Pipeline Corrosion Growth Modeling for In-Line Inspection Data Using a Population-Based Approach

2015 ◽  
Author(s):  
Markus R. Dann ◽  
Marc A. Maes ◽  
Mamdouh M. Salama
Keyword(s):  
Growth Process ◽  
Probabilistic Approach ◽  
Population Based ◽  
Metal Loss ◽  
Small Subset ◽  
Maintenance And Repair ◽  
Small Set ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Pipeline Corrosion

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.

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

2012 ◽  
Author(s):  
M. Al-Amin ◽  
W. Zhou ◽  
S. Zhang ◽  
S. Kariyawasam ◽  
H. Wang
Keyword(s):  
Growth Model ◽  
Power Law ◽  
Management Program ◽  
Metal Loss ◽  
Hierarchical Bayesian ◽  
Natural Gas Pipelines ◽  
Mcmc Simulation ◽  
Corrosion Defects ◽  
External Corrosion ◽  
Inspection Data

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.

Study on the Distribution of Submarine Pipeline Corrosion Defects Based on Internal Inspection Data and Data Mining Method

2020 ◽  
Author(s):  
Guoxi He ◽  
Sijia Chen ◽  
Kexi Liao ◽  
Shuai Zhao
Keyword(s):  
Least Square ◽  
Support Vector ◽  
Submarine Pipeline ◽  
Distribution Rules ◽  
Pipe Segment ◽  
Integrity Management ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Oil Gas ◽  
Pipeline Corrosion

Abstract Submarine pipelines in the sea are applied for oil, gas, water and mixed transportation. Among them, 91% of the pipes contain CO2. Here, based on the existing pipeline internal inspection data of submarine pipeline, the APRIORI algorithm and least-square-support-vector-machine (LSSVM) are applied to analyze the distribution rules and defect characteristics of internal defects along the pipeline. The corrosion defects are divided into 7 types and the pipeline section is divided into 12 intervals. Also, the pipe segment has been defined as J (general pipe), W (weld) and C (close to weld). The contents include the analysis of the characteristics and types of defects, the distribution of defects along the pipe, the severity of the corrosion defects, the size characteristics of defects, and the comparison of the data detected in multiple rounds. The defect depth of four kinds of pipelines is mostly 10%–20% of the wall thickness, hereby the severity of defects is studied via the percentage distribution of corrosion depth. The data of multi-round inspection shows that the corrosions in the mixed pipeline are active and the defects are increasing. The methods and results in this paper can be employed to predict the most likely defect type, mileage location, clock orientation, and shape size of submarine pipeline corrosion. This is helpful for the integrity management of submarine pipelines.

A Comprehensive Approach to Corrosion Management Based on Structural Reliability Methods

2006 ◽  
Author(s):  
Mark Stephens ◽  
Maher Nessim
Keyword(s):  
Structural Reliability ◽  
Failure Modes ◽  
Prediction Models ◽  
Probabilistic Approach ◽  
Corrosion Damage ◽  
Metal Loss ◽  
Corrosion Failure ◽  
Probability Estimates ◽  
Reliability Methods ◽  
Inspection Data

Quantitative analysis approaches based on structural reliability methods are gaining wider acceptance as a basis for assessing pipeline integrity and these methods are ideally suited to managing metal loss corrosion damage as identified through in-line inspection. The essence of this approach is to combine deterministic failure prediction models with in-line inspection data, the physical and operational characteristics of the pipeline, corrosion growth rate projections, and the uncertainties inherent in this information, to estimate the probability of corrosion failure as a function of time. The probability estimates so obtained provide the basis for informed decisions on which defects to repair, when to repair them and when to re-inspect. While much has been written in recent years on these types of analyses, the authors are not aware of any published methods that address all of the factors that can significantly influence the probability estimates obtained from such an analysis. Of particular importance in this context are the uncertainties associated with the reported defect data, the uncertainties associated with the models used to predict failure from this defect data, and the approach used to discriminate between failure by leak and failure by burst. The correct discrimination of failure mode is important because tolerable failure probabilities should depend on the mode of failure, with lower limits being required for burst failures because the consequences of failure are typically orders of magnitude more severe than for leaks. This paper provides an overview of a probabilistic approach to corrosion defect management that addresses the key sources of uncertainty and discriminates between failure modes. This approach can be used to assess corrosion integrity based on in-line inspection data, schedule defect repairs and provide guidance in establishing re-inspection intervals.

