Numerical Assessment of Dented Pipe Using Inline Inspection Data

Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.

This paper establishes a dynamic Bayesian network to model the growth of corrosion defects on energy pipelines. The integrated model characterizes the growth of defect depth by a homogeneous gamma process and considers the biases and random errors associated with the in-line inspection (ILI) tools. The distributions of the mean value and coefficient of variation of the annual growth of defect depth are learned from multiple ILI data using the parameter learning technique of Bayesian networks. With the same technique, the distributions of the biases and standard deviation of random errors associated with ILI tools are learned from ILI data and their corresponding field measurements. An example with real corrosion management data is used to illustrate the process of developing the model structure, learning model parameters and predicting the corrosion growth and time-dependent failure probability. The results indicate that the model can in general predict the growth of corrosion defects with reasonable accuracy and the ILI-reported and field-measured depth can be used to update the time-dependent failure probability in a near-real-time manner. In comparison with existing growth models, the graphical feature of Bayesian networks makes it more intuitive and transparent to users. The employment of parameter learning provides a semi-automated and convenient approach to elicit the probabilistic information from ILI and field measurement data. The above advantages will facilitate the application of the model in the practice of corrosion management in pipeline industry.

The paper describes a new practical method of updating the stochastic remnant life of pipelines with defects using ILI data. The paper describes a comprehensive algorithm for assessing pipeline remnant life taking into account the stochastic results of in-line inspection (ILI). It is assumed that the pipeline segment wall has a longitudinal external crack of semi-elliptical form and is described by the J-integral. The limit state function (LSF) is described as the difference of the critical and current value of the J-integral. The latter is calculated for the current time of pipe performance and is assumed known due to monitoring of the pumping equipment. The critical crack depth is defined using the notion of fracture toughness and the J-integral approach. The algorithm contains solutions of three sequentially interconnected problems. First, the deterministic problem of fatigue crack growth (FCG) is analyzed. Then the stochastic FCG is analyzed. The probability of failure assessment algorithm is designed on the basis of the authors’ version of the adaptive important sampling (AIS) procedure. The main steps of the AIS algorithm are described in detail. The samples are generated in such a way, that at all times a majority of samples belong to the fracture region. Finally, the results of the latest ILI are fused into the algorithm, providing best possible assessment of pipeline remnant life as a random variable. The remnant life update for pipeline segment with crack-like defects using ILI data takes into account three possible outcomes: defect not discovered: defect is discovered but not measured; defect is discovered and measured. This result permits solving most important problems of pipeline maintenance: prioritization of pipeline segments for repair/rehabilitation; optimization of the time between ILI; minimization of pipe operational risk. Two real cases are described of assessing the probability of fracture/leak of a pipeline section with an external crack at different periods of its performance. The described approach currently is being generalized for the case of multiple stress corrosion SC cracks.

With the publication of GB 32167, pipeline in-line inspection has been executed comprehensively in China, which causes many problems such as mismatching, missing and misreporting of pipeline features, commanding inspection level of different providers to improve the inspection quality, data mining of multiple rounds of inspections and so on. In-line inspection data alignment can solve these problems efficiently. In this paper, a series of in-line inspection data automatic alignment experiences and rules are put forward, which can find out the error of in-line inspection data initially, realize the alignment of above-ground and underground features of pipeline, search and locate the combined defects of one inspection data, and match defects of different rounds of inspections. Thus problems such as locating pipeline defects and segments above ground, judging and analysis of different kinds of combined defects, quality verification of inspection data from different service providers, knowing the evolution of defects for pipeline, etc. can be solved. The methodology is applied to one certain product oil pipeline in China and the following results are obtained. Two ILIs data alignment rates of bends, girth wields and defects are 100%, 99.05% and 28.6%. The result shows that 2 dents intersecting the metal loss in 2016 ILI and 4 metal losses in the heat affected zone of girth weld in 2021 ILI are unreported. Reasons for low defects alignment rate are proposed. The results demonstrate problems such as misreporting and missing of combined defects, bends, girth welds and other features, as well as quality problems and logical errors existed in two rounds of ILIs data. The relevant advices were given to the pipeline enterprise finally.

