bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advancement of Probabilistic Analysis of Seam Weld Cracking Integrity Management
Authors: Ma Jing , Bagnoli Kenneth E. , Thirumalai Neeraj S. , Krynicki Joseph W. , Cater-Cyker Zachary D. , Gonzalez Gus 
Published: December 2022

Abstract
Probabilistic analysis has been increasingly used in the pipeline industry to provide insight on reliability as input to quantitative risk assessments. This paper details an overview of recent advancements in its application to seam weld cracking threat. Three major aspects are covered.
The first aspect is material property. There are limited fracture toughness data outside of Charpy V-Notch (CVN) tests available in the industry for vintage electric resistance welded (ERW) seams. A large number of testing has been conducted to better understand pre-1980 ERW pipe fracture behavior. The database includes conventional toughness tests such as 467 compact tension (CT), 20 single edge notched bend (SENB) and 106 pin-loaded single edge notched tension (SENT) specially designed to simulate the constraint condition of full scale pipe. The distributions presented in the paper characterize the fracture toughness of pre-1980 ERW pipe and provide sufficient information to represent this class of material for probabilistic analysis.
The second aspect is the use of validation spools for in-line inspection (ILI) performance evaluation. Nondestructive evaluation (NDE) data collected during excavations are a traditional source to validate the tool run. Recently, validation spools are manufactured to quantify the ILI performance by designing configurations with a variety of “synthetic” flaws having accurately known sizing information. The use of these spools enables the rapid and comprehensive assessment of ILI performance by applying API STD 1163 Level 3 analysis. It also provides the opportunity to determine probability of detection (POD) as a function of flaw size, which is critical to interpret the residual failure probability after running ILI and completing repairs.
The third aspect is to leverage the accumulated industry flaw data to infer a postulated population to characterize the condition of a pipeline without historical ILI. This approach is beneficial in a few scenarios, for instance, to prioritize assets for ILI runs, to evaluate difficult-to-pig pipelines, and to estimate the probability of failure (POF) of pipelines only subject to hydrotest mitigation. The paper provides some case study to illustrate the implementation of the process and debriefs the ongoing industry progress.
Vulnerability of Pipelines Installed by Horizontal Directional Drilling to Landslides and a Proposed Framework for Developing Preliminary No Drill Zones for Landslide Avoidance
Authors: Van Hove Joel , Barlow Pete , Duguay Max , Karimian Hamid 
Published: December 2022

Abstract
Horizontal directional drilling (HDD) is a method of trenchless pipeline installation which has been widely used in the Western Canadian Sedimentary Basin (WCSB) during the past 40 years to cross challenging terrain, including watercourses and slopes. In the case study presented, 7,952 pipeline slope crossings are considered, of which an estimated 14% are partially or fully crossed by HDD. Often the primary objective of the HDD installation at the time of construction was to cross a watercourse and adequate consideration was not always given to the possible presence of landslide terrain adjacent to the watercourse. Minimizing HDD cost often requires shallower and shorter installations, which combined with the practice of not always identifying existing landslide features resulted in an estimated 16% of HDD landslide crossings spatially intersecting landslides. Due to the increased stiffness and overburden stress of soil or bedrock with depth as well as other factors, pipeline vulnerability and hence probability of failure is significantly increased relative to shallower conventionally trenched pipelines. Within the case study inventory, the combination of historical HDD installations that did not effectively avoid landslides and the increased vulnerability of pipelines impacted by landslides at depth accounted for approximately 35% of landslide related pipeline failures within a recent 10-year period, a failure rate approximately 15 times that of conventionally trenched pipelines when adjusted for frequency of landslide intersection.
Many pipeline operators have recognized the disproportionate risk landslides pose to ineffective HDD installations and are prioritizing assessment and management accordingly. This paper proposes a screening framework to provide guidelines for evaluating the effectiveness of HDD installations avoiding landslides for both existing and planned installations.
The Why and How of Data Integration for Integrity Management Pursuant to API TR 1178
Authors: Smith Jackie , Espinoza Cesar , Collins Karen 
Published: December 2022

