J.M. Hallen

Predictive Model for Pitting Corrosion in Buried Oil and Gas Pipelines

Abstract A predictive model for pitting corrosion in buried pipelines is proposed. The model takes into consideration the chemical and physical properties of the soil and pipe to predict the time dependence of pitting depth and rate. Maximum pit depths were collected together with soil and pipe data at more than 250 excavation sites over a three-year period. The time dependence of the maximum pit depth was modeled as dmax = κ(t − t0)ν, where t is the exposure time, t0 is the pit initiation time, and κ and ν are the pitting proportionality and exponent parameters, respectively. A multivariate regression analysis was conducted with dmax as the dependent variable and the pipeline age, and the soil and pipe properties as the independent variables. The dependence of κ and ν on the predictor variables was found for the three soil textural classes identified in this study: clay, clay loam, and sandy clay loam. The proportionality parameter κ was found to be primarily influenced by the redox potential, pH value, ...

Criteria for performance assessment and calibration of in-line inspections of oil and gas pipelines

Oil and gas pipeline operators routinely use magnetic flux leakage (MFL) and ultrasonic (UT) in-line inspection (ILI) to detect, locate and size metal losses caused by corrosion. As a preliminary step in fitness-for-service evaluations, the quality of the ILI is assessed through statistical comparison of the ILI data with data gathered in the field at dig sites. This work presents generalized criteria for the performance assessment and calibration of MFL and UT ILI tools from field measurements. The proposed criteria are capable of accounting for the measurement errors of both the ILI tool and the field instrument. The performance assessment of the ILI run is based on the determination of the minimum number of unsuccessful field verifications required to reject the ILI at a given significance level. The calibration of the ILI data uses new, simplified, error-in-variables methods to estimate the true size of the corrosion metal losses reported by the ILI tool. The proposed methodology also allows for determination of the errors associated with the estimation of the true defect depths. This information is of utmost importance in conducting reliability and risk assessments of pipelines based on either the probability distribution properties of the pipeline defect population, or the probability of failure of each individual defect in the pipeline. The proposed criteria are tested using Monte Carlo simulations and a real-life case study is presented to illustrate their application.

Markov Chain Models for the Stochastic Modeling of Pitting Corrosion

The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorovs forward equations is used to describe the transition probability function in a discrete pit depth space. The transition probability function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorovs system of equations is also found for the transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.

Statistical Analysis of Pitting Corrosion Field Data and Their Use for Realistic Reliability Estimations in Non-Piggable Pipeline Systems

The reliability and risk of non-piggable, corroding oil and gas pipelines can be estimated from historical failure data and through reliability models based on the assumed or measured number of corrosion defects and defect size distribution. In this work, an extensive field survey carried out in an upstream gathering pipeline system in Southern Mexico is presented. It has helped determine realistic values for the number of corrosion defects per kilometer (defect density) and obtain a better description of the corrosion defect size distributions in this system. To illustrate the impact that these new corrosion data can have on pipeline risk management, a reliability study is also presented where the field-gathered corrosion data have been used as input to a reliability framework for the estimation of the failure index of non-piggable pipelines and pipeline systems when different amounts of corrosion data are available.

Technical Note: Field Study—Pitting Corrosion of Underground Pipelines Related to Local Soil and Pipe Characteristics

Abstract Recently, the authors proposed a new predictive model for pitting corrosion in underground pipelines. The model is based on field measurements of maximum pitting corrosion depth together with local soil and pipeline characteristics. The pitting corrosion data collection was conducted over a three-year period, for onshore buried pipelines operating in southern Mexico. This technical note contains a detailed description of the results of the field measurements, indicating the data entries classified as outlier observations and the textural soil class ascribed to each data entry.

