bluuebunny/crossref_metadata_2025 huggingface dataset:
Engineering Assessment of Substandard In-Line Isolation Split Fitting
Authors: Zhang Ken , Chune Ron , Cox Frank , Denowh Chantz , Alexander Chris
Published: December 2022

In-line isolation fittings used in TC Energy systems typically include a split tee assembly, flange, completion plug and bolt-up hardware, and often form part of an in-line isolation train plugging system during maintenance projects. Due to potential concerns about a few large-size split tee flanges not meeting required strength and toughness properties, TC Energy undertook a fitness-for-service assessment. This assessment involved physical proof testing coupled with high-fidelity continuum finite element analysis (FEA) and engineering critical assessment (ECA), accounting for material deficiencies and hypothetical fracture indications.

Proof testing in excess of 2 times (2 x) maximum allowable pressure (MOP) at a temperature of −46°C was performed, which involved introducing strategically sized, positioned and oriented electrical discharge machining (EDM) notches to approximate hypothetical crack-like flaws. Both the analysis and full-scale pressure proof testing demonstrated that the flanges did not experience any form of crack growth, though mechanical destructive testing showed that at certain locations the Charpy V-notch (CVN) toughness was as low as 3 J. Analysis predicted high plastic strain, which was confirmed in the full-scale testing without causing plastic collapse, burst or propagation of the preexisting flaw introduced by EDM. Test results showed reasonable consistency with the analytical results, supporting the appropriateness of applying the proposed analytical approach to location-specific assessment for current and potential similar future issues.

Based on the assessment, confidence has been gained that for split tee flanges with low toughness and low tensile strength. The testing validated analytical approach adopting sound design parameters with installation specific design loading is reasonable to determine fitness for service. The engineering assessment methods, testing setup, results and findings are summarized in this paper.
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

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

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

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

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

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

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

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

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

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

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

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.
Overcoming Challenges for Quantitative Risk Modeling Using Machine Learned Data Correlations and Predictive Modeling
Authors: Biagiotti Stephen F. , Williams Dan , Kondratyuk Sergiy , Johnson Brett
Published: December 2022

After 20 years of learnings and successful risk reduction, the pipeline industry is striving to achieve the next step change in risk performance by migrating from relative index-based risk models toward probabilistic approaches across all threats and all pipeline segments for system-wide risk assessment. While the quantification of pipeline risk can be readily supported by in-line inspection (ILI) results coupled with probabilistic limit state modeling for certain threats and pipeline segments where this information is available, where such data is lacking for other pipeline segments or threats, it is necessary to apply a meaningful methodology to establish the probability of failure for all dynamic segments used to quantify risk.

The process for establishing probability-based threat assessments for these other segments involves several stages: (1) identify correlations based on ILI historical results (i.e., create the “training” dataset); (2) leverage classification trees to identify statistically relevant data observations of key variables; (3) apply machine learning techniques to develop probabilistic and/or causal models that predict target outcomes from the combinations of key variables; and, (4) apply the relationships established in stages 1, 2 and 3 to all assets lacking ILI results. Although seemingly straightforward, several challenges exist for achieving seamless implementation.

This paper will review each process step and provide guidance on preparing and tackling common data intelligence challenges. The paper will also propose strategies for classifying assets, other than indexing, for application in those situations where machine learning does not indicate statistically significant variable correlations or yield strong predictions of target outcomes. Although still early in the understanding of migrating from relative index-based to probabilistic risk algorithms, the value provided in this paper is the sharing of lessons learned regarding “how” to gather the “evidence” necessary to identify statistical dependencies, how to apply data confidence metrics within the decision process, the challenges in data preparation/QC and interpretation techniques, and suggestions for determining the necessary limitations that should be applied to the outcomes toward the objective of quantifying the risk.
Dynamic Stress Analysis of Multi-Section Curved Pipes Subjected to a Moving ILI Tool
Authors: Mostaghimi Hamid , Rezvani Sina , Chune Ronald , Hugo Ron , Park Simon S.
Published: December 2022

