predicting corrosion rates and future corrosion severity from in-line inspection data

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Page 1: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

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Report Information from ProQuestJanuary 11 2012 14:57

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Page 2: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

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Predicting corrosion rates and future corrosion severity from in-line inspection dataDesjardins, Guy. Materials Performance 40. 8 (Aug 2001): 60.

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Recent advancements in the accuracy and resolution of in-line inspection tools have made it

possible to estimate corrosion rates and future corrosion severity on pipelineswith a

reasonable degree of confidence. This allows pipelineoperatord to identify specific areas

where corrosion is most active and predict what the probable future severity of that corrosion

will be.

Pipelinecorrosion is most prevalent when the failure of coatings, inhibitors, or catholic

protection occurs in a corrosive environment. It is important to realize that these factors do

not affect me pipelineequally at all locations, and corrosion does not grow at the same rate

throughout a pipeline. If an operator can identify which corrosion defects are active or

growing, they predictions of future corrosion severity for each and every defect on the

pipelinecan be made.

In-line inspection (ILI) technology has provided an effective means of determining the

corrosion rates on a pipeline. ILItechnology has made significant advances in identifying,

locating, and assessing pipelinedefects. Through the correlation and analyses of corrosion

anomalies from IU data sets, corrosion rates and predictions of corrosion severity can be

estimated within a measurable level of confidence. Dynamic models of a pipelinecan then be

developed based on the probable future state of corrosion anomalies.

Determining Corrosion Rates from ILIData

In theory, determining corrosion rates from multiple IU data sets should be relatively simple.

II-Is provide the location and size of corrosion defects, and corrosion rates can then be

calculated from the change in defect sizes between inspections. In practice, however, several

difficulties need to be Overcome.

The first problem arises when attempting to match defects accurately from one inspection to

the next. Oodometer slippage, orientation differences, changes in corrosion size and shape,

and different inspection tools with varying accuracy and sensitivity collectively make the

matching process quite complicated. On the other hand, computer technology and the

development of pattern-recognition software have made matching large numbers of defects a

manageable task. This software can identify and correlate corrosion patterns between ILI

data sets, accounting for differences in orientation and defect characteristics (Figure 1).

Page 3: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

Matching corrosion defects can now be primarily automated with manual checks to ensure

reliability and accuracy. This enables hundreds of thousands of corrosion defects to be

matched and analyzed accurately and efficiently.

The defect matches provide the growth history of individual corrosion anomalies, from which

the corrosion rates can be calculated in terms of depth, length, and width. Corrosion severity

for each defect and the probability of pipelinefailure caused by corrosion can also be

predicted for any given time.

A second problem that needs to be overcome when forecasting corrosion growth involves

accounting for the error associated with IU tools. If ILItools were perfectly accurate,

determining corrosion rates would be quite straightforward. Because they are not perfect,

however, a probabilistic approach to the problem is necessary. Corrosion rates and

predictions must be determined within some confidence bounds, which themselves need to

be determined.

Figure 2 compares an ILIrun with field-measurement results. Data points that lie off of the 1:1

line indicate measurement error in either the ILIor field tool. The accuracy of any calculation

based on ILIdata is limited by the error associated with the data. Accuracy can be stated as a

confidence interval, with the typical confidence bounds of high-resolution ILIdata being +/-

10% nominal wall thickness (NWT) 80% of the time. The level of confidence must be

accounted for in all of the corrosion predictions based on the measured data.

Page 4: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

Predicting Corrosion Severity and Probability of Failure

With multiple inspections, the corrosion rates for individual defects on a pipelineare

calculated from the observed changes in defect size from one ILIto the next. Based on these

calculated corrosion rates, future corrosion depths, lengths, and widths can be predicted. The

associated measurement error requires that the confidence bounds of the corrosion rate and

predictions be accurately calculated; this is done using a Bayesian method.

Figure 3 illustrates the probability functions for depth measurements of a corrosion defect

from two separate ILIruns, along with the resulting probability function of the predicted depth

and corrosion rate.

The probability distributions of depth and length allow one to determine the probability

distribution for pipelinefailure pressure. Resultant failure predictions (predictions that a

pipelinewill leak or rupture at some future date) can now be calculated, but they are also

affected by the uncertainty in a defect's depth measurement and predicted failure pressure.

Figure 4 shows that, from a statistical perspective, the probability of rupture is the area under

the failure pressure probability function that falls to the left of the operating pressure.

