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).
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.
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).
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.
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.
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)
TÃtulo Predicting corrosion rates and future corrosion severity from in-line
inspection data
Autor Desjardins, Guy
TÃtulo 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
PaÃs 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
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