Technip Energies has recently completed a fit-for-service and life extension project for nearly 200 submarine pipelines with a total length of over 2,000 km in the Gulf of Thailand. One of the main challenges in this project was to accurately determine the remaining life and necessary intervention works for free spans, which are a typical concern for pipeline integrity with respect to local buckling and fatigue capacity. Initial assessments revealed that the conventional free span evaluation method of comparing observed free span lengths with analytically determined allowable lengths would lead to extensive intervention works. Additionally, ROV (Remotely Operated Vehicle) surveys showed a continuous increase in free span lengths due to scouring, and pipeline Inline Inspections (ILI) identified multiple active corrosion defects at span locations. These findings underscored the importance of developing an automated approach to enhance free span assessment accuracy with higher workflow efficiency. The automated approach developed integrates industry-standard design tools with tailored in-house programs to streamline the free span assessment process. The workflow begins with a detailed analysis of pipeline design data, ROV survey and inline inspection data. ROV survey data from multiple inspection campaigns are analyzed to determine free span lengths, gaps, and growth rates, while inline inspection data are used to identify major corrosion defects and their growth. An in-house program is then used to develop a global finite element (FE) model for the entire pipeline – either on a flat seabed with predefined free spans or on a more realistic undulated seabed profile to account for rotated boundary conditions. This FE model accurately captures the on-bottom condition of the free spans, including the effective axial force. Free span growth is either incorporated directly in the global model (through multiple analysis iterations) or its effect is considered separately through a mapping function. For spans affected by corrosion, a separate specialized program develops a localized 3D shell element finite element model featuring local pipe wall thickness reduction from corrosion to determine equivalent pipe stiffness for fatigue analysis and corroded bending moment capacity for local buckling assessment. Outputs extracted from the global FE models serve as inputs to pipeline local buckling assessment and Vortex-Induced-Vibration (VIV) fatigue analysis software for a comprehensive fatigue analysis. This paper presents the developed automated free span assessment methodology together with a case study of a typical free spanning pipeline with internal corrosion. The paper compares various modelling techniques and their effects on fatigue damage and local buckling assessment results. The findings provide valuable insights for the assessment of free spans in corroded pipelines in dynamic seabed environments, enhancing the service life of operating subsea pipelines while substantially reducing unnecessary intervention costs.

Operators wish to understand the condition of their pipelines to manage ongoing integrity. Information on the condition of the pipeline along its entire length can be obtained using in-line inspection (ILI). However, some pipelines cannot be internally inspected due, for example, to tee connections, tight bends, low flow or to a lack of launcher and receiver facilities. The condition of these ‘unpiggable’ lines can sometimes be largely unknown. To aid the understating of the pipeline condition without ILI data, operators will often rely on alternative sources of information, such as localised external inspections, model predictions and company and individual experience. However, there may be significant uncertainty associated with these alternative data sources when using them to assess the condition of the entire pipeline. This uncertainty may be understood by applying a probabilistic approach to the assessment of pipeline integrity using structural reliability analysis (SRA) methods. An SRA approach applies probabilistic input parameters to a failure prediction model for a defined limit state function. Previous IPC papers[1,2,3] have presented guidance on probabilistic assessments to model pipeline failure. Recommended probability distributions are presented which account for uncertainties associated with line pipe properties, defect sizing and the error associated with the predicted failure model. However, there is little published guidance readily available on recommended defect characteristic distributions specific to internal corrosion features. Parameter distributions are recommended for defect sizing based on empirical data, which are mainly used for external corrosion features. In this paper, a case study is used to present a practical application of an SRA methodology for assessment of pipeline integrity with respect to internal corrosion. Discussion is presented on alternative sources of information for the assessment when ILI data is unavailable, including targeted external inspections of unpiggable lines and data sets from comparable piggable lines. Probability distributions are derived from the available inspection data for the internal corrosion feature size and corrosion rate input parameters to the SRA. Probabilistic analysis is used to account for the expected population of unknown features in the uninspected parts of the pipelines. The expected feature size, corrosion rate and feature density calculated are used in the SRA to estimate the total probability of failure due to internal corrosion over time for the entire length of the pipeline. Recommendations are provided on the application of an SRA methodology to assess pipeline failure due to internal corrosion.

