Hierarchical Bayesian Corrosion Growth Model Based on In-Line Inspection Data

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Mohammad Al-Amin ◽  
Wenxing Zhou ◽  
Shenwei Zhang ◽  
Shahani Kariyawasam ◽  
Hong Wang

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.

Author(s):  
M. Al-Amin ◽  
W. Zhou ◽  
S. Zhang ◽  
S. Kariyawasam ◽  
H. Wang

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 parameters involved in the growth model are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on 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.


Author(s):  
Rafael G. Mora ◽  
Curtis Parker ◽  
Patrick H. Vieth ◽  
Burke Delanty

With the availability of in-line inspection data, pipeline operators have additional information to develop the technical and economic justification for integrity verification programs (i.e. Fitness-for-Purpose) across an entire pipeline system. The Probability of Exceedance (POE) methodology described herein provides a defensible decision making process for addressing immediate corrosion threats identified through metal loss in-line inspection (ILI) and the use of sub-critical in-line inspection data to develop a long term integrity management program. In addition, this paper describes the process used to develop a Corrosion In-line Inspection POE-based Assessment for one of the systems operated by TransGas Limited (Saskatchewan, Canada). In 2001, TransGas Limited and CC Technologies undertook an integrity verification program of the Loomis to Herbert gas pipeline system to develop an appropriate scope and schedule maintenance activities along this pipeline system. This methodology customizes Probability of Exceedance (POE) results with a deterministic corrosion growth model to determine pipeline specific excavation/repair and re-inspection interval alternatives. Consequently, feature repairs can be scheduled based on severity, operational and financial conditions while maintaining safety as first priority. The merging of deterministic and probabilistic models identified the Loomis to Herbert pipeline system’s worst predicted metal loss depth and the lowest safety factor per each repair/reinspection interval alternative, which when combined with the cost/benefit analysis provided a simplified and safe decision-making process.


Author(s):  
M. Al-Amin ◽  
S. Kariyawasam ◽  
S. Zhang ◽  
W. Zhou

External metal-loss corrosion is one of the major contributing factors for pipeline failures in North America. Corrosion growth rate plays a crucial role in managing corrosion hazard for gas and liquid pipelines. Quantifying the growth of corrosion over time is critically important for the risk and reliability analysis of pipelines, planning for corrosion mitigation and repair, and determination of time intervals for corrosion inspections. Conservatism in predicting the growth rate has significant engineering implication as non-conservatism can lead to critical anomalies being missed by mitigation actions and may cause pipeline failure; whereas, over conservatism can lead to unnecessary inspections and anomaly mitigations that may result in significant unnecessary cost to pipeline operators. As more and more pipelines are now being inspected by in-line inspection (ILI) tools on a regular basis, the ILI data from multiple inspections provide valuable information about the growth of corrosion anomalies on the pipeline. Although the application of linear growth rate calculated by comparing depths from two successive ILI is a common practice in the pipeline industry, research has shown that the growth of corrosion anomaly is non-linear and anomaly-specific. The authors of this paper have previously developed anomaly-specific non-linear corrosion growth model based on multiple ILI data. The objectives of this paper are to demonstrate the appropriateness of anomaly-specific non-linear corrosion growth model, and to illustrate the advantages of using non-linear corrosion growth model in the integrity management program. Two case studies were performed to illustrate the application of non-linear growth model by incorporating the measurement errors associated with the ILI tools, which include both the bias (constant and non-constant) and random scattering error. The findings of these case studies are presented in this paper.