Effect of artificial aging time on the mechanical properties of weldment on API 5L X-52 line pipe steel

The effect of artificial aging time on the microstructure and mechanical properties in the weld and base metals of an API 5L-X52 line pipe steel was studied. Artificial aging was performed for 1000 hours at 250 °C and was monitored every 100 hours. Vickers hardness and tensile tests were used to examine the aging effect on the mechanical properties. Scanning electron microscopy and transmission electron microscopy (TEM) studies were carried out to analyze the microstructure evolution. The Vickers hardness results showed that the weld and base metals displayed a hardening tendency up to a maximum value at 500 hours of aging. The yield strength increased with aging time while the elongation-to-fracture decreased. The maximum yield strength was found at 500 hours, which was attributed to the peak-aged condition. After 500 hours, both the Vickers hardness and yield strength were reduced while the elongation was increased due to the overaging condition. The TEM observations and fracture analysis of specimens showed that the improvement of strength was associated with the nanoparticles’ precipitation, while the degradation of the microstructure and mechanical properties was related to the coarsening process of iron carbide (cementite) and niobium carbide for the weld and base metals, respectively. The largest amount of precipitation in both alloys occurred at 500 hours.

Efecto del envejecimiento natural sobre la microestructura, propiedades mecánicas, resistencia a corrosión y fractura en uniones soldadas de acero para tubería API5L X52

A characterization study was done to analyze how microstructural regions affect the mechanical properties, corrosion and fractography of the Heat Affected Zone (HAZ), weld bead and base metal for pipe naturally aged for 21 years at 30 °C. Results showed that microstructures exhibited damage and consequently decrease in properties, resulting in over-aged due to service. SEM analysis showed that base metal presented coarse ferrite grain. Tensile test indicated that microstructures showed discontinuous yield. Higher tensile strength was obtained for weld bead, which exhibited a lower impact energy in comparison to that of HAZ and base metal associated with brittle fracture by trans-granular cleavage. The degradation of properties was associated with the coarsening of nano-carbides observed through TEM images analysis, which was confirmed by SEM fractography of tensile and impact fracture surfaces. The weld bead reached the largest void density and highest susceptibility to corrosion in H2S media when compared to those of the HAZ and base metal.

Discussion: Statistical Characterization of Pitting Corrosion—Part 1: Data Analysis and Part 2: Probabilistic Modeling for Maximum Pit Depth

Abstract A. Valor, et al., discuss “Statistical Characterization of Pitting Corrosion—Part 1: Data Analysis” and “Statistical Characterization of Pitting Corrosion—Part 2: Probabilistic Modeling for Maximum Pit Depth,” by R.E. Melchers, which were published in Corrosion 61, 7 (2005), p. 655–664 and Corrosion 61, 8 (2005), p. 766–777, respectively. A reply from R.E. Melchers follows.

Defect Detection in Aluminium with an Eddy Currents Sensor

The purpose of this paper is to present an eddy current testing technique for surface defect detection in conducting materials using a giant magneto resistive (GMR) sensor. A flat coil is used to produce an alternate magnetic field, which gives rise to eddy currents in the material under test. The GMR sensor with the coil placed on top of it is mounted in a holder, which is moved over the surface of the metal plate using an XY table. Three aluminium plates were used with defects having nominals depths of 2, 4, 6, and 8 mm and widths of 0.6, 1 and 1.4 mm, respectively. The defects were scanned with the sensing axis perpendicular to the defect length. The GMR output voltage depends on the width and depth of the defects. Two parameters extracted from the GMR output voltage signal are obtained and a simple correlation between the defects dimensions and the GMR output voltage is proposed.

Crack detection in steel using a GMR-based MFL probe with radial magnetization

This paper presents the development of a portable probe for detecting cracks in steel plates. The probe consists of a magnet and a giant magneto-resistance (GMR) sensor. The magnet provides a radial magnetization at the surface of the steel plate. The GMR sensor detects the tangential component of the magnetic flux leakage due to a crack in the steel plate. Two steel plates were inspected with six cracks of depths: 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 mm, and widths of 0.25 and 0.5 mm, respectively. The cracks were scanned with the GMR sensitivity axis at 90°, 80°, 70°, 60° and 50°. It is demonstrated that the output voltage of the GMR sensor is sensitive to the orientation of the crack.