-10% Fatigue Damage Accuracy Across Majority of Seastates

0

0.5

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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

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dic

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ar)

FEA Fatigue Damage (1/Year)

Flexible Riser Fatigue Tracker

MACHINE LEARNING - STRESS TIME TRACEENARX/LM, Exponent 6 - 251 Seastates, Rainflow Counting

Over-Prediction

Under-Prediction


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Integrate Computer Vision with Structural Digital Twin

• Use past anomalies to train using ML so computer can detect:• Excessive marine growth• Missing strakes or fairings• Missing buoyancy modules• Coating damage or disbondment• Deep trenches at touch down• Spans on seabed

• Use trained anomaly algorithm with ROV/AUV inspection video

• Feed into digital twin to check strength and fatigue integrityLearn more at www.2hoffshore.com

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Summary

• Current approach to subsea integrity inspection and monitoring can benefit from digital technologies to improve efficiencies and unlock performance for extending life

• Machine learning digital twin algorithms can be used to better track dynamic structures in service using existing measured data (sensor data and inspection video footage)

• Structural digital twin demonstrated for risers – can be replicated for dynamic flowlines, subsea jumpers and moorings


Documents

TECHNICAL PAPER - Global Offshore Engineering & Riser Design