Author(s):  
Miaad Safari ◽  
David Shaw

Abstract As integrity programs mature over the life of a pipeline, an increasing number of data points are collected from second, third, or further condition monitoring cycles. Types of data include Inline Inspection (ILI) or External Corrosion Direct Assessment (ECDA) inspection data, validation or remediation dig information, and records of various repairs that have been completed on the pipeline system. The diversity and massive quantity of this gathered data proposes a challenge to pipeline operators in managing and maintaining these data sets and records. The management of integrity data is a key element to a pipeline system Integrity Management Program (IMP) as per the CSA Z662[1]. One of the most critical integrity datasets is the repair information. Incorrect repair assignments on a pipeline can lead to duplicate unnecessary excavations in the best scenario and a pipeline failure in the worst scenario. Operators rely on various approaches to manage and assign repair data to ILIs such as historical records reviews, ILI-based repair assignments, or chainage-based repair assignments. However, these methods have significant gaps in efficiency and/or accuracy. Failure to adequately manage excavation and repair data can lead to increased costs due to repeated excavation of an anomaly, an increase in resources required to match historical information with new data, uncertainty in the effectiveness of previous repairs, and the possibility of incorrect assignment of repairs to unrepaired features. This paper describes the approach adopted by Enbridge Gas to track and maintain repairs, as a part of the Pipeline Risk and Integrity Management (PRIM) platform. This approach was designed to create a robust excavation and repair management framework, providing a robust system of data gathering and automation, while ensuring sufficient oversight by Integrity Engineers. Using this system, repairs are assigned to each feature in an excavation, not only to a certain chainage along the pipeline. Subsequently, when a new ILI results report is received, a process of “Repair Matching” is completed to assign preexisting repairs and assessments to the newly reported features at a feature level. This process is partially automated, whereby pre-determined box-to-box features matched between ILIs can auto-populate repairs for many of the repaired features. The proposed excavation management system would provide operators a superior approach to managing their repair history and projecting historical repairs and assessments onto new ILI reports, prior to assessing the ILI and issuing further digs on the pipeline. This optimized method has many advantages over the conventional repair management methods used in the industry. This method is best suited for operators that are embarking on their second or third condition monitoring cycle, with a moderate number of historical repairs.


Author(s):  
Szabolcs Sza´vai ◽  
Gyo¨ngyve´r B. Lenkey

The most important question for the user is if pipelines having metal loss defect could be operated safely, if any pipe sections should be repaired or replaced, and how much is the reserved safety against a possible failure. There are several engineering methods for determining the safety margin of operation but those are usually quite conservative. For this reason Lenkey has proposed safety diagrams based on finite element analysis of external corrosion defects in underground pipelines [4]. These safety diagrams could be used to determine safety factors in a less conservative way for critical situations during the pipeline operation. The FEM calculations have been verified by burst tests carried out on several pipe sections. In the present paper the results of some further analyses are presented about the difference between the measured, numerically and analytically determined failure pressure values.


Author(s):  
Lisa Barkdull ◽  
Herbert Willems

The information supplied from inline inspection data is often used by pipeline operators to make mitigation and/or remediation decisions based on integrity management program requirements. It is common practice to apply industry accepted remaining strength pressure calculations (i.e. B31G, 0.85 dl, effective area) to the data analysis results from an inline inspection survey used for the detection and characterization of metal loss. Similar assessments of data analysis results from an ultrasonic crack detection survey require expert knowledge in the field of fracture mechanics and, just as importantly, require knowledge to understand the limitations of shear wave ultrasonic technology as applied to an inline inspection tool. Traditionally, crack-like and crack-field features have been classified with a maximum depth distributed over the entire length of the feature; crack-field features also have width reported. In an effort to provide further prioritization, techniques such as “longest length” or “interlinked length” [1] have been employed. More recently, an effort has been made to provide a depth profile of the crack-like or crack-field feature using the ultrasonic crack detection data analysis results. This presentation will discuss the advantages of post assessment of ultrasonic crack detection data analysis results to aid in the evaluation of pipeline integrity and discuss the limitations of advanced analysis techniques. Additionally, the potential for new inline inspection ultrasonic technologies which lend themselves to more accurate data analysis techniques will be reviewed.


