A3M Mexico 8-13 for Publication

8/9/2019 A3M Mexico 8-13 for Publication

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Permanently Installed Guided Wave

Pipeline Monitoring

A. Galvagni and A. Demma

Implementacion de sistemas de monitoreo

adentro de los planes de integridad

Mexico 22/8/2013

Alessandro Demma


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A3Monitoring

Monitoring

ReducedRisk

Reducedcost

Improved

performance

Codecompliance

Asseteasily

managed

Companyimage


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Implementation of monitoring

Integritystudy

Identify threatand target area

Feasibilitymonitoring

Select sensors+ parameters

Statisticalanalysis

DataFusion


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Target areas ID by RBI or similar methodology


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Define critical defect size


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Feasibility

Which type of sensor could detect damage?UT

GW long range

GW short range

AE

Fibre optics

Etc..

How many sensors?

How frequent data gathering?


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Both bare and buried pipes

3 inches and above

Temp from -10C to 120C

Mobile comms

.

UT monitoring on bare and buried pipes

Buried

Buried


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UT monitoring high temp


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Long Range Guided Wave Monitoring

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the GW sensor transmits a

torsional wave packetalong the pipe

Corrosion

Patch

damage, such as corrosion patches, cracks, etc.,

reflect a portion of the transmitted wave packet

back to the GW sensor in proportion to its cross-section

Weld

Cap

other pipeline features, such as flanges,

weld caps, supports, etc., also reflect a portion ofthe transmitted wave packet back to the GW sensor


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Long range GW Monitoring


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Short Range Guided Wave


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Inspection vs Monitoring

Inspection concentrates on coveringmany areas

Monitoring concentrates on inspection of

critical or high risk/consequence areas

Monitoring can provide better sensitivity,

reliability and efficiency if appropriately

used


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GWhow to use multiple readings

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call level is 6dB above noise, but at features call level is undefined

1.0%

Sensitivity


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Multiple Readings

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several readings are collected from each GW sensor


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Maximize monitoring benefit

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Data shown here after compensation. When do you say that a change is relevant?

For example the point at 600 days is acceptable or not? We deal with this previously

unresolved challenge. Algorithm works also at supports, welds and bends. Here weld example

?


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Statistical Analysis

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future samples outside change

thresholds is inconsistent with

baselines, i.e. CHANGE

future samples inside no changethresholds is consistent with

baselines, i.e. NO CHANGE

other future samples cannot be

classified, i.e.

MORE SAMPLES NEEDED

We can say if a change is relevant or not with assigned confidence value


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Sequential Analysis

17Guided Wave Pipeline Monitoring

What influences change / no change thresholds?

1. Number & quality of baseline samples. Environmental compensation reduce baseline point variance, tightens thresholds.

2. False call & detection probabilities. Lower false call probabilities widen thresholds.

Higher detection probabilities tighten thresholds.

3. Number of current samples available.

More samples represent more evidence and tighten thresholds.

new current

samples

o

2% detection

target


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Sequential Analysis








new current

samples

o o

2% detection

target


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Sequential Analysis








new current

samples

o o o

2% detection

target


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20 Current Samplesnow possible to detect target!

Sequential Analysis






2% detection

target


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A3Monitoring Software analysis

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Here there was a defect 0.75%

cross section loss

shieldCube automatically highlights zones of concern along the pipeline and reports the

estimated cross-sectional area loss rate, subject to:The desired confidence level.The

minimum cross-sectional area loss rate that is acceptable.

The algorithms of shieldCube guided wave monitoring have been extensively field

tested.It has been proven during field trials that:For a given number of readings,shieldCube maximises the probability of detecting damage and corrosion.shieldCube

can predict in advance the minimum loss rate that can be detected within a given period

of time and with a given number of readings.


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A3Monitoring Software analysis online

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shieldCube Thickness Monitoring


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shieldCube Data Fusion

The data fusion concept stems from the recognition that

corrosion monitoring must rely on many different tools.

As in medical science, no single test can diagnose all

possible

Tools include:

Non-Intrusive Sensors

UT, LRGW, SRGW, etc.

Intrusive Sensors

ER, LPR, Coupons, ILI, etc.

Corrosion Models


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Data Fusion is achieved by combining the effectdifferent measurements have on the PDFs at eachnode.

Data Fusion

imagine associatingprobability density functionsto each node


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The result is a heat map of the most likely corrosionrate, remaining wall thickness and pit depth at allpositions along the pipe, leveraging on all theinformation from sensors and models.

Data Fusion


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How do you manage visually the monitoringinformation?

Documents

A3M Mexico 8-13 for Publication