bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Previously Examined Dent Recoated With Two-Part Epoxy and Later Found With Near Neutral Stress Corrosion Cracking - What Was Missed
Authors: Wu Shanshan , Bratton Joseph P. , Polasik Steven J. , Kemp David , Padgett Barbara N.
Published: December 2024

The pipeline industry continues to strive for improvements in managing mechanical damage and to learn from case studies. A technical case study will be presented for a rock dent from the original construction of the pipeline that was excavated, constraining rock removed, examined, and recoated with two-part epoxy external coating. The recoated dent was subsequently excavated as part of a later dig program and the field non-destructive testing (NDE) discovered the dent had developed external cracking through near-neutral pH stress corrosion cracking (NNpH SCC) while in service.

This paper aims to share the learnings from metallurgical analysis, finite element analysis, and a high-level root cause analysis and will cover the findings and contributing factors that allowed for NNpH SCC to occur on a dent externally coated with two-part epoxy and how a liquid operator is using the findings in its integrity management program when assessing dents.
Multiscale Multidisciplinary Machine Learning Modeling for Pipeline Risk Assessment
Authors: Liu Guanlan , Ayello Francois , Shabarchin Oleg , Langlois-Rahme Gabriel , Lu Dongliang
Published: December 2024

Ensuring the safety and reliability of pipeline systems across diverse components, materials and operating conditions necessitates the integration of information from various fields of expertise. Machine learning can be a powerful tool to analyze multiscale and multidisciplinary data, enhancing our ability to make informed decisions and manage pipeline risks effectively. Also, machine learning technology, when applied to pipeline risk assessment, can be used to organize valuable pipeline information, and uncover hidden correlations. However, machine learning faces challenges like overfitting and identifying false correlations without a real causal impact. Understanding the causes of pipeline failures is essential for taking appropriate actions. Since data can be imperfect with measurement error, a probabilistic risk prediction model is necessary to incorporate uncertainty considerations. This publication uses a previously published Bayesian network model for Stress Corrosion Cracking (SCC) pipeline risk assessment to demonstrate how Bayesian networks can be used to aggregate data at multiscale and from multidisciplinary factors. The model is reviewed with field data to share knowledge with pipeline engineers using machine learning for pipeline risk assessment.
Bank Erosion and Encroachment Model Development to Enhance Pipeline Integrity Management
Authors: Wlodarczyk Kyle , Davidson Sarah , Van Hove Harmen , Song Peter , Porter Dean
Published: December 2024

Bank erosion can expose pipelines at watercourse crossings or in floodplains at encroachment sites. Once a pipeline is exposed, failure can occur due to water loading, debris loading, excessive strain, or vortex-induced vibration (VIV). Empirical engineering formulae have been developed to predict scour depths in rivers and can be used at pipeline watercourse crossings to estimate the annual probability of pipeline exposure from scour. Vulnerability algorithms can then be used to estimate the annual probability of pipeline failure at watercourse crossings, which most commonly results from VIV. However, bank erosion has generally proven to be complex and time-consuming to assess, and it is therefore often considered at the site scale rather than at a screening level in system-wide assessments of hydrotechnical hazards.

This paper describes a novel bank erosion model developed and validated using morphological characteristics and historical aerial imagery from over 240 detailed assessments of pipeline watercourse crossings and encroachment sites across North America. The model predicts the rate of bank erosion based on channel and floodplain morphology, as well as other site characteristics such as debris presence where data exists. Based on these characteristics, as well as the actual rate of observed erosion where possible, a time to pipeline exposure is estimated at watercourse crossings and encroachment sites. The estimated time to pipeline exposure and bank protection type is then used to assign an annual probability of pipeline exposure.

This paper also addresses a gap in the current practice by providing a framework for assessing vulnerability at encroachment sites where flow is parallel or sub-parallel to the pipeline and VIV is unlikely to occur. The probability of pipeline failure at both watercourse crossings and encroachment sites can then be assessed by considering site-specific characteristics, such as the potential free span length, pipe properties, and pipeline operating conditions. As the models use morphological characteristics observable on the ground or in a desktop review and known pipeline data, the results can be used to prioritize actions (e.g., ground inspections or mitigation) at watercourse crossings and encroachment sites and to guide geohazard management strategies to enhance pipeline integrity management.
Getting to the Bottom of the Groove: NDE of SSWC
Authors: Futch David , Newton Christopher
Published: December 2024