Abstract
Data integration is a foundational element and a regulatory requirement for pipeline operators as part of an Integrity Management (IM) program. Having taken effect in July 2020, the Pipeline Hazardous Materials and Safety Administration’s (PHMSA) new Gas Mega Rule heavily emphasizes the requirement for the validation of data and records. As a result, pipeline operators must face new challenges to ensure they are in continuous regulatory compliance. Data integration plays a major role in supporting pipeline operators and engineering consultants as they adjust to the new requirements set forth in regulations. The Integrity Management (IM) regulations require pipeline operators to include the results of the latest as well as of previous integrity evaluations, including risk assessment information based on integrated data from the entire pipeline. Leveraging this data and the analysis thereof is required when making decisions regarding the course of action to address pipeline threats.
The American Petroleum Institute (API) Technical Report (TR) 1178, “Integrity Data Management and Integration,” provides a compendium of systematic methodologies, recommendations and processes to spatially integrate and normalize integrity data to empower pipeline operators to efficiently analyze integrity-related data to support their integrity management decisions and programs. Through the execution of multiple post-ILI service projects, the significance of this TR has been realized as a cornerstone to support pipeline operators in their efforts to meet the requirements of new regulations and achieve compliance.
In the present case study, ILI results from various inspections gathered from over 179 post-ILI data integration projects for a mid-size pipeline operator located in the southern United States — totaling over 5,500 mi of pipe segments and ranging in diameter from 10″ to 24″ — were analyzed following the core processes described in API TR 1178. The datasets — ranging from hundreds to hundreds of thousands of integrity data records from different sources including a variety of in-line inspection (ILI) tool technologies and service providers — were interpreted to distinguish pipe features or references from anomalies. Subsequent analyses were applied to the integrated datasets and delivered per the pipeline operator’s specifications.
This effort greatly supported a seamless transition to achieve compliance with the new regulations and help to establish reporting requirements for technology and inspection service providers. The operator now has a complete historical ILI database. As it is fully integrated into the GIS system of record, the pipeline operator is able to transition into advanced analytics, helping them to spot trends and make sounder integrity management decisions long-term.
Bayesian Failure Rate Estimation for the Reliability and Risk Assessment of Energy Pipelines
Authors: Dann Markus , Lu Dongliang , Dooley Colin , Tayyab Hassan 
Published: December 2022

Abstract
Failure rates, which quantify the normalized likelihood of pipeline failure, are an integral part of assessing the reliability and risk of pipelines. The industry-wide trend of utilizing probabilistic methods for estimating failure rates raises the question whether the frequentist or Bayesian definition of probability is more suitable. The paper illustrates some limitations of the frequentist probability definition for pipeline risk assessment and supports the Bayesian approach for analyzing pipeline failure rates. The Bayesian quantification of probabilities leads to coherent uncertainty assessment and propagation even if evidence is combined from different sources either through a repetition of the prior-likelihood model or a multi-level / hierarchical approach that integrates all available data and information in one model. Selecting or disregarding data for estimating failure rates is no longer necessary as they all contribute to the result based on their relative uncertainties. Examples are provided in the paper to illustrate the benefits of the Bayesian probability approach.
Alternative Sampling Plans for Verification of Pipeline Material Properties: Frequentist and Bayesian Statistical Approaches
Authors: Ludlow Jed 
Published: December 2022

Abstract
The creation and retention of accurate records reflecting the makeup of a pipeline is a critical aspect of pipeline safety. In the U.S., 49 CFR 192 provides guidance on the verification of pipeline material properties for populations of like pipe, including the development of alternative sampling plans for populations that lack complete documentation. But the code provides little guidance on how to formulate such a plan and how to process the samples as they arrive.
Various statistical approaches exist for developing lower bounds on material properties based upon measurements. On one hand, frequentist (or classical) approaches are sometimes simpler to understand and implement. On the other hand, Bayesian approaches allow for the incorporation of natural prior information to improve the estimate.
Estimating the yield strength (YS) is often one of the most important objectives of an alternative sampling plan. This paper presents a study of frequentist and Bayesian statistical approaches to developing a lower bound on YS using simulations of alternative sampling plans. Various hypothetical pipe populations with different distribution assumptions, indicative of different possible manufacturing patterns at the pipe mill, are simulated then alternative sampling plans are conducted for each simulated population under both frequentist and Bayesian statistical approaches. For each approach, the resulting lower bound on YS is estimated and compared with the true population lower bound. This direct comparison is possible since full information on the simulated populations is available. The relative merits of each statistical approach are compared, and recommendations about which approaches are most suitable are provided.
Consequence Modeling of Hypothetical Releases From Carbon Dioxide (CO2) Transport Pipelines
Authors: Fontenault Jeremy , Franey Tara 
Published: December 2022