Pipelines are susceptible to degradation over time due to different types of defects caused by environmental and loading conditions. In-line inspection (ILI) is an assessment method widely used for pipeline degradation monitoring. The passage of an ILI tool through a section of a pipeline can generate significant dynamic stress within the pipe. Pipelines can pass through water, muskeg, excavated, or free-span sections, which provide less support. These partially-supported pipe segments are more prone to dynamic stress with the passage of an ILI tool. This research aims to study the effects of passing an ILI tool through multiple pipe bends in series constituted of both straight and curved segments. The passage of an ILI tool can excite a pipeline close to its natural frequencies where the amplitude of vibrations and consequently the dynamic stress increase rapidly. A 3D finite element (FE) model based on the Timoshenko beam theory is developed to model curved pipes subject to the passage of an ILI tool. Lab-scale experiments are performed to verify the results of the developed FE model. The developed model is further verified using finite element analysis (FEA) performed in ABAQUS™ Implicit. A comparison of the simulation and experimental results shows that the proposed model predicts the dynamic stress and displacements of multi-section pipe segments during the passage of an ILI tool effectively and accurately.
Reliability Model to Predict Likelihood of Failure of Facility Auxiliary Piping Assets
Authors: Haider Syed , Sen Millan , Lawrence Doug
Published: December 2022

There is demonstrated potential for failures to occur on station piping assets in facilities, therefore it is prudent to take measures to manage preventable releases. In 2018, Enbridge developed a reliability model that uses readily available asset information to quantify the likelihood of failure of station piping assets. With successful implementation of the station piping model, and a follow-on flange reliability model, Enbridge realized opportunity to develop a much-needed auxiliary piping reliability model.

Auxiliary piping assets may be defined as piping systems connected to the main process flow piping which provide specific operational functionality (instrumentation, drains, pressure relief pathways, etc.) but are not part of the normal process flow path. These assets may contain components such as flanges, valves, fittings, threaded or welded connections, fittings, small diameter piping/tubing, or flex hoses.

Historical leak data indicates that auxiliary piping assets experience a higher leak frequency than other assets in a facility.

While there are industry guidelines that provide description of the individual components of auxiliary piping assets, there are few that discuss integrity management of auxiliary connections as a unit once they are operational. Previously published auxiliary piping models require inputs which are not readily available, e.g., condition of threads, welds, internal corrosion, etc. which often require on-site inspection. For pipeline operators, field data gathering is costly and challenging given stations are spread out over numerous km’s.

Given the high number of auxiliary piping assets and their potentially wide variation in parameters, there is benefit in developing a reliability-based model to support the planning phase of a risk-based integrity management program. A reliability model that works in two stages has been developed for this purpose. The pre-inspection assessment (first) stage is designed to prioritize groups of auxiliary piping assets on a station piping segment for inspection, and the post-inspection assessment (second) stage is then applied to select the specific assets that require maintenance action.

The model development considered industry guidelines, historical failure data, and subject matter experts’ inputs. This paper will discuss the design concepts, model architectures, the contributing factors, and their sensitivities to the likelihood of failure results. Through this paper, the auxiliary piping model may be applied by any operator managing such assets.
Self-Shielded Flux-Cored Arc Welding - Practical Approaches for Improved Performance of Girth Welds in High-Strength Steel Pipelines
Authors: Phillips Sam , Quintana Marie , Jia Dan , Wang Jiawei , Wang Yong-Yi
Published: December 2022

Self-shielded flux-cored arc welding (FCAW-S) has been widely used in some parts of the world for mainline and tie-in welding of large diameter pipelines for many years. FCAW-S offers high deposition rates and requires less equipment than gas-shielded wire processes. Consequently, FCAW-S is an attractive option for girth welding under difficult field conditions (e.g., windy, remote and/or challenging terrain) and is under consideration as an alternative to shielded metal arc welding (SMAW) with cellulosic electrodes due to several well-documented girth weld failures of predominantly newly constructed X70 pipelines in North America. While FCAW-S is not likely to produce weld properties as robust as is possible with mechanized gas metal arc welding (GMAW, GMAW-P) or gas-shielded flux-cored arc welding (FCAW-G), it is evident that FCAW-S offers higher performance and productivity than SMAW with cellulosic electrodes.

