A hierarchical Bayesian growth model is presented in this paper to characterize and predict the growth of individual metal-loss corrosion defects on pipelines. The depth of the corrosion defects is assumed to be a power-law function of time characterized by two power-law coefficients and the corrosion initiation time, and the probabilistic characteristics of the these parameters are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on in-line inspection (ILI) data collected at different times for a given pipeline. The model accounts for the constant and non-constant biases and random scattering errors of the ILI data, as well as the potential correlation between the random scattering errors associated with different ILI tools. The model is validated by comparing the predicted depths with the field-measured depths of two sets of external corrosion defects identified on two real natural gas pipelines. The results suggest that the growth model is able to predict the growth of active corrosion defects with a reasonable degree of accuracy. The developed model can facilitate the pipeline corrosion management program.

References

References
1.
Kiefner
,
J. F.
,
Mesloh
,
R. E.
, and
Kiefner
,
B. A.
,
2000
, “
Analysis of DOT Reportable Incidents for Gas Transmission and Gathering System Pipelines, 1985–1997
,” Report to the Pipeline Research Council International, Inc. (PRCI), Catalog No. L51830e.
2.
Kariyawasam
,
S.
, and
Peterson
,
W.
,
2010
, “
Effective Improvements to Reliability Based Corrosion Management
,”
Proceedings of the 8th International Pipeline Conference
, Volume
4
, Paper No. IPC2010-31425, ASME, Calgary, Alberta, Canada, September 27–October 1, 2010, pp.
603
615
.10.1115/IPC2010-31425
3.
Worthingham
,
R.
,
Morrison
,
T.
, and
Desjardins
,
G.
,
2000
, “
Comparison of Estimates from a Growth Model 5 Years After the Previous Inspection
,”
Proceedings of the International Pipeline Conference
, ASME, Calgary, Alberta, Canada.
4.
Desjardins
,
G.
,
2001
, “
Corrosion Rate and Severity Results from In-Line Inspection Data
,” CORROSION 2001, NACE International, Houston, TX, Paper No. 01624.
5.
Achterbosch
,
G. G. J.
, and
Grzelak
,
L. A.
,
2006
, “
Determination of the Corrosion Rate of a MIC Influenced Pipeline Using Four Consecutive Pig Runs
,”
Proceedings of the International Pipeline Conference
, Volume
2
, Paper No. IPC2006-10142, ASME, Alberta, Canada, September 25–29, 2006, pp.
209
217
.10.1115/IPC2006-10142
6.
Nessim
,
M.
,
Dawson
,
J.
,
Mora
,
R.
, and
Hassanein
,
S.
,
2008
, “
Obtaining Corrosion Growth Rates From Repeat In-Line Inspection Runs and Dealing With the Measurement Uncertainties
,”
Proceedings of the 7th International Pipeline Conference
, Volume
2
, Paper No. IPC2008-64378, ASME, Calgary, Alberta, Canada, September 29–October 3, 2008, pp.
593
600
.10.1115/IPC2008-64378
7.
Soares
,
C. G.
, and
Garbatov
,
Y.
,
1999
, “
Reliability of Maintained, Corrosion Protected Plates Subjected to Non-linear Corrosion and Compressive Loads
,”
Mar. Struct.
,
12
(
6
), pp.
425
445
.10.1016/S0951-8339(99)00028-3
8.
Romanoff
,
M.
,
1989
,
Underground Corrosion
,
NACE
,
Houston, TX, USA
.
9.
Caleyo
,
F.
,
Velázquez
,
J. C.
,
Valor
,
A.
, and
Hallen
,
J. M.
,
2009
, “
Probability Distribution of Pitting Corrosion Depth and Rate in Underground Pipelines: A Monte Carlo Study
,”
Corros. Sci.
,
51
(
9
), pp.
1925
1934
.10.1016/j.corsci.2009.05.019
10.
Maes
,
M. A.
,
Faber
,
M. H.
, and
Dann
,
M. R.
,
2009
, “
Hierarchical Modeling of Pipeline Defect Growth Subject to ILI Uncertainty
,”
Proceedings of the 28th International Conference on Ocean, Offshore and Arctic Engineering
, Paper No. OMAE2009-79470, ASME, Honolulu, HI, May 31–June 5, 2009, pp.
375
384
.10.1115/OMAE2009-79470
11.
Banerjee
,
S.
,
Carlin
,
B. P.
, and
Gelfand
,
A. E.
,
2004
,
Hierarchical Modeling and Analysis for Spatial Data
,
Chapman & Hall/CRC
,
New York
.
12.
Gelman
,
A.
,
Carlin
,
J. B.
,
Stern
,
H. S.
, and
Rubin
,
D. B.
,
2004
,
Bayesian Data Analysis
,
Chapman & Hall/CRC
,
Boca Raton, FL
.
13.
Robert
,
C. P.
,
2007
,
The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation
,
Springer
,
New York
.
14.
Demichelis
,
F.
,
Magni
,
P.
,
Piergiorgi
,
P.
,
Rubin
,
M.
, and
Bellazzi
,
R.
,
2006
, “
A Hierarchical Naive Bayes Model for Handling Sample Heterogeneity in Classification Problems: An Application to Tissue Microarrays
,”
BMC Bioinf.
,
7
(
1
), p.
514
.10.1186/1471-2105-7-514
15.
Bayes
,
M.
, and
Price
,
M.
