Probabilistic growth modeling for metal-loss corrosion defects on energy pipelines using inspection data

Author(s):  
S Zhang ◽  
W Zhou
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):  
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.


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.


2021 ◽  
Author(s):  
Biramarta Isnadi ◽  
Luong Ann Lee ◽  
Sok Mooi Ng ◽  
Ave Suhendra Suhaili ◽  
Quailid Rezza M Nasir ◽  
...  

Abstract The objective of this paper is to demonstrate the best practices of Topside Structural Integrity Management for an aging fleet of more than 200 platforms with about 60% of which has exceeded the design life. PETRONAS as the operator, has established a Topside Structural Integrity Management (SIM) strategy to demonstrate fitness of the offshore topside structures through a hybrid philosophy of time-based inspection with risk-based maintenance, which is in compliance to API RP2SIM (2014) inspection requirements. This paper shares the data management, methodology, challenges and value creation of this strategy. The SIM process adopted in this work is in compliance with industry standards API RP2SIM, focusing on Data-Evaluation-Strategy-Program processes. The operator HSE Risk Matrix is adopted in risk ranking of the topside structures. The main elements considered in developing the risk ranking of the topside structures are the design and assessment compliance, inspection compliance and maintenance compliance. Effective methodology to register asset and inspection data capture was developed to expedite the readiness of Topside SIM for a large aging fleet. The Topside SIM is being codified in the operator web-based tool, Structural Integrity Compliance System (SICS). Identifying major hazards for topside structures were primarily achieved via data trending post implementation of Topside SIM. It was then concluded that metal loss as the major threat. Further study on effect of metal loss provides a strong basis to move from time-based maintenance towards risk-based maintenance. Risk ranking of the assets allow the operator to prioritize resources while managing the risk within ALARP level. Current technologies such as drone and mobile inspection tools are deployed to expedite inspection findings and reporting processes. The data from the mobile inspection tool is directly fed into the web based SICS to allow reclassification of asset risk and anomalies management.


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.


2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Ahmed A. Soliman ◽  
Mohammad M. Megahed ◽  
Ch. A. Saleh ◽  
Mostafa Shazly

Abstract Corrosion in pipes is usually found in the form of closely spaced defects, which eventually reduce the pipe pressure carrying capacity and piping planned useful life. Codes and standards have been developed to evaluate the effect of such form of metal loss on the piping pressure carrying capacities. However, predictions of such codes are usually conservative, and hence, there is a need to assess their degree of conservatism. The present paper utilizes nonlinear finite element analysis (FEA) in estimating pressure carrying capacities of defective pipes, and hence provides an evaluation of codes degree of conservatism. Shell elements with reduced thickness at the corrosion defect are adopted and their accuracy is assessed by comparison with those of solid elements as well as experimental test results. The influence of defects interaction is investigated by considering two neighboring defects in an inclined direction to each other. The influence of inclination angle, inclined proximity distance between the two defects, and the defect depth to wall thickness ratio are investigated. Comparisons were made with predictions of codes of practice in all cases. Code predictions were found to be conservative compared to FEA results. Furthermore, the interaction rule embedded in the codes for checking for interaction leads to inaccurate predictions for closely spaced defects as it does not include the effect of defect depth.


Author(s):  
H. Willems ◽  
K. Reber ◽  
M. Zo¨llner ◽  
M. Ziegenmeyer

Inline inspection of pipelines by means of intelligent pigs usually results in large amounts of data that are analyzed offline by human experts. In order to increase the reliability of the data analysis process as well as to speed up analysis times methods of artificial intelligence such as neural networks have been used in the past with more or less success. The basic requirement for any technique to be used in practice is that no relevant features should be overlooked while keeping the false call rate as low as possible. For the task of automated analysis of in-line inspection data obtained from ultrasonic metal loss inspections, we have developed a two-stage approach. In a first step (called boxing), any defect candidates exceeding the specified size limits are recognized and described by a surrounding box. In the second step, all boxes from step 1 are analyzed yielding basically a relevant/non relevant decision. Each feature considered to be relevant is then classified according to a given set of feature classes. In order to efficiently perform step 2, we have adapted the SVM (support vector machines) algorithm which offers some important advantages compared to, for example, neural networks. We describe the approach applied, and examples as obtained from in-line inspection data are presented.


