Predictive Model for Pitting Corrosion in Buried Oil and Gas Pipelines

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

doi:10.5006/1.3319138

Cited by 167 publications

(89 citation statements)

References 5 publications

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“…For a given L/D and L/√Dt (Figures 9 and 10 It is evident from the information in Figure 8 that the burst pressure retention ratio (R r ) of the pipes reduced with the increase in d/t, which is similar to the findings of other researchers [5][6][7][8][9]. This scenario was necessitated by the increasing corrosion defect depth, which generally results in increased stress concentration on pipelines [18][19][20]. For a given L/D and L/ √ Dt (Figures 9 and 10), the retention ratios were fairly steady at each d/t as the L/D and L/ √ Dt values increased.…”

Section: Variability Of the Retained Strength Of The Corroded Pipelinessupporting

confidence: 79%

“…It is evident from the information in Figure 8 that the burst pressure retention ratio (Rr) of the pipes reduced with the increase in d/t, which is similar to the findings of other researchers [5][6][7][8][9]. This scenario was necessitated by the increasing corrosion defect depth, which generally results in increased stress concentration on pipelines [18][19][20]. For a given L/D and L/√Dt (Figures 9 and 10 It is evident from the information in Figure 8 that the burst pressure retention ratio (R r ) of the pipes reduced with the increase in d/t, which is similar to the findings of other researchers [5][6][7][8][9].…”

Section: Variability Of the Retained Strength Of The Corroded Pipelinessupporting

confidence: 76%

See 1 more Smart Citation

Finite Element Modelling and Retained Life Estimation of Corroded Pipelines in Consideration of Burst Pressures—A Fractural Mechanics Approach

Ossai

2017

Infrastructures

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This study used Finite Element Modelling (FEM) to determine the relationship between the burst pressure (P b ) of internally, circumferentially corroded pipelines, with the corrosion defect depth (d), pipe wall thickness (t) and the pipe diameter (D). After modelling X46 and X52 grades of pipes, the P b estimated was compared with those determined experimentally and with industry standard models-ASME B31G (modified), RSTRENG, DNV F101, SHELL92 and FITNET FFS. The comparison specified a Root Mean Square Percentage Error (RMSPE) that ranged from 7.06% to 20.4% and a coefficient of determination (R 2 ) that varied from 0.7932 to 0.9813. Multivariate regression was also used to compute a general linear relationship between the burst pressure (P b ) and (d/t), (L/D) and (L/ √ Dt). The resulting FEM burst-pressure model, developed with multivariate regression, was later used to estimate the expected allowable operating pressure of a corroded X46 grade pipeline over the lifecycle duration, for low, mild, high and severe corrosion categories. It was observed that the burst pressure retention ratio (R r ), which is an indicator of the reliability of the pipeline, decreased with the increase in (d) but did not show distinctive changes with the increase in (L). Considering the robustness of the FEM developed in this study, it can be concluded that it will be very vital for flowline design and pipeline integrity management.

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Section: Variability Of the Retained Strength Of The Corroded Pipelinessupporting

confidence: 79%

Section: Variability Of the Retained Strength Of The Corroded Pipelinessupporting

confidence: 76%

Finite Element Modelling and Retained Life Estimation of Corroded Pipelines in Consideration of Burst Pressures—A Fractural Mechanics Approach

Ossai

2017

Infrastructures

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“…There has not been another investigation as exhaustive as Romanoff's that can replace the data obtained in his study. He did investigate a second small set of samples to complete the previous cases [116]. These experiments were much shorter than those conducted by NIST, generally limited to a maximum of three years.…”

mentioning

confidence: 99%

Methods to Evaluate Corrosion in Buried Steel Structures: A Review

Arriba-Rodriguez

Balsera

Fernández

et al. 2018

Metals

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Around the world, there are thousands of metal structures completely or partially buried in the soil. The main concern in their design is corrosion. Corrosion is a mechanism that degrades materials and causes structural failures in infrastructures, which can lead to severe effects on the environment and have direct impact on the population health. In addition, corrosion is extremely complex in the underground environment due to the variability of the local conditions. The problem is that there are many methods to its evaluation but none have been clearly established. In order to ensure the useful life of such structures, engineers usually consider an excess thickness that increases the economic cost of manufacturing and does not satisfy the principles of efficiency in the use of resources. In this paper, an extended revision of the existing methods to evaluate corrosion is carried out to optimize the design of buried steel structures according to their service life. Thus, they are classified into two categories depending on the information they provide: qualitative and quantitative methods. As a result, it is concluded that the most exhaustive methodologies for estimating soil corrosion are quantitative methods fed by non-electrochemical data based on experimental studies that measure the mass loss of structures.

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“…The time of pitting initiation on the pipeline depends on the physical and chemical characteristics of the environment exposed to the pipeline [10,30] and the corrosion resistance ability of the pipeline material. The rate of change of deterministic pit depth (∆ ( ) ) can be expressed in terms of the deterministic intensity (λd) and change in time interval as follows:…”

Section: Figure 2: Graphical Representation Of Chapman-kolmogorov Equmentioning

confidence: 99%

“…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. Pitting corrosion rate has also been modelled by researchers using damage function analysis by considering pit nucleation, growth rate and repassivation of carbon steel in chloride solution [22].The pitting rate for underground pipelines was also predicted with lognormal linear model in consideration of environmental variables [29] and the time of initiation of pitting has also been predicted for different soil categories using Monte Carlo simulation of field observed soil conditions [10].…”

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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Predictive Model for Pitting Corrosion in Buried Oil and Gas Pipelines

Cited by 167 publications

References 5 publications

Finite Element Modelling and Retained Life Estimation of Corroded Pipelines in Consideration of Burst Pressures—A Fractural Mechanics Approach

Finite Element Modelling and Retained Life Estimation of Corroded Pipelines in Consideration of Burst Pressures—A Fractural Mechanics Approach

Methods to Evaluate Corrosion in Buried Steel Structures: A Review

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

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