Abstract
Reducing carbon emissions is increasingly becoming a priority to combat climate change. Carbon capture, utilization, and storage (CCUS) is one of the primary approaches to help combat carbon emissions in the oil and gas and other industries. These technologies involve capturing the CO2 from combustion, refining, or other types of industrial activities, then transporting that CO2 to another location where it can be utilized or stored underground or below the sea floor. Pipelines are one of the primary transportation methods, and as more CCUS operations start to come online, more pipelines will be built or converted from transporting hydrocarbons to transporting CO2.
Like most products transported by pipeline, there are risks associated with CO2 transport. However, these risks are quite different from those of hydrocarbon transport. CO2 is not flammable and is less toxic. The primary risk associated with a release of a large quantity of CO2 is the displacement of oxygen that can cause an asphyxiation hazard. Direct exposure to cooled CO2 liquid or gas can cause irritation or even frostbite. CO2 releases into water can harmfully alter the water pH level. Due to these risks, it is necessary for operators to understand the potential consequences of an accidental loss of containment.
This paper will review an approach for consequence modeling used for the potential conversion of service from crude oil transport to CO2, for a confidential pipeline operator. This will include an overview of the modeling tools used, the inputs and assumptions incorporated, the range of hypothetical release scenarios considered (including full-bore ruptures and smaller leaks) and overview of the results. This assessment was used to answer a variety of questions asked to evaluate whether this conversion was a viable project. This included determining the potential impact area from a worst-case discharge, what receptors are at risk, and identifying optimal operational considerations (i.e. valve type and placement, leak detection requirements, etc.).
This approach for consequence modeling for CO2 pipelines can be used to help ensure safety during the coming energy transition.
An Integrated Model for Predicting Stress Corrosion Cracking of Buried Pipelines
Authors: Liu Guanlan , Ayello Francois , Vervake Gary , Beck Justin , Thodla Ramgopal , Sridhar Narasi 
Published: December 2022

Abstract
Stress corrosion cracking (SCC) is a type of crack that grows in a corrosive environment, which threatens the integrity of pipeline and may lead to major and/or sudden pipeline failures. The complex mechanism of SCC involves interactions of electrolyte chemistry, coating quality, metallurgy, stress, and other pipeline operating conditions. As a result, it is challenging to estimate the SCC failure probability at a certain location of the pipeline. Additionally, the nature of data uncertainty in the pipeline operation made a precise SCC prediction even more difficult. In this study, a Bayesian network model was developed to integrate the theoretical and empirical knowledge regarding SCC prediction. By combining the relevant parameters and varying mechanisms, both high pH SCC and near-neutral pH SCC probabilities and crack growth rate can be predicted. The initial prediction results are validated by comparing with the field SCC data. The Bayesian network SCC model can serve as a reference tool for the pipeline operators to determine the time or location of SCC inspection, repair, or replacement. The probabilistic results make it feasible to run sensitivity analysis, to determine the impact of uncertainty data.
Addressing Data Gaps for Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis
Authors: Stewart Ryan , Di Blasi Martin , Dessein Thomas 
Published: December 2022

Abstract
Performing reliability assessments for a large asset inventory of pipeline facility equipment, such as compressor station assets, requires a substantial dataset of attributes for a diverse range of equipment types. In many cases, equipment data inventories have gaps, with one or more required attributes unknown, such as diameter, wall thickness, operating pressure or material properties. The identification and collection of complete records is typically labor-intensive and time consuming, so data gaps are often filled with assumptions while ongoing data collection improves. A standard approach to fill these gaps is to use conservative assumptions for missing attributes. This results in missing data producing higher assessed risk than complete records. The benefit of this conservative approach is that it appropriately penalizes the incomplete records, driving action toward collecting the information where it matters. However, this approach is simple, does not leverage all the information available within the available dataset, and can produce a distorted representation of risk that may reduce the credibility of the risk assessment.
This paper describes a process to use unsupervised machine learning algorithms to organize large asset inventories into groups and fill data gaps with reasonable, but conservative assumptions. We used a non-hierarchical clustering method to group asset records into clusters. Instead of using the most conservative value to fill data gaps across all records, gaps are filled using the most conservative value from similar records. This method provides estimates for data gaps that are more realistic while still maintaining conservatism, striking a balance between prioritizing equipment with confirmed attributes that indicate higher risk and equipment with little information.
The approach described in this study relies on K-means clustering. We discuss the practical uses of dimensionality reduction, heuristic techniques for selecting the number of clusters, and sensitivity analysis.
Accounting for Undetected In-Line Inspection Features in the Risk Assessment of Axial Crack and Corrosion Threats
Authors: Ibrahim Mahmoud , Shahzad Khurram , Akonko Saheed , Koduru Smitha , Bott Steven 
Published: December 2022

