The approach proposed by Najjar and coworkers for the prediction of maximum pit depth is applied and validated through direct comparison with real pipeline steel pitting corrosion data. This methodology combines the Generalized Lambda Distribution (GLD) and the Bootstrap Method (BM) in order to estimate both the maximum pit depth and confidence intervals associated with the estimation. Samples are drawn from real-life pitting corrosion data and the GLD is used to obtain modeled pit depth distributions emulating the experimental ones. In order to estimate the maximum pit depth over an N -times larger area, simulated distributions, N -times larger than the experimental ones, are generated 10 4 times. The deepest pit depth is extracted from each simulated bootstrap sample to obtain a dataset of 10 4 extreme pit-depth values. An estimate of the maximum pit depth for the N -times larger surface can be obtained from this dataset by calculating the average of the 10 4 extreme values. The uncertainty in the estimation is derived from the 95% confidence interval of the bootstrap estimate. In this report, the results of the application of the GLD-BM framework are compared with extreme pit depth values observed in real pitting corrosion data. The agreement between the estimated and actual maximum pit depths points to the applicability of the GLD-BM as an alternative in estimating the maximum pit depth when only a small number of samples are available. The main advantage of the combined methodology over the Gumbel method is its great simplicity, since fast and reliable estimations can be made with at least only two experimental samples.

A continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov’s forward equations for this type of Markov process gives the transition probability function in a discrete space of pit depths. The transition probability function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. Previously reported Monte Carlo simulations have been used for the prediction of the evolution of the pit depth distribution mean value with time for different soil types. The simulated pit depth distributions are used to develop a stochastic model based on Markov chains to predict the progression of pitting corrosion depth and rate distributions from the observed soil properties and pipeline coating characteristics. The proposed model can also be applied to pitting corrosion data from repeated in-line pipeline inspections. Real-life case studies presented in this work show how pipeline inspection and maintenance planning can be improved through the use of the proposed Markovian model for pitting corrosion.

Arc blow is a phenomenon associated with the deviation of the arc that usually occurs during arc welding of pipelines under repair. This phenomenon is most commonly observed after the pipeline has been inspected using an in-line magnetic flux leakage (MFL) inspection tool, which magnetized the pipe wall during the inspection run. Among the different welding techniques, DC arc welding is one of the most popular in the oil and gas industry due to its versatility and relative low cost. The interaction between the magnetic field associated with the current flowing through the electrode, and the residual magnetic field in the pipeline under repair can produce arc blow. In this work, a simple method to reduce arc blow during DC arc welding of pipelines has been developed. In contrast to the methods so far available in the literature [1,2], the method proposed here gives simple rules to be followed by welders with little background on magnetism. Residual magnetic field levels from different pipelines in southern Mexico were measured in the gap after damaged pipeline sections had been cut, and in the V groove once the new pipeline sections had been inserted. Magnetic finite element simulations were performed with freeware (FEMM) for the residual magnetic field compensation using real-life pipeline dimensions and field parameters. A large number of simulations were performed, using as variables the residual magnetic field in the groove, the number of coil turns required for the residual magnetic field compensation, the DC current flowing through the coil and the electrode, the position of the coil with respect to the groove, and the pipeline wall thickness and diameter. An empirical predictive equation was developed for the compensation of the residual magnetic field from the results obtained during the simulations. Most of the procedures developed in the past propose to adjust the current in order to compensate for the magnetic field in the groove, which is a disadvantage during DC arc welding since the electrodes specifications do not cover a wide range of current values. In contrast, the method proposed herein supersedes this disadvantage by granting the possibility of properly selecting the number of coil turns and the position of the coil with respect to the groove, in order to compensate for the residual magnetic field in the groove.

These days, in-line inspections based on the magnetic flux leakage (MFL) principle are routinely used to detect and size metal loss and mechanical anomalies in operating oil and gas pipelines. One of the characteristics of the MFL technology is that after the inspection, the pipeline wall shows a remanent magnetization. In this work, the influence of the magnetic field on pitting corrosion in pipeline steel is studied. Pitting corrosion experiments have been carried out on samples of an API 5L grade 52 steel under a magnetization level of the same order of magnitude of the remanent magnetization in the pipeline wall after the MFL inspection. The samples were magnetized using rings of the investigated steel. The closed magnetic circuit configuration used in this study survey guaranteed that the samples kept the same magnetization level during the complete duration of the conducted experiments. This experimental setup was used in order to reproduce the conditions observed in MFL-inspected pipelines in which the magnetic field was confined to the pipe wall thickness. Immediately after magnetization, the investigated samples were subjected to pitting by immersing them in a solution with dissolved Cl − and SO 4 2− ions. The pitting experiments were conducted for exposure times of 7 days. Non-magnetized specimens were used as control samples. The depths of the pits induced in the investigated samples were measured using optical microscopy. The maximum pit depth of each sample was recorded and used to conduct extreme value analysis of the pitting process in the magnetized and non-magnetized specimens. The results of this investigation indicate that the magnetic field confined within the pipeline wall has a significant influence on the pitting corrosion process. The statistical assessment of the pitting corrosion data collected during this study shows that the magnetic field reduces the average depth of the pit population. It also reduces the extreme pit depth values that can be predicted from the maximum values observed in the magnetized samples, with respect to the non-magnetized control samples. Scanning electron microscopy observations show that the magnetic field alters the pit morphology by increasing the pit opening (mouth). It is shown that the observed reduction in the pit depth when a magnetic field is confined to the volume of the corroding material can be explained based on the behavior of the paramagnetic corrosion products under the influence of the local magnetic field gradients produced inside and within the immediate vicinity of stable pits.