,
1763
, “
An Essay Towards Solving a Problem in the Doctrine of Chances (By the Late Review Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S)
,”
Philos. Trans.
,
53
, pp.
370
418
.10.1098/rstl.1763.0053
16.
Ntzoufras
,
I.
,
2011
,
Bayesian Modeling Using WinBUGS
,
John Wiley & Sons
,
Hoboken, NJ
.
17.
Congdon
,
P. D.
,
2010
,
Applied Bayesian Hierarchical Methods
,
CRC Press
,
Boca Raton, FL
.
18.
Fenyvesi
,
L.
, and
Dumalski
,
S.
,
2005
, “
Determining Corrosion Growth Accurately and Reliably
,” Corrosion2005, NACE International, Houston, TX, Paper No. 05154.
19.
Caleyo
,
F.
,
Alfonso
,
L.
,
Espina-Hernández
,
J. H.
, and
Hallen
,
J. M.
,
2007
, “
Criteria for Performance Assessment and Calibration of In-Line Inspections of Oil and Gas Pipelines
,”
Meas. Sci. Technol.
,
18
(
7
), pp.
1787–1799
.10.1088/0957-0233/18/7/001
20.
Huyse
,
L.
, and
van Roodselaar
,
A.
,
2010
, “
Effects of Inline Inspection Sizing Uncertainties on the Accuracy of the Largest Features and Corrosion Rate Statistics
,”
Proceedings of the 8th International Pipeline Conference
, Volume
4
, IPC2010-31037, ASME, Calgary, Alberta, Canada, September 27–October 1, 2010, pp.
403
413
.10.1115/IPC2010-31037
21.
Coleman
,
G. A.
, and
Miller
,
S. J.
,
2010
, “
ILI Tool Tolerance and Repeatability Effect on Corrosion Growth Rates
,”
Proceedings of the 8th International Pipeline Conference
, Volume
4
, Paper No. IPC2010-31381, ASME, Calgary, Alberta, Canada, September 27–October 1, 2010, pp.
549
556
.10.1115/IPC2010-31381
22.
Spencer
,
K.
,
Kariyawasam
,
S.
,
Tetreault
,
C.
, and
Wharf
,
J.
,
2010
, “
A Practical Application to Calculating Corrosion Growth Rates by Comparing Successive ILI Runs From Different ILI Vendors
,”
Proceedings of the 8th International Pipeline Conference
, Voume
4
, Paper No. IPC2010-31306, ASME, Calgary, Alberta, Canada, September 27–October 1, 2010, pp.
467
473
.10.1115/IPC2010-31306
23.
Al-Amin
,
M.
,
Zhou
,
W.
,
Zhang
,
S.
,
Kariyawasam
,
S.
, and
Wang
,
H.
,
2012
, “
Bayesian Model for Calibrating the ILI tools
,”
Proceedings of the 9th International Pipeline Conference
, Volume
4
, Paper No. IPC2012-90491, ASME, Calgary, Alberta, Canada, September 24–28, 2012, pp.
201–208
.10.1115/IPC2012-90491
24.
Jaech
,
J. L.
,
1985
,
Statistical Analysis of Measurement Errors
,
John Wiley & Sons, Inc.
,
New York
.
25.
Fuller
,
W. A.
,
1987
,
Measurement Error Models
,
John Wiley & Sons, Inc.
,
New York
.
26.
Bernardo
,
J.
, and
Smith
,
A. F. M.
,
2007
,
Bayesian Theory
,
John Wiley & Sons Inc.
,
New York
.
27.
Carlin
,
B. P.
, and
Louis
,
T. A.
,
2000
,
Bayes and Empirical Bayes Methods for Data Analysis
,
Chapman & Hall/CRC
,
London, UK
.
28.
Lunn
,
D.
,
Spiegelhalter
,
D.
,
Thomas
,
A.
, and
Best
,
N.
,
2009
, “
The BUGS Project: Evolution, Critique and Future Directions
,”
Stat. Med.
,
28
(
25
), pp.
3049
3067
.10.1002/sim.3680
29.
Spiegelhalter
,
D. J.
,
1998
, “
Bayesian Graphical Modelling: A Case-Study in Monitoring Health Outcomes
,”
J. R. Stat. Soc.: Ser. C
,
47
(
1
), pp.
115
133
.10.1111/1467-9876.00101
30.
McNealy
,
R.
,
McCann
,
R.
,
Van Hook
,
M.
,
Stiff
,
A.
, and
Kania
,
R.
,
2010
, “
In-Line Inspection Performance III: Effect of In-Ditch Errors in Determining ILI Performance
,”
Proceedings of the 8th International Pipeline Conference
, Volume
4
, Paper No. IPC2010-31269, ASME, Calgary, Alberta, Canada, September 27–October 1, 2010, pp.
469
473
.10.1115/IPC2010-31269
31.
Pipeline Operators Forum (POF),
2009
, “
Specification and Requirements for Intelligent Pig Inspection of Pipelines
."
32.
Maes
,
M. A.
,
Dann
,
M. R.
,
Breitung
,
K. W.
, and
Brehm
,
E.
,
2008
, “
Hierarchical Modeling of Stochastic Deterioration
,”
Proceedings of the 6th International Probabilistic Workshop
, C. A. Graubner, H. Schmidt, D. Proske, T. U. Darmstadt, eds., pp.
111
124
.
You do not currently have access to this content.