This paper provides a comprehensive update of key considerations and drivers that influence FCAW-S weld toughness, specifically the causes of seemingly large variation in CVN energy through the transition region. CVN transition curves were developed for seven E81T8 FCAW-S welds fabricated under a range of welding conditions represented by heat inputs ranging from 0.6 to 1.7 kJ/mm. Toughness performance, measured as mid-transition temperature was considered in the context of fracture morphology below the CVN notches and metallographic examination of notch locations in transverse and longitudinal weld sections. Analysis indicates that toughness improves when welding practices are employed that maximize the amount of reheated weld metal at the Charpy notch location. Comparison of CVN performance with E8010 cellulosic SMAW under welding conditions typical for pipeline girth welds, the FCAW-S exhibits higher upper shelf energies and lower mid-transition temperatures.

The FCAW-S and cellulosic SMAW were also compared relative to HAZ softening and weld strength overmatching using the data from microhardness maps. HAZ results indicate the FCAW-S will produce a narrower softened zone than the SMAW for a given steel at a comparable heat input. Weld metal results indicate greater potential for overmatching an X70 specified strength range with FCAW-S.

The outcome indicates that FCAW-S can be a viable option for girth welding of high strength pipelines provided certain process controls and welding practices are implemented. Recommendations for improving performance are provided for consideration.
Methodologies for Conversion of New Natural Gas Pipelines to Hydrogen Service in North America
Authors: Olsen Kristian , Shulhan Guy , Di Zanno Pietro
Published: December 2022

There is an increased interest in the production and use of hydrogen by various industries (utilities, refining, petrochemical, transportation) over the last three years. Applications run from monetizing the molecule for its chemical as well as for its heating and combustion properties, in boilers and fuel cells.

As hydrogen projects begin to scale-up across the world, the best distribution solution is via pipelines, even within present natural gas distribution and transmission pipeline systems. A project’s upper hydrogen blending limit will be dictated by the natural gas pipeline owner’s excess driver, compression, and pipeline flow capacities. In this paper we consider a project whereby a greenfield 100% natural gas pipeline would be designed for future conversion to blended natural gas - hydrogen operation and possibly even to 100% hydrogen use. Material selection would have to meet stringent guidelines, including dual certification to ASME B31.8 Gas Transmission and Piping Systems / ASME B31.12 Hydrogen Piping and Pipelines in the US and CSA Z662 Oil and Gas Pipelines / ASME B31.12 in Canada. In addition, the integrity management strategy should be tailored to provide sufficient information for an engineering assessment that maximizes the chances of regulatory acceptance with minimal effort. In this paper, we outline anticipated advisory services for regulatory, engineering, construction support, and integrity management to increase the validity of the service conversion application. Cost differences between natural gas only and hydrogen-ready infrastructure are summarized, including strategies for low-stress design, selection of pipe material grade and wall thickness, and metallurgical considerations for welding, fracture toughness, hydrogen embrittlement and fatigue life management. Finally, additional cost considerations are explored regarding integrity management program development costs, environment and engineering permitting, and consultation.
Reframing PSMS in the Context of Operational Risk Management and ESG Sustainability
Authors: Marshall Michael , Fingerhut Martin
Published: December 2022

Current Environmental, Health, and Safety (EHS) and Asset Integrity/Performance Management (AIM/APM) platforms fail to properly aggregate data from multiple Pipeline Safety Management System (PSMS) workflows into a single database which can be queried to inform real-time, risk-based analyses and decision-making relative to profitability impacts.