The application of in-line inspection (ILI) to assess pipelines for various anomalies is standard practice in the pipeline industry. When ILI data identifies the presence of anomalies such as denting or ovalization, current convention is to perform either a depth-based or strain-based assessment to assess the severity. Although a strain-based methodology is generally accepted in the pipeline industry, this approach does not address all of the primary damage mechanisms associated with pipeline dents. Assessment based upon either depth or strain alone may not only provide non-conservative results but also fail to properly rank dents in order of their true severity. A life-cycle assessment approach that considers the damage caused by the dent formation, the stress intensification effect of the dent profile, and the severity of future pressure cycling provides an improved understanding of the probability of failure, allowing for more informed integrity management decision making. Strain-based assessment of dents in pipelines is typically performed by calculating the local curvatures in the dent geometry as measured by ILI. Local strains are then calculated based on these local curvatures. However, this approach does not address that once a dent has been formed, continued pressure cycling at that location is what will ultimately cause a failure. The current strain-based methodology does not account for the severity of the pressure cycling at the dent. A new and innovative methodology has been developed which takes a life-cycle approach to the assessment of pipeline dents. This approach estimates the remaining life of a dent based on fatigue damage accumulation. Finite element analysis (FEA) is used to calculate various stress concentration factors (SCFs) based on the geometry of the dent. These SCFs are used to calculate an equivalent alternating stress for a unit pressure cycle event. Past representative pressure cycling data is gathered using a rainflow counting approach. The amount of damage accumulated during each pressure cycle is calculated using stress or strain based (S-N) fatigue curves; this allows for a damage rate to be calculated based on past operational history. A remaining life can be estimated based on this damage rate and an estimation of the initial fatigue damage accumulated during formation of the dent. This estimation is made based on previous elastic-plastic FEA of various scenarios which simulate the formation and shakedown of a pipeline dent. Case studies which explore the use of different assessment methods to analyze dents will be presented. A comparison of different assessment methodologies will illustrate the improved understanding of the probability of failure of dents based upon the life-cycle assessment.

Corrosion growth rate (CGR) is one of the key elements in corrosion threat management. It is used to determine metal loss In-Line Inspection (ILI) re-assessment interval, identify high growth features that require excavation before the next ILI and predict probability of failure of an unmitigated feature. Overestimated CGR may result in unnecessary expenditures for excavations and remediation, while underestimated CGR could result in pipeline failure. Although there are many methods to derive CGR from successive ILIs, there is no particular approach widely accepted by industry. In this paper, several CGR methodologies used by Enbridge to manage corrosion threats on pipelines including CGR based on feature matching and signal matching are discussed and validated. CGR based on feature matching refers to the rate that is calculated based on ILI reported depths of two back-to-back ILIs. CGR based on signal matching refers to the rate that is determined based on ILI raw data review provided from the ILI vendor. Both feature level and joint level CGR have been used in corrosion threat management. A probabilistic CGR methodology based on Pipeline Research Council International (PRCI)(1) EC-1-2 project (Development of detailed procedures for comparing successive ILI runs to establish corrosion growth rate) that accounts for uncertainty of measurement tools is also included in this study. The ILI data and field non-destructive evaluation (NDE) results of six onshore pipelines are used for this study. Each of the six pipelines has at least three high resolution Magnetic Flux Leakage (MFL) inspections. The various CGRs are established based on the 1st and 2nd inspections. The CGR methodologies are validated based on comparing the predicted depth using CGR with the field measured depth as well as the depth reported by the 3rd ILI. Over 700 field measured feature depths are used in this study and over 60,000 ILI features are used to validate the estimated depth at the time of field measurements and at the time of the 3rd ILI. This paper demonstrates the validation results of feature level and joint level CGR based on both feature matching and signal matching. The probabilistic CGR methods are also validated using the same method. The CGR validation study enables pipeline operators to establish defect repair schedules and re-inspection intervals with increased confidence.