There exists a large number of works aimed at the application of Extreme Value Statistics to corrosion. However, there is a lack of studies devoted to the applicability of the Gumbel method to the prediction of maximum pitting-corrosion depth. This is especially true for works considering the typical pit densities and spatial patterns in long, underground pipelines. In the presence of spatial pit clustering, estimations could deteriorate, raising the need to increase the total inspection area in order to obtain the desired accuracy for the estimated maximum pit depth. In most practical situations, pit-depth samples collected along a pipeline belong to distinguishable groups, due to differences in corrosion environments. For example, it is quite probable that samples collected from the pipeline’s upper and lower external surfaces will differ and represent different pit populations. In that case, maximum pit-depth estimations should be made separately for these two quite different populations. Therefore, a good strategy to improve maximum pit-depth estimations is critically dependent upon a careful selection of the inspection area used for the extreme value analysis. The goal should be to obtain sampling sections that contain a pit population as homogenous as possible with regard to corrosion conditions. In this study, the aforementioned strategy is carefully tested by comparing extreme-value-oriented Monte Carlo simulations of maximum pit depth with the results of inline inspections. It was found that the variance to mean ratio, a measure of randomness, and the mean squared error of the maximum pit-depth estimations were considerably reduced, compared with the errors obtained for the entire pipeline area, when the inspection areas were selected based on corrosion-condition homogeneity.

Low-carbon steel specimens, all within API (American Petroleum Institute) specifications, were produced following different thermomechanical paths. After austenization, the samples were rolled and recrystallized. The rolling process was carried out using different reduction-in-thickness degrees and finishing temperatures. The investigated steels showed similar microstructural features but differed considerably in their crystallographic textures and grain boundary distributions. After cathodic hydrogen charging, hydrogen-induced cracking (HIC) was detected in the hot-rolled recrystallized steels, whereas the cold and warm-rolled recrystallized steels proved resistant to this damage. Among the investigated specimens, the HIC-stricken show either the strongest {001}ND texture fiber, the smallest fraction of low-angle grain boundaries, or the weakest {111}ND (γ) texture fiber ({hkl}ND representing crystallographic orientations with {hkl} planes parallel to the steel rolling plane). In contrast, the HIC-resistant steels show the weakest {001}ND texture fiber, the largest fraction of low-angle grain boundaries, and the strongest γ fiber. These results support the hypothesis of this and previous works, that crystallographic texture control, through warm rolling schedules, helps improve pipeline steel resistance to hydrogen-induced cracking.

The role of local crystallographic texture (microtexture) in hydrogen-induced crack interaction and coalescence is investigated in pipeline steels using stress simulation and orientation imaging microscopy. It is shown that, depending on the material’s microtexture, crack interaction and coalescence can significantly depart from the conditions predicted by the mixed-mode fracture mechanics of isotropic linear elastic materials. The results of stress simulations and microtexture analyses conducted on several observed crack interaction zones show that the presence of cleavage planes and slip systems favorably oriented to the mixed-mode stresses can activate low-resistance transgranular paths along which cracks can merge. In such situations, the response of the material to the mixed-mode stress state resulting from crack interaction produces results drastically different to that predicted by the fracture mechanics of isotropic linear elastic materials. This evidences the need for considering the material’s crystallographic texture when developing predictive models for the stepwise propagation of hydrogen-induced cracking in pipeline steels.

Small leaks caused by external pitting corrosion are the leading cause of failure in oil and gas pipelines in many regions of Mexico. Because of this, the need for realistic and reliable pitting corrosion growth models that are capable of accounting for the chemical and physical properties of soils and pipeline coatings is especially great. In this work, maximum pit depths and soil and coating data that were gathered at excavation sites across southern Mexico are used to investigate the impact of soil and pipe characteristics on pitting corrosion in buried pipelines. Soil field-measurements included resistivity, pH, pipe-to-soil potential, humidity, chloride, bicarbonate and sulphate levels, redox potential, soil texture and coating type. Together with the local physical chemistry of the soil and the coating characteristics, the maximum pit depth and pipeline’s age were recorded at more than 250 dig sites. The time dependence of the maximum pit depth was modeled as y max = β ( t−t 0 ) α , with β and α being positive constants, t being the pipe’s age and t 0 the pit initiation time. A multivariate regression analysis was conducted with y max as the dependent variable, while the pipeline’ age and the soil and pipe properties were used as the independent variables. The optimal dependence of β and α on these variables was found and predictive models were proposed to describe the time dependence of the average maximum pit depth and growth rate on soil and pipe properties. Besides the creation of a generic model fitted to all the gathered data, a model was proposed for each one of the three soil types identified in this study: clay, clay-loam and sandy-clay-loam. It is shown that the application of the proposed model allows for prediction of corrosion pit growth more accurately than previous models and that this improvement positively impacts on integrity management plans that address the threat posed by external pitting corrosion.