With predictive analytics at the core of an asset integrity and PSMS framework intent upon achieving holistic, enterprise-wide visibility and accountability for Environmental, Social, and Governance (ESG) program effectiveness and sustainability, this paper proposes an ideal solution designed around the following core functions:

• Reframing EHS and AIM/APM in the context of Operational Risk Management (ORM) by normalizing data relative to performance and process-related parameters

• Categorizing, prioritizing, and risk ranking incidents by economic impact (specifically lost production), enabling problem solving teams to resolve high value deep-dive systemic problems

• Satisfying the need for a one-stop system that has the highly interlinked EHS, compliance and enterprise risk management systems all in one framework

• Linking to data historians like OSIsoft PI to “give voice to equipment” for predictive analytics as necessitated by today’s digital transformation movement

• Featuring incident investigation, reporting and failure modes decision support functionality based on industry best practice standards including API 754 Process Safety Performance Indicators for Refining and Petrochemical Industries, which is also directly applicable to petroleum pipeline industry operating systems and processes where loss of containment may occur

• Offering a highly configurable user interface for key performance indicator (KPI) trending, reporting, alerts/notifications, action planning and follow-up
Potential and kinetic energy of underwater noise measured below a passing ship and response to sub-bottom layering
Authors: Dahl Peter H. , Dall'Osto David R.
Published: December 2022

Observed near the seafloor, broadband noise emissions from a vessel passing directly above exhibit frequency bands where potential acoustic energy is greater than kinetic energy while the opposite occurs in neighboring frequency bands. The condition where the dynamic and kinematic energy forms differ in this manner is characteristic to interference involving steep angles or near-normal incidence reflection from the seafloor. Measurements are made at two experimental sites using a research vessel passing above a vector sensor, positioned ∼1.5 m above the seabed, resulting in a vessel horizontal range approaching ∼0. The data are expressed as a ratio of kinetic to potential energy in decibels and yield information on seabed properties. A model for kinetic and potential energy is developed from the method of images using a layered seabed and is used to invert data collected in Puget Sound. A higher-impedance seabed is identified via inversion, which is consistent with the thin Holocene sediments in the region. For data collected on the New England Mud Patch, the model is instead applied directly to nominal seabed parameters originating from prior studies that identify a low-speed mud layer atop a higher-speed transition layer separating the mud substrate from a sediment basement.
An Evaluation of Participant Perspectives and Wear-Time Compliance for a Wrist-Worn Versus Thigh-Worn Accelerometer in Cancer Survivors
Authors: Hidde Mary C. , Crisafio Mary E. , Gomes Emma , Lyden Kate , Leach Heather J.
Published: December 2022

Background: Accelerometers are frequently used to measure free-living physical activity and sleep in cancer survivors. To obtain valid data, participants must adhere to wear-time guidelines; therefore, understanding survivor’s preference may be critical when selecting an accelerometer. This study compared cancer survivors’ reported discomfort and interference, and wear-time compliance between a wrist-worn accelerometer and a thigh-worn accelerometer. Methods: This was a secondary data analysis. Cancer survivors (N = 52, mean age = 51.8 [13.0], 82.3% female) wore the Actiwatch-2 (wrist) and the activPAL (thigh) for 7 days, 24 hours per day. On day 7, participants completed a questionnaire to evaluate each accelerometer using a 1 to 5 Likert scale and open-ended questions. The Kolmogorov–Smirnov test evaluated differences in discomfort and interference. Paired samples t test evaluated differences in wear-time compliance. Open-ended responses were analyzed using thematic analysis methods. Results: No differences were observed in discomfort, interference, or wear-time compliance (P = .08). Qualitative analysis resulted in 2 themes: discomfort and ease of use and interference and adverse reaction. Interferences were primarily reported with the Actiwatch-2, whereas discomfort and ease were primarily reported with the activPAL. Conclusion: No significant differences were observed regarding discomfort, interference, and compliance. Results of this study can prepare researchers for common issues regarding accelerometer compliance, allowing researchers to offer resources to alleviate discomforts or interferences that may affect wear-time compliance.
Intraocular pressure increases the rate of macular vessel density loss in glaucoma
Authors: Mahmoudinezhad Golnoush , Moghimi Sasan , Nishida Takashi , Micheletti Eleonora , Du Kelvin H , Mohammadzadeh Vahid , Wu Jo-Hsuan , Kamalipour Alireza , Weinreb Robert N
Published: December 2022