In-line inspection (ILI) by Magnetic Flux (MFL) Intelligent Pigging of pipelines is perhaps the most convenient method to assess the internal condition of an operating pipeline. Even though ILI pigging data provides a sound assessment of the internal corrosion status of the pipeline, it may miss some indications which may lead to failures even after conducting the ILI assessment. Hence judicious interpretation of the ILI data is needed. This paper highlights the findings of an investigation of a ruptured high-pressure gas line which failed in a region, where the ILI data had not shown indications of any significant corrosion damage or reduction in thickness. The possible reasons for the discrepancy in the ILI data are discussed.

For many years, pipeline safety regulations in the US have defined prescriptive minimum requirements for integrity management combined with a clear expectation that operators should do more than the minimum where appropriate. The regulations have also provided operators with the flexibility to take a performance based integrity management approach leveraging as much information available to manage threats effectively. One the threats that must be managed is Selective Seam Weld Corrosion (SSWC). SSWC is an environmentally assisted mechanism in which there is increased degree of metal loss in the longitudinal weld in comparison to the surrounding pipe body. An appropriate definition is linear corrosion that is deeper in the longitudinal weld zone than the surrounding pipe body. In some cases, the surrounding pipe body may have limited or no corrosion present, and in other cases the pipe body corrosion may have occurred but at a slower rate than the local corrosion in the longitudinal weld zone. Conventional responses to potential or identified threats focus on in-situ investigations, often resulting in expensive and un-planned repairs for features reported by In-line Inspection (ILI) that when assessed properly demonstrate a remnant life well into the next inspection interval. When ILI identifies metal loss indications co-located with the longitudinal seam weld, the current prescribed response is often a blanket call for remediation. Such a response may not be appropriate if an ILI system is deployed to discriminate feature types and integrity assessment is exercised leveraging a sound understanding of the pipe’s material properties. This paper describes an approach that can be taken to manage the threat of SSWC. The foundation of the approach is deployment of an appropriate ILI system incorporating an effective ILI technology, an optimized evaluation process considering the specific threat morphology, material testing and a structured dig program. The evaluation process uses the ILI data and data from the field in combination material properties data and a susceptibility analysis to classify anomalies as “Likely”, “Possible” and “Unlikely” SSWC. This is aligned with the guidance in API RP 1176 “Assessment and Management of Cracking in Pipelines” for defining an appropriate response to ILI calls. Approaching the management of SSWC in this way allows operators to define a structured response for excavation activities to verify the process and remediate features as required. By using likelihood classification the risk to pipeline integrity can be reduced by acting on the most likely SSWC features as a priority, whilst collecting the data needed to make informed decisions on where to focus resources and efforts on what is a very complicated and difficult to manage threat. The output form this work, including a future plan for managing the remaining metal loss features, can be documented in a procedure and incorporated into an existing Integrity Management Plan.

In-line inspection (ILI) data is commonly used in corrosion growth models (CGMs) to predict the corrosion growth in energy pipelines. A hierarchical stochastic corrosion growth model is considered in this paper which considers the variations in the corrosion growth, both spatially and temporally, the inherent measurement error of the ILI tools as well as the model uncertainties. These uncertainties are represented as unknown model variables and are often inferred using a Bayesian method [1], [2] and samples of the unknown parameters’ posterior probability density functions (PDFs) are obtained using Markov Chain Monte Carlo (MCMC) sampling techniques [3]. ILIs can result in massive data sets. In order for MCMC-based inference techniques to yield reasonably accurate results, many samples (approaching infinity) are required. This fact in addition to the massive data sets exponentially increases the scale of the inference problem from an attainable solution to a potentially impossible one that is limited by today’s computing power. For this reason, MCMC-based inference techniques can become inefficient in the cases where ILI datasets are large. The objective is to propose variational inference (VI) as an alternative to MCMC to determine a Bayesian solution for the unknown parameters in complex stochastic CGMs. VI produces approximations of the posterior PDFs by treating the inference as an optimization problem. Variational inference emerged from machine learning for Bayesian inference of large data sets; therefore, it is an appropriate tool to use in the analysis of mass pipeline inspection data[4]–[7]. This paper introduces VI to solve the inference problem and provide a solution for a hierarchical stochastic CGM to describe the defect-specific corrosion growth experienced in pipelines based on excessively large ILI datasets. To gauge the accuracy of the VI implementation in the model, the results are compared to a set of values generated using a stochastic gamma process that represents the corrosion growth process experienced by the pipe.