Author(s):  
Terry Huang ◽  
Shahani Kariyawasam ◽  
Patrick Yeung ◽  
Mohammad Shariq

The recent industry wide post-ILI pipeline ruptures due to external corrosion happened in a relatively short period of time after the ILI using high-resolution Magnetic Flux Leakage (MFL) technology. Failure investigations show that the critical defects that caused these pipeline ruptures are generally long and complex corrosion, which typically consist of a number of deep corrosion pits (i.e. localized metal-loss) within an overall shallower, but relatively large corrosion area (i.e. generalized metal-loss). This has led us to investigate the gaps and “blind spots” of the ILI-based corrosion management program particularly to find out why existing methods fail to effectively identify and remediate such critical defects before they fail. Learning from these post-ILI failures, TransCanada has developed many assessment methods and criteria for identifying challenging areas. The many types of criteria account for blind spots from different perspectives in a multi-faceted manner. The traditional ILI based corrosion management programs calculate a deterministic failure pressure ratio (FPR) and maximum anomaly depth and ensure these do not reach a limiting value. However, this strictly deterministic assessment does not acknowledge the uncertainties, particularly the significant uncertainties in the ILI measurements, assessment models, and material properties. When all uncertainties are accounted for and a probabilistic excavation criterion is used, the excavations reveal that certain anomalies are found to be near-critical in the field even though the deterministic FPR based criteria did not identify these. The probabilistic criteria identifies longer shallower anomalies, with non-critical ILI based FPR values, as anomalies that have a higher probability of exceeding the FPR criteria in-the-ditch (where the uncertainties are minimized). This is because the probabilistic criterion acknowledges that longer anomalies are more sensitive to the depth measurement error and have a higher probability of becoming critical in-the-ditch. This “blind spot” in the deterministic method was overcome by incorporating a probabilistic criterion into the corrosion management program. The effectiveness of these new measures is discussed by examining excavation results of this program and subsequent ILI results. This paper discusses the approach to corrosion management where new learning and knowledge as well as new-found uncertainties are readily accommodated. The approach is also transparent and documented; so that new information can be incorporated into the assessment and post-ILI failures can be prevented more effectively.


Author(s):  
Markus R. Dann ◽  
Marc A. Maes ◽  
Mamdouh M. Salama

To manage the integrity of corroded pipelines reliable estimates of the current and future corrosion growth process are required. They are often obtained from in-line inspection data by matching defects from two or more inspections and determining corrosion growth rates from the observed growth paths. In practice only a (small) subset of the observed defects are often reliably matched and used in the subsequent corrosion growth analysis. The information from the remaining unmatched defects on the corrosion growth process are typically ignored. Hence, all decisions that depend on the corrosion growth process such as maintenance and repair requirements and re-inspection intervals, are based on the information obtained from the (small) set of matched defects rather than all observed corrosion anomalies. A new probabilistic approach for estimating corrosion growth from in-line inspection data is introduced. It does not depend on defect matching and the associated defect matching uncertainties. The reported defects of an inspection are considered from a population perspective and the corrosion growth is determined from two or more defect populations. The distribution of the reported defect sizes is transformed into the distribution of the actual defect sizes by adjusting it for detectability, false calls, and sizing uncertainties. The obtained distribution is then used to determine the parameters of the assumed gamma-distributed corrosion growth process in order to forecast future metal loss in the pipeline. As defect matching is not required all reported corrosion defects are used in the probabilistic analysis rather than the truncated set of matched defects. A numerical example is provided where two in-line inspections are analyzed.


Author(s):  
Haralampos Tsaprailis ◽  
Mona Abdolrazaghi ◽  
Jeff Liang

In this paper, pipelines with similar parameters (e.g., diameter, environment, temperature of the product, etc.) are used to compare the effectiveness of field repair coatings (i.e., liquid epoxy and tape coating systems) against external corrosion. Liquid epoxy and tape coating systems are compared based on number of corrosion features, severity, morphology and distribution of features over depth and length. Once corrosion defects are assessed in the field and a repair coating is applied, In-Line Inspection (ILI) tools can detect and size the corrosion features under these coatings. If the corrosion feature is growing, the growth can be detected and measured using ILI data assessment. In this paper, data from both field nondestructive examination (NDE) and ILI measurements collected from 1996 to 2013 are used to assess the corrosion growth rate (CGR) under both types of repair coatings. The CGR with respect to depth is compared for both liquid epoxy and tape coatings and then normalized by the number of corrosion features over the segment. In addition, the effectiveness of cathodic protection was evaluated at the locations of the assessed repair coating. Ultimately, this paper provides useful information to pipeline operators for decision making regarding the choice of repair coatings based on operational data collected over 14 years.


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