Selective seam weld corrosion presents a set of unique circumstances that results in difficulty in accurately determining the groove depth. Classification and measurement of in-the-ditch selective seam weld corrosion (SSWC) has proved difficult due to many factors. On the classification side, lack of fusion (LOF) and SSWC often look similar and distinguishing between the two can prove challenging. Visual difficulties can then result in an incorrect classification which may affect the determined remaining life or repair criteria. On the sizing side, narrow SSWC may prevent sizing by pit gauges and surrounding corrosion may prevent phased array UT from measuring the depth. Surrounding external corrosion creates a difficult surface to land the NDE equipment and tight features prevent the use of hand tools, such as pit gages from reaching the bottom of the groove. Lack of a defined procedure can lead to a varying level of success when inspecting these features. Accurate depth measurements are critical when determining a fitness for service, remaining life, or even the appropriate repair method.

This paper focuses on the main task of a recent Pipeline Research Council International project, PRCI NDE 2-15, whereby the goal was to develop in-the-ditch evaluation methods and necessary tools to address difficulties associated with determining SSWC depth. Through a series of targeted NDE, a specialized NDE procedure was developed to identify external SSWC verses other defects such as lack of fusion or seam trim and measure external SSWC depth and length. In some cases, this NDE procedure combines multiple NDE techniques, each building on each other to determine the grooving depth. Finally, this protocol was validated through metallurgical examination.

This paper provides a first of its kind to provide the pipeline industry with guidance when attempting to inspect and properly classify SSWC. This paper provides the first step to gaining a better understanding of SSWC inspection.
Solving the Non-Linear ILI Sizing Challenge of Crack-Like Flaws With Bayesian Logic
Authors: Krynicki Joseph W. , Skow Jason , Gonzalez Gustavo , Dessein Thomas , Peng Lujian
Published: December 2024

The sizing performance of in-line inspection (ILI) tools are commonly illustrated on unity plots relating the ILI-reported size to a validation measurement, typically a non-destructive evaluation (NDE) field-measured height obtained using techniques expected to be more accurate than the ILI. The comparison of ILI and NDE field measurements is simplified as a linear relationship and tolerance interval. While the slope for a perfectly performing system is 1, it is rarely 1 in practice.

Pipeline operators have continuously strived for better ILI sizing performance to optimize maintenance cost and prioritize repairs. API 1163 has comprehensively addressed sizing performance with the incorporation of Level 3 (Bayesian Approach) that enables pipeline operators to bring new analysis techniques to validate performance, but the linearity of the model still presents challenges because it assumes the performance is consistent across all flaw sizes.

The challenges associated with a linear model are especially problematic for thin-wall pipelines due to height sizing errors. Height tends to be the most influential parameter for the fitness-for-service acceptance criteria of crack-like flaws.

In this paper, a non linear sizing model that uses Bayesian logic is described with the intention of enhancing ILI validation analysis and for future inclusion and interpretation by the API 1163 technical committee. A case study of a population of crack-like flaws is used to illustrate the non-linear sizing performance in ILI results and explain the causes for common sizing errors. While this paper focused on crack-like flaws, the non-linearity of ILI sizing performance is also applicable to metal loss ILI results.
Fault Diagnosis of a Crude Oil Pre-Treatment Systems Based on DOOBNs
Authors: Liu Daqian , Song Shangfei , Shen Siheng , Kang Qi , Li Xiaoping , Gong Jing
Published: December 2024

The crude oil pre-treatment system plays a crucial role in the domain of oil and gas gathering and transportation, serving as a linchpin in ensuring the efficient and secure flow of petroleum resources. Its main function is to process the wellhead production, and complete the dehydration and degassing of the production, bringing it up to the standards required for subsequent processes. The crude oil pre-treatment system is intricate, and its fault could potentially result in significant economic losses and catastrophic consequences. Therefore, the precise and expeditious identification of both the location and the reasons for the occurrence of faults within the crude oil pre-treatment system is of paramount importance. To address this imperative, a fault diagnosis model based on dynamic object-oriented Bayesian networks is designed to conduct fault attribution analysis on the system. The model is mainly composed of the static Bayesian networks and the Markov model. The static Bayesian network consists of Fault Layer, Fault Symptom Layer, and External Variable Layer, used to identify the causes of system faults. The Markov model is employed to determine the temporal evolution of the system’s states. In this study, static Bayesian networks is utilized not only to diagnose individual faults but also to successfully identify the causes of concurrent faults. Then, static Bayesian networks is extended in the time domain by 20 iterations. By inputting fault symptoms into the first static model, predictions of the system’s state are achieved, allowing the time of system maintenance completion to be determined. By inputