Risers, Pipelines & Subsea Systems

Reducing Uncertainty & Gaining Confidence by Monitoring

Tze King Lim, Hugh Howells

AOG 2015, Perth

12th March 2015

Agenda

� Introduction – Fatigue Sources and Uncertainties

� Selecting Correct Instrumentation

� Getting Best Value from Measurements

� Screening

� Filtering

� Conversion to Useful Parameters

� Correlation with Environment

� Benefits

� Conclusions

Applicable to all subsea systems subjected to cyclic loads3 of 32

Wellhead and Conductor Fatigue

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Fatigue Failure

� West of Shetland Region, 440m depth

� Reference DOT paper 1983, C. Hopper, Britoil

� Risk increasing due to larger BOP stacks, longer well duration

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Fatigue Sources

� Riser motions from:

� Wave loads on riser

� Wave-induced vessel motions

� VIV (Vortex Induced Vibrations)

� Internal flow e.g. slugging

� VIV of pipeline and jumper spans

� Transportation and installation

Vessel motions

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Example Components

Infield and riser base spools, tree connectors: VIV, slugging

Pipeline spans: VIV, slugging

Jacket platforms and conductors: waves, VIV

Mid-water flow bundles: towing, installation, in-place

waves & VIV

Introduction – Causes of Uncertainties

� Variability and unknowns exist in:

� Metocean conditions – not all waves/currents in metocean reports will be seen during drilling

� Vessel motions – vessel heading

� Hydrodynamic properties – drag coefficients, damping

� Soil strength – range of strengths specified

� Well installation – stickup, cement shortfall, casing preload

� Internal fluids – density variations, flow regimes

� Fatigue details – S-N curves and SCFs

� Large uncertainties exist

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Case Study – Conductor Connector, North West Shelf

Analysis Parameter

Input Giving Low

Fatigue Damage

Input Giving High

Fatigue Damage

Factor ofChange in

Fatigue Damage

Soil strength Stiff soil Soft soil 2.2

Wave dataDifferent combination of

waves during drilling1.5

Vessel heading -10% surge +10% surge 2.2

Structural damping 0.3% 0% 2.5

Background current 0.2m/s 0m/s 51.6

Drag coefficient 1.2 1.0 2.9

Wellhead stickup 2.5m 3.5m 17.9

Cement level around conductor No shortfall 2m shortfall 1.59 of 32

Introduction – Approach to Fatigue Design

� Conservative parameters considered in analysis

� Large safety factors (e.g. 10)

� Design code objective to obtain target probability of failure (~10-5)

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Calculated Fatigue Damage vs Fatigue Resistance

A < B

FSF

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To gain the best value from monitoring, we need:

Robust Instrument Selection

Execute Monitoring Campaign

Accurate Data

Processing

Correlation with

Environment

Feedback into Future

Design

Monitoring Steps

*not in this presentation

Wellhead and

Conductor Design

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Instrument Selection

� Understand expected behaviour based on analysis predictions: what are range of motions?

� What parameters to monitor – acceleration, angular rates, strain?

� What accuracy is required?

� What uncertainties will be introduced from monitoring system: calibration error, resolution, noise

� Testing to verify calibration and noise

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Instrument Selection Case Study -Accelerometers

More precision required for worse fatigue detail

Sensor noise level

with filtering

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Instrument Selection Case Study –Angular Rate Sensors

Sensor noise level

with filtering

Angular rate sensors

selected in this example

– better signal to

noise ratio

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Data Processing Steps

Data Management

ScreeningData

Correction

Inspect Frequency Spectra

FilteringConversion to Useful

Parameters

Robust Instrument Selection

Execute Monitoring Campaign

Accurate Data

Processing

Correlation with

Environment

Feedback into Future

Design

Wellhead and

Conductor Design

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Data Management Challenges

� Large volumes of data are collected:

� 1 motion measurement device, 3 accelerometer, 2 angular rate, 1 temperature for 1 year = 2.4 Gb

� How and where to store?

� Providing reliable access to data and results

� Handover responsibility with change in personnel

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Screening

� High level review of data

� Checks that instrument is working as expected

� Data collected is in line with expectations

� Identify events with significant motions to be investigated further

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Screening Case Study

Events to investigate

further

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Data Correction

� Gravity correction:

� Component of gravity is measured by accelerometers when inclined

� Results in over or under-prediction of fatigue depending on deflected shape

� Unexpected Responses – remove measurements of installation /retrieval, drilling vibration, impacts

� Clock Drift – needed if data from multiple devices are combined

� Temperature Drift – calibration changes with temperature

L o g g e r o n U n d e fo rm e d R is e r

θ

g a

A c c e le ra t io n s a t P e a k R is e r D e fo rm a t io n

θ

g c o s θ

M e a s u re d A c c e le ra t io n s a t P e a k R is e r D e fo rm a t io n

g -C o n ta m in a te d A c c e le ra t io n

a + g s in θ θ

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Inspect Frequency Spectra

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Angula

r ra

te (

deg/s

)

Filtering

High pass –remove drift

Low pass: remove noise

� Noise affects magnitude of measurements and introduce errors

� Integration amplifies error at low frequencies

� Uncertainty in fatigue life is ^3 or ^4 uncertainty in stress

� Noise can be minimised by correct filtering

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Filtering Case Study – 10Hz Sampling Rate

� 10Hz sampling rateFiltering Method

Signal to Noise Ratio

Measured Parameter

Integrated Parameter

Double Integrated Parameter

No filtering 3.60 1.25 0.01

Averaging over 1s (slow varying parameters)

11.38 - -

Low pass (f>1Hz removed) 8.00 1.25 0.01

High pass (f1Hz and

Conversion to Useful Parameters

Measured parameters: accelerations, angular rates, curvature

Stress range & number of cycles

Accumulated fatigue damage & remaining fatigue life

Is it safe to continue operations?

Is the component performance up to spec?

When is it recommended to inspect?

Can unplanned workover be performed?

Can service life be extended?

Transfer functions, fatigue details

Calibration

Feed into operations

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Example Conversion to Accumulated Fatigue

25 of 32Most fatigue accumulated during few events with large waves

Correlation with Environment

� Compare wave and VIV motion measurements with environmental conditions

� Compare slugging motion measurements with flow conditions

� Allows calibration of analysis models and reduces conservatisms

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Assessing Conservatism

Probability of fatigue failure revised

Bias in measurement mean vs design

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Effects of Reducing Uncertainty

Probability of fatigue failure reduced

Variability reduced

28 of 32Reliability analysis can be used to justify less conservative design

Example Calibration of VIV Parameters

� Calculated VIV fatigue is conservative compared to calculated VIV

� Adjusted input parameters which are less conservative can be justified

� Ref: M. Tognarelli, S. Taggart (BP), M. Campbell (2H) – “Actual VIV Fatigue Response of Full Scale Drilling Risers: With and Without Suppression Devices”, OMAE 2008 29 of 32

Feedback into Present and Future Design

� Final step is to implement the findings from monitoring:

� Refined fatigue lives for present wells

� Optimised wellhead and conductor for future wells

� Justified reduction in safety factors

� Use calibrated analysis models for future wells

� Enables cost savings

Conclusions

� To obtain best value from monitoring, the following is required:

� Robust instrument selection

� Accurate data processing methods

� Correlation with metocean/internal fluid conditions

� Feedback into ongoing inspections and future design

� Benefits:

� Justify less demanding safety factors

� Reduce over-design

� Reduce costs

� Calibrated models – better predictions for future design

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Questions?

Further information:

2H Offshore Engineering

www.2hoffshore.com

+61 8 9222 5000