Pipeline integrity management requires data integration to achieve the best assessment of pipeline condition. In the field of in-line inspection (ILI), data integration can be achieved correlating and merging the analysis results of data sets collected by tools (smart pigs) equipped with diverse non-destructive inspection technologies. Combined analysis of ILI data presents advantages in addressing the characterization, evaluation, and sizing of pipeline anomalies. The anomalies can then be related more readily to the threats that are known to affect pipeline integrity and a comprehensive integrity management program can be designed and implemented. The combined ILI data analysis approach has shown merit in situations were the integrity is compromised by anomalies that exhibit the simultaneous occurrence of two or more pipeline threats. This paper presents a case history that shows how the analysis of data from diverse ILI tools can better define and assess pipeline anomalies. The case focuses on the evaluation of selective internal metal loss affecting the heat affected zone (HAZ) of an older vintage electric-resistance welded (ERW) pipeline and its implications on integrity management.

This study collected and analyzed field data to validate the probabilistic model developed previously for predicting internal corrosion threats resulting from condensed water from nominally dry natural gas. Model-predicted wall loss was compared to actual measurement to validate the model. One past failure case from the National Transportation Safety Board(1) due to internal corrosion and in-line inspection (ILI) data from five pipeline operators were collected and used to validate the Model. Two of the data sets indicate wall loss that is either evenly distributed at all locations around the circumference of the pipe or predominantly at the top of the pipe, which is in agreement with the negligible wall loss predicted by the Model. Another two sets of data show significant wall loss occurring at the bottom of the pipe, which is in agreement with non-negligible risk of internal corrosion predicted by the Model. The 5th data set confirmed the analysis of ILI data uncertainty and corroborated the validation results. For the failure case, the prediction is in good agreement with the post-failure examination. Although uncertainties exist in both Model predictions and ILI measurements, the wall loss predictions from the Model are in good agreement with trends seen in the ILI measurements.

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.

Corrosion is still one of the major threats to the integrity of onshore and offshore pipelines. Realistic corrosion growth rates are essential inputs to safe and effective pipeline integrity management decisions. For example, corrosion rates are needed to predict pipeline reliability as a function of time, to identify the need for and timing of field investigations and/or repairs and to determine optimum re-inspection intervals to name just a few applications. The consequences associated with using wrong corrosion growth rates range from the inefficient use of resources (time, people and money) on unnecessary repairs and/or inspections to unexpected pipeline releases. The identification of where corrosion is active on a pipeline and how fast it is growing is a complex process which is understood in the general sense but is highly variable. Corrosion is therefore difficult to predict due to the very localised nature of its behaviour and the many parameters that influence the corrosion reaction. Running an in-line inspection (ILI) tool in a pipeline identifies the internal and/or external corrosion located along the full length of the pipeline. The ILI inspection also determines the depth, length and width measurements for each corrosion site and for the overall feature. The use of repeat ILI data to match and compare metal loss sites in order to estimate the corrosion growth rates at individual defects along a pipeline is a well-used and established practice in the industry. The use of such corrosion rates to make predictions of the future integrity of a pipeline started in earnest approximately 5 to 10 years ago and over that time considerable experience has been gained. Now that we are starting to collect 3, 4 or even 5 or more ILI data sets for the same pipelines we are able to test and validate our earlier ILI based growth rate predictions versus what actually occurred in the pipeline over time. With the benefit of this hindsight, the methodologies employed for evaluating and applying ILI based corrosion rates can be further improved and refined to give more accurate predictions of the future pipeline condition, the response schedule and for setting the timing of re-inspections. This paper shares the experience gained and the improvements that can be made to the determination of corrosion rates and application of these rates in a pipeline integrity assessment. These topics are illustrated and investigated via the use of case studies on real ILI data sets.