Because active corrosion increases defect depth and length and decreases failure pressure,

the failure pressure probability function will move left with time. Integrating the area under

each failure pressure probability function that falls to the left of the operating pressure yields

the increasing probability of eventual failure (Figure 5).

Page 5: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

Decision Modeling

Determining where corrosion is active on a pipelineand at what rate it is growing allows

operators to, in effect, perform virtual inspections for any given point in the future. Figure 6

outlines a typical decision model that can be developed using this virtual inspection concept.

The probability of failure curve for a pipelinecan be adjusted to reflect potential repairs on

that pipelineand the resultant reduction in failure probability. This range of probability failure

curves can then be used to compare the net present cost of any number of pipelinerepairs to

the net present cost of reinspecting the pipeline. It can be adjusted based on the operator-

determined maximum allowable probability of failure. In the example in Figure 6, the optimal

reinspection point is at 7 years-based on minimizing the total cost while maintaining the

minimum comfort level.

Conclusions

Assessing corrosion rates on a pipelinefrom IU data is both possible and viable. With

powerful pattern-matching software and the statistical methodology to assess accurately the

confidence bounds associated with corrosion data, pipelineoperators can develop a dynamic

model of their pipelinethat incorporates current and probable future states. Pipelineoperators

can then more effectively prioritize pipelinerepairs, optimize future inspection schedules, and

correlate active corrosion with environmental variables to better understand potential root

causes of pipelinecorrosion.

Page 6: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

References

Bibliography

References

Bhatia, A., T. Morrison, N.S. Mangat, "Estimation of Measurement Errors." In Proceedings of

the International PipelineConference Book no. G1075A, 1998. New York, NY: ASME

International, 1998.

Bhatia, A., T. Morrison, G. Desjardins, "Analysis of Corrosion Growth Using a High-

Resolution In-Line In

References

spection Tool." In Conference Proceedings of NACE Northern Area Eastern Conference,

held October 24-27, 1999. Ottawa, Ontario, Paper 38. 1.

Jaska, C.E., J.A. Beavers, B.A. Harle, "Effects of Stress Corrosion Cracking on Integrity and

Remaining Life of Natural Gas Pipelines." CORROSION/96, paper no. 255. Houston, TX:

NACE, 1996.

References

Morrison, T., R. Worthingham. "Reliability of High Pressure Line Pipe Under External

Corrosion," Offshore Mechanics and Arctic Engineering, vol. 5, Part B, Book no. H0746B.

New York, NY: ASME International, 1992.

Work in Progress by NACE Task Group T10E-6, "InLine Nondestructive Testing of Pipelines

." Houston, TX: NACE.

Page 7: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

References

Worthingham, R., T. Morrison, G. Desjardins, "Case History of Integrity Management on a

Corroded Pipeline." In Proceedings of NACE Northern Area Western Conference, held

March 8-11, 1999. Calgary, Alberta, Session 3A.

AuthorAffiliation

GuY DESJARDINS, Morrison Scientific, Inc.

AuthorAffiliation

GUY DESJARDINS is the President of Morrison Scientific, Inc., Suite 815,706- 7 Ave, SW,

Calgary, Alberta, UP 0Z1. With a geophysics degree and more than 23 years' experience in

the oil and gas industry, he has spent the past 8 years specializing in the analyses of pipeline

inspection data and corrosion measurement. He is a member of APEGGA and NACE and is

an active member of numerous NACE committees related to ILls and pipelinecorrosion.

_______________________________________________________________ Indexación (detalles)

Título Predicting corrosion rates and future corrosion severity from in-line

inspection data

Autor Desjardins, Guy

Título de publicación Materials Performance

Tomo 40

Número 8

Páginas 60

Número de páginas 4

Año de publicación 2001

Fecha de publicación Aug 2001

Año 2001

Editorial National Association of Corrosion Engineers

Lugar de publicación Houston

País de publicación United States

Materia de la revista Engineering--Engineering Mechanics And Materials, Metallurgy

ISSN 00941492

CODEN MTPFBI

Tipo de fuente Trade Journals

Idioma de la publicación English

Tipo de documento PERIODICAL

ID del documentos de

ProQuest

222972175

URL del documento http://search.proquest.com/docview/222972175?accountid=43790

Copyright Copyright National Association of Corrosion Engineers Aug 2001

�ltima actualización 2010-06-09

Page 8: Predicting Corrosion Rates and Future Corrosion Severity From in-line Inspection Data

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