Abstract
In-Line Inspection (ILI) tool performance is a key input parameter to the pipeline integrity reliability models. The ILI performance is measured through metrics, established in the pipeline industry, that quantify the ILI tool’s detection, sizing, and identification capabilities. The minimum expected performance values are specified by the ILI vendor based on the vendor’s internal testing. However, the actual observed ILI tool performance by individual pipeline operators may vary, and thus, it is important to include the actual tool performance in the reliability model to properly estimate the pipeline Probability of Failure (PoF).
In addition to estimating the PoF for the list of ILI detected features across the pipeline, the pipeline operator needs to estimate the PoF for features that might have not been detected by the ILI tool due to ILI performance limitations. These undetected features may pose integrity threat and should be accounted for in reliability modelling for a comprehensive risk assessment. This paper provides a practicable implementation of a methodology developed in a Pipeline Research Council International (PRCI) project for characterizing the population of undetected ILI corrosion features. The methodology estimates the density of undetected features based on a function of ILI’s probability of detection (POD), probability of identification (POI), and probability of false call (POFC) while utilizing the ILI sizing accuracy to estimate the severity of undetected features.
In the present study, the PRCI undetected feature methodology was extended to include crack ILI features and was adapted to provide an accurate quantification of ILI sizing error from linear regression using matched ILI and field-measurement data. In addition, the paper demonstrates, (1) the impact of using feature morphology specific ILI performance values compared to generic performance values, and (2) effect of feature depth and length correlation in undetected feature severity modelling. The practicable implementation will be presented through two case studies that will simulate the population of undetected features across two pipelines and estimate their PoF values along with the PoF values of detected features population.
Ranking Models for Managing the Integrity of Ageing Pipelines
Authors: Bacchi Luca , Medoro Marco , Annoni Giampaolo 
Published: December 2022

Abstract
SNAM operates more than 29,000 km of gas transmission pipelines in Italy. The pipelines have diameters up to 56″, with different MAOP up to 75 bar. The oldest ones were built in the 40s and new ones are nowadays under construction.
Italy, like most of the European countries, is characterized by a high level of anthropic presence on the territory; moreover, many pipelines cross the country from south to north, passing through the Appennini mountains, where there are many areas with geological instabilities (landslides).
Different pipelines can have different features, issues and challenges, but, for all of them, the TSO must guarantee a high level of safety in terms of structural integrity.
In such a scenario, a TSO must tackle many questions in order to properly manage the integrity of its pipelines that compose the complex asset that is the whole network: where do I need to do local actions? Is there any pipeline that is convenient to totally replace? Which ones of the existing unpiggable pipelines should I invest to make piggable? Is there a way to establish the priorities for all these actions?
SNAM built two tools to prioritize such actions on all its pipelines: the first tool is a model able to produce a ranking of all the pipelines as a function of the technical convenience for their replacement, the second tool is a model that produce a ranking of all the pipelines as a function of the technical convenience to make them piggable. Both the models provide numerical and objective tools, based on technical local data of the pipelines and of the territorial context, that can be applied at once to the whole network and that help the TSO in the strategical decision making oriented to the best management of the integrity of the ageing asset.
Reliability Assessment of Pipeline Third Party Damage
Authors: Mehranfar Mahsa , Sen Millan , Lam Christopher , Bott Steven 
Published: December 2022

Abstract
Pipeline failure statistics indicate that mechanical damage caused by third-party excavation represents the largest threat to the integrity of onshore oil and gas pipelines in North America. In 1999, PRCI developed a reliability model that quantifies the pipeline probability of failure due to the 3rd party damage threat. The model employs a fault tree approach comprised of four main elements: the probability of excavation occurring on the pipeline alignment, the effectiveness of damage preventive measures, the probability that the excavation depth exceeds the depth of cover, and the probability that the excavator force is sufficient to fully penetrate the pipe wall. The PRCI model has been implemented by numerous operating companies over the past two decades.
Despite this large contribution, there has been a gap in quantitative assessment techniques regarding the effectiveness of the methods used to prevent mechanical damage, and the pipelines resistance to the impact loads applied to pipelines by excavation equipment. In 2020 Enbridge applied this model to its 25,000+ km liquid pipeline system. During implementation numerous learnings and areas for improvement were identified. Correspondingly, the model was expanded to improve consideration of four important 3rd party damage threats that are not currently included within the model: agricultural activity, vehicle crossings, pipeline exposures, and mitigation activities. The results of this updated model showed that the probability of failure’s due to 3rd party damage were generally increased at locations with high population density, agricultural land use, and road crossings, that exhibited shallow cover. It is expected that this updated model will assist in prioritizing the mitigation of various locations that are potentially susceptible to the 3rd party damage threat in alignment with operator expectations. This paper discusses the data gathering steps required for implementation, example probability of failure results, and provides the details of the model updates which may be incorporated by other operators.