Currently, the reliability of non-piggable pipelines is mainly assessed either from historical failure data or from the results of direct assessment evaluations. When external, localized corrosion is the main threat to the pipeline integrity, the most important factor in assessing the reliability of a pipeline segment is the distribution of maximum pit depths. This distribution cannot be directly derived from historical failure data, nor from the information obtained from external corrosion direct assessment. In contrast, the statistical modeling of extreme values could be used to predict the distribution of pit depth maxima in a pipeline from a relatively small number of maximum pit depths measured at excavation sites along its length. Despite of the large number of works aimed at the application of the extreme value statistics, there is a lack of studies devoted to the applicability of the method for prediction of the maximum pit depth for the pit densities and pit spatial patterns typical of long buried pipelines. In this work, Monte Carlo simulations were conducted in order to assess the statistical errors associated with the prediction of the maximum pit depth for a wide range of the number and size of the inspection areas, pits per unit area and pit spatial patterns. As a result, the optimum area of inspection is proposed. The Monte Carlo numerical experiments were run by using synthetic and real corrosion data acquired by magnetic flux leakage and ultrasonic in-line inspection (ILI) tools, an approach that has not been reported in previous studies. The ILI data was sampled using standard methods of extreme value analysis, and the predicted maximum pit depth was compared with that reported by the in-line inspection. Monte Carlo simulations with synthetic and real corrosion data have allowed assessing the influence of the number and size of the inspected areas on the accuracy of predictions when pits distribute homogeneously and non-homogeneously in the pipeline. It is shown that, when the distribution of pits is homogeneous, the accuracy in the maximum pit depth prediction using the proposed method is similar to the measurement errors associated with magnetic flux leakage ILI tools.

External pitting corrosion constitutes the degradation mechanism responsible for about 66% of the incidents reported in the last decade for oil and gas pipelines in Mexico. Thus, major efforts are underway to improve the characterization and modeling of pitting corrosion of buried pipelines. Special attention is devoted to estimate the average corrosion rate and corrosion rate variance because they are the key parameters in the estimation of the trend in pipeline reliability. This work presents the results of field and simulation studies in which soil and pipe data were gathered together with the maximum depth of external corrosion pits found at more than 250 excavation sites across southern Mexico. The distributions of parameters such as chloride, bicarbonate and sulfate levels, resistivity, pH, pipe/soil potential, humidity, redox potential, soil texture and coating type have been used to predict the distribution of pitting corrosion rate of pipelines in contact with clay, clay-loam and sandy-clay-loam soils. The time dependence of the pitting corrosion rate was fitted to a power law through a multivariate regression analysis with the maximum pit depth as the dependent variable and the pipeline age and the soil and coating properties as the independent variables. Monte Carlo simulations were conducted in which random values drawn from the distributions fitted to the field data were used to evaluate the power law model proposed for the corrosion rate. For each soil type, the distribution that best fitted the corrosion rate data was found. The results of this study will provide reliability analysts with a more accurate description of the growth rate of external corrosion pits. It is expected that this information will positively impact on integrity management plans addressing the threat posed by this damage mechanism.

This work presents the results of ongoing investigations aimed at determining the influence of crystallographic texture, microtexture and mesotexture on hydrogen induced cracking (HIC) in low carbon pipeline steels. HIC samples of two steels were investigated using X-ray diffraction texture measurement and Orientation Imaging Microscopy (OIM™). The first steel is a low strength API 5L X46 retired from service and the second is a low sulfur ASTM A106 steel. The results of this work confirm the feasibility of improving the HIC resistance of pipeline steels through crystallographic texture control and grain boundary engineering. Controlled rolling schedules can be proposed in order to induce a crystallographic texture dominated by the {112}//ND, {111}//ND and {011}//ND fibres. Such a texture is expected to decrease significantly the steel susceptibility to HIC by: (i) reducing the number of available transgranular and intergranular low resistance cleavage paths provided by the {001}//ND oriented grains, (ii) reducing the probability of crack coalescence and stepwise HIC propagation and (iii) increasing the number of high resistance intergranular crack paths provided by coincidence site lattice (CSL) and low angle boundaries with the lowest possible energy.

In this work, the statistical methods for the reliability of repairable systems have been used to produce a methodology capable to estimate the annualized failure rate of a pipeline population from the historical failure data of multiple pipelines systems. The proposed methodology provides point and interval estimators of the parameters of the failure intensity function for two of the most commonly applied stochastic models; the homogeneous Poisson process and the power law process. It also provides statistical tests to assess the adequacy of the stochastic model assumed for each system and to test whether all systems have the same model parameters. In this way, the failure data of multiple pipeline systems are only pooled to produce a generic failure intensity function when all systems follow the same stochastic model. This allows addressing both statistical and tolerance uncertainty adequately. The proposed methodology is outlined and illustrated using real life failure data of multiple oil and gas pipeline systems.