Probability distribution of pitting corrosion depth and rate in underground pipelines: A Monte Carlo study

Caleyo, F.; Velázquez, J. C.; Valor, A.; Hallen, J.M.

doi:10.1016/j.corsci.2009.05.019

Cited by 208 publications

(88 citation statements)

References 14 publications

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“…They concluded that Markov chain predictive model was best for describing the corrosion distributions [23]. Caleyo et al [19] also used Monte Carlo simulation to model pit depth growth of underground pipelines in different soil conditions and fitted the three maximal extreme value distributions -Weibull, Fretchet and Gumbel to the resulting best fit models of the studied soils, however, Fretchet distribution was best for describing the best fit model over a long-time exposure as was already stated in this work. Again, another work on experimental determination of internal pitting rate in pipelines concluded that increases in pitting rate occurs due to increased chloride concentration, temperature, subcutaneous substances (such as sand) and flow rate whereas decrease in pitting rate was observed with increase in bicarbonate, CO2 and H2S partial pressures and operating pressure [4], however, the results in this research were validated with limited field data.…”

Section: Introductionmentioning

confidence: 99%

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Ossai

Boswell

Davies

2016

Engineering Failure Analysis

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A continuous time non-homogenous linear growth pure birth Markov model was used to predict the future pit depth distribution of internally corroded oil and gas pipelines. A negative binomial distribution was used for calculating the transition probability functions of the pit depths whilst pit depths growth was estimated for low, moderate, high and severe pitting corrosion rates using field measured data of pit depths, temperatures, CO2 partial pressures, pH and flow rates. The Markov predicted results agreed well with field measured pit depth data from X52 grade pipeline and L-80 and N-80 grades offshore well tubing.

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Section: Introductionmentioning

confidence: 99%

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Ossai

Boswell

Davies

2016

Engineering Failure Analysis

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“…Restrepo et al (2009) applied the proportionate stratified sampling method to establish "index of aggressiveness" (IA) of the soil, with contributors including soil moisture, pH, redox potential, pipe-soil potential , soil resistivity, and sulphide content. Caleyo et al (2009) investigated the distributions of several soil properties along a 50-year old oil steel pipeline. They then proposed a Rossum (1969)-type multivariate power model to predict maximum pits depths as a function of soil properties and time of exposure and used the probability distributions of the soil properties to carry on a Monte-Carlo analysis to discern the distribution of corrosion pit growth.…”

Section: Introductionmentioning

confidence: 99%

Performance of Ductile Iron Pipes. I: Characterization of External Corrosion Patterns

Kleiner

Rajani

Krys

2013

J. Infrastruct. Syst.

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/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1061/(ASCE) IS.1943-555X.0000097 Journal of Infrastructure Systems, 19, 1, pp. 108-119, 2012-02-06 Performance of ductile iron pipes. I : characterization of external corrosion patterns Kleiner, Yehuda; Rajani, Balvant; Krys, Dennis

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“…Pitting corrosion is an extremely dangerous form of localized corrosion since a perforation resulting from a single pit can cause complete in-service failure of installations like water pipes, heat exchanger tubes or oil tank used for example in chemical plants or nuclear power stations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The pits depth distribution is an important characteristic of the extent of such damage; the deeper the pits, the more dramatic the damage.…”

Section: Introductionmentioning

confidence: 99%

A generic statistical methodology to predict the maximum pit depth of a localized corrosion process

et al. 2011

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This paper outlines a new methodology to predict accurately the maximum pit depth related to a localized corrosion process. It combines two statistical methods: the Generalized Lambda Distribution (GLD), to determine a model of distribution fitting with the experimental frequency distribution of depths, and the Computer Based Bootstrap Method (CBBM), to generate simulated distributions equivalent to the experimental one. In comparison with conventionally established statistical methods that are restricted to the use of inferred distributions constrained by specific mathematical assumptions, the major advantage of the methodology presented in this paper is that both the GLD and the CBBM enable a statistical treatment of the experimental data without making any preconceived choice neither on the unknown theoretical parent underlying distribution of pit depth which characterizes the global corrosion phenomenon nor on the unknown associated theoretical extreme value distribution which characterizes the deepest pits.Considering an experimental distribution of depths of pits produced on an aluminium sample, estimations of maximum pit depth using a GLD model are compared to similar estimations based on usual Gumbel and Generalized Extreme Value (GEV) methods proposed in the corrosion engineering literature. The GLD approach is shown having smaller bias and dispersion in the estimation of the maximum pit depth than the Gumbel approach both for its realization and mean. This leads to comparing the GLD approach to the GEV one. The former is shown to be relevant and its advantages are discussed compared to previous methods.

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Probability distribution of pitting corrosion depth and rate in underground pipelines: A Monte Carlo study

Cited by 208 publications

References 14 publications

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Markov chain modelling for time evolution of internal pitting corrosion distribution of oil and gas pipelines

Performance of Ductile Iron Pipes. I: Characterization of External Corrosion Patterns

A generic statistical methodology to predict the maximum pit depth of a localized corrosion process

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