Background/aims

To evaluate the relationship over time between intraocular pressure (IOP) and the rate of macula whole image vessel density (wiVD) loss and whole image ganglion cell complex (wiGCC) thinning in glaucoma

Methods

From 62 patients in the Diagnostic Innovations in Glaucoma Study, 59 Primary open-angle glaucoma and 27 glaucoma suspect eyes with mean follow-up of 3.2 years were followed. Optical coherence tomography angiography (OCT-A)-based vessel density and OCT-based structural thickness of the same 6×6 mm GCC scan slab were evaluated. Univariable and multivariable linear mixed models were performed for all eyes and also a subset of them in which peak IOP <18 mm Hg to investigate the effect of IOP parameters on the rate of wiVD and wiGCC change.

Results

The mean baseline visual field mean deviation (95% CI) was −3.3 dB (−4.4 to –2.1). Higher mean IOP (−0.07%/year per 1 mm Hg (−0.14 to –0.01), p=0.033), peak IOP (−0.07%/year per 1 mm Hg (−0.13 to –0.02), p=0.004) and IOP fluctuation (IOP SD) (−0.17%/year per 1 mm Hg (−0.32 to 0.02), p=0.026) were associated with faster macular vessel density loss. Faster wiGCC thinning was associated with higher mean IOP (−0.05 µm/year per 1 mm Hg (−0.10 to –0.01), p=0.015), peak IOP (−0.05 µm/year per 1 mm Hg (−0.08 to –0.02), p=0.003) and IOP fluctuation (−0.12 µm/year per 1 mm Hg (−0.22 to –0.01), p=0.032). In eyes with peak <18 mm Hg, faster wiVD progression was associated with higher mean IOP (p=0.042). Faster wiGCC progression was associated with higher mean IOP in these eyes (p=0.025).

Conclusion

IOP metrics were associated with faster rates of overall macular microvascular loss and also in the eyes with peak IOP <18 mm Hg. Future studies are needed to examine whether additional IOP lowering reduces the rate of microvascular loss in patients with glaucoma.

Trial registration number

NCT00221897.
Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study
Authors: Osawa Itsuki , Goto Tadahiro , Tabuchi Takahiro , Koga Hayami K , Tsugawa Yusuke
Published: December 2022

Objective

To investigate determining factors of happiness during the COVID-19 pandemic.

Design

Observational study.

Setting

Large online surveys in Japan before and during the COVID-19 pandemic.

Participants

A random sample of 25 482 individuals who are representatives of the Japanese population.

Main outcome measure

Self-reported happiness measured using a 10-point Likert scale, where higher scores indicated higher levels of happiness. We defined participants with ≥8 on the scale as having high levels of happiness.

Results

Among the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6–8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; p<0.001). Conversely, self-reported poor health, anxiety about future household income, psychiatric diseases except depression and feeling isolated were negatively associated with happiness (eg, aOR for self-reported poor health 0.44; 95% CI 0.39 to 0.48; p<0.001). Using machine-learning methods, we found that meaning in life and social capital (eg, having a spouse and trust in communities) were the strongest positive determinants of happiness, whereas poor health, anxiety about future household income and feeling isolated were important negative determinants of happiness. Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.

Conclusion

Using machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.