Author(s):  
H. Qin ◽  
W. Zhou

This paper presents a methodology to evaluate the reliability of corroding pipelines by simultaneously considering the growth and generation of corrosion defects. The non-homogeneous Poisson process is employed to model the generation of corrosion defects, whereas the non-homogeneous gamma process is used to characterize the growth of corrosion defects once generated. The parameters included in the non-homogeneous Poisson process and non-homogeneous gamma process are evaluated from the inline inspection data using a hierarchical Bayesian model. The measurement errors associated with the inline inspection tools are taken into account in the Bayesian updating. The time-dependent failure probability of the corroding pipeline is evaluated using the Monte Carlo simulation technique. The methodology is illustrated using a natural gas pipeline that has been subjected to multiple inline inspections over a period of time. The results illustrate the necessity to incorporate the generation of new corrosion defects in the reliability analysis of corroding pipelines.


Author(s):  
José L. F. Freire ◽  
Ronaldo D. Vieira ◽  
Pablo M. Fontes ◽  
Adilson C. Benjamin ◽  
Luis S. Murillo C. ◽  
...  

The Critical Path (CP) Method (CPM proposes a set of rules allowing the drawing of failure lines that represent adjacent areas positioned along selected circumferential and longitudinal directions of pipelines that contain colonies of corrosion defects. Failure pressures are calculated for each of those lines to determine the most critical one. This selected line is considered as the most probable path of rupture, and it corresponds to the minimum calculated internal pressure to take the pipeline to fracture. The proposed method was checked against twelve burst pressure tests performed on pipeline tubular specimens. Three specimens were labeled as control specimens — one was a pipe without defect and the other two had single small base defects of different depths. Nine of the specimens contained interacting corrosion defects, which were composed of the combinations of two or more base defects. Comparisons were made of the measured burst pressures with those predicted by the CPM, by one recently proposed method called MTI, version 1, or MTI V1, and by four other Level-1 or Level-2 assessment methods, namely the American Society of Mechanical Engineers (ASME) B31G method, the Det Norske Veritas (DNV) RP-F101 for single and for complex and interacting defects, and the RSTRENG Effective Area method. The CPM and MTI V1 methods predicted the failure pressures closest to the actual test failure pressures, with the CPM presenting suitable small mean error of evaluation as well as very low standard deviation error for its predictions.


Author(s):  
Adilson C. Benjamin ◽  
Aldo R. Franzoi ◽  
Jose´ Luiz F. Freire ◽  
Ronaldo D. Vieira ◽  
Jorge L. C. Diniz

A corrosion defect can be considered as being of a regular shape if its defect depth profile is relatively smooth and the longitudinal area of metal loss is approximately rectangular. A corrosion defect can be considered as being of an irregular shape if its defect depth profile presents one or more major peaks in depth. In this paper the burst tests of four tubular specimens are presented. In these tests the tubular specimens were loaded with internal pressure only. The specimens were cut from longitudinal welded tubes made of API 5L X80 steel with a nominal outside diameter of 457.2 mm (18 in) and a nominal wall thickness of 7.93 mm (0.312 in). Each of the four specimens had one external irregular shaped corrosion defect, machined using spark erosion. Measurements were carried out in order to determine the actual dimensions of each tubular specimen and its respective defect. Tensile specimens and impact test specimens were tested to determine material properties. The failure pressures measured in the laboratory tests are compared with those predicted by six assessments methods, namely: the ASME B31G method, the RSTRENG 085dL method, the DNV RP-F101 method for single defects, the RPA method, the RSTRENG Effective Area method and the DNV RP-F101 method for complex shaped defects.


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