Time-Dependent Corrosion Growth Modeling Using Multiple In-Line Inspection Data

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Shenwei Zhang ◽  
Wenxing Zhou ◽  
Mohammad Al-Amin ◽  
Shahani Kariyawasam ◽  
Hong Wang
Keyword(s):  
Measurement Errors ◽  
Oil And Gas ◽  
Time Dependent ◽  
Random Measurement Error ◽  
Random Measurement ◽  
Proposed Model ◽  
Oil And Gas Pipelines ◽  
Corrosion Defects ◽  
Inspection Data ◽  
Pipeline Corrosion

This paper describes a nonhomogeneous gamma process-based model to characterize the growth of the depth of corrosion defect on oil and gas pipelines. All the parameters in the growth model are assumed to be uncertain; the probabilistic characteristics of these parameters are evaluated using the hierarchical Bayesian methodology by incorporating the defect information reported by the multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random measurement error associated with the ILI tools as well as the correlation between the measurement errors associated with different ILI tools are taken into account in the analysis. The application of the model is illustrated using an example involving real ILI data on a pipeline that is currently in service. The results suggest that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

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
Keyword(s):  
Growth Model ◽  
Power Law ◽  
Management Program ◽  
Metal Loss ◽  
Hierarchical Bayesian ◽  
Natural Gas Pipelines ◽  
Mcmc Simulation ◽  
Corrosion Defects ◽  
External Corrosion ◽  
Inspection Data

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.

Smart PIG “Fingerprint” Inspections Can Lead to Fitness for Purpose Assessments and Contractual Disputes

2002 ◽  
Author(s):  
Roland Palmer-Jones ◽  
Phil Hopkins ◽  
Popi Nafis ◽  
Gordon Wintle
Keyword(s):  
High Sensitivity ◽  
Baseline Survey ◽  
Metal Loss ◽  
Safe Operation ◽  
Line Pipe ◽  
Fit For Purpose ◽  
The Subject ◽  
Corrosion Defects ◽  
And Storage ◽  
Pipeline Corrosion

A recent ‘fingerprint’ smart pigging inspection recorded over 40,000 metal loss (corrosion) features in a 57km 42” diameter, dry gas pipeline supplying a major LNG facility in Indonesia. The pipeline had been in operation for less than 6 months. Assessment of these results by the inspection company identified 10 sections of pipe that required repair according to ASME B31.G, indicating that the pipeline was not ‘fit for purpose’. The pipeline operator immediately cut out these 10 sections to ensure the continued safe operation of the new pipeline. A detailed pipeline corrosion study subsequently identified the features as corrosion that had occurred during transport and storage of the line pipe. In addition, the corrosion was found to be less severe than initially thought and the same work assessed the remaining defects and, calculations using DNV Guideline RP F101, showed that the features were all acceptable. It was concluded that the high sensitivity of the smart pigging tool, combined with the failure to identify the cause of the features and the simple initial feature assessment overestimated the significance of the corrosion defects. This demonstrates the need for good care and inspection of line pipe during transport storage and construction. It also highlights the need to conduct engineering assessments to determine the inspection philosophy and to quantify the ‘workmanship’ level of metal loss features acceptable on a fingerprint run, before the run takes place. Otherwise new pipelines containing ‘custom and practice’ defects could be the subject of lengthy and costly disputes between operator and constructor. This paper proposes a method for assessing baseline survey data that provides an acceptance level for pre-existing defects. This methodology will assist operators in assessing smart pigging data from new pipelines.

A Hybrid Topside Structural Approach to the Management of Aging Fleet

2021 ◽  
Author(s):  
Biramarta Isnadi ◽  
Luong Ann Lee ◽  
Sok Mooi Ng ◽  
Ave Suhendra Suhaili ◽  
Quailid Rezza M Nasir ◽  
...  
Keyword(s):  
Structural Integrity ◽  
Metal Loss ◽  
Web Based ◽  
Strong Basis ◽  
Asset Risk ◽  
Risk Ranking ◽  
Design Life ◽  
Industry Standards ◽  
Integrity Management ◽  
Inspection Data

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.

Probability of Exceedance (POE) Methodology for Developing Integrity Programs Based on Pipeline Operator-Specific Technical and Economic Factors

2002 ◽  
Author(s):  
Rafael G. Mora ◽  
Curtis Parker ◽  
Patrick H. Vieth ◽  
Burke Delanty
Keyword(s):  
Decision Making ◽  
Probabilistic Models ◽  
Cost Benefit Analysis ◽  
Management Program ◽  
Metal Loss ◽  
Decision Making Process ◽  
Pipeline System ◽  
Probability Of Exceedance ◽  
Integrity Verification ◽  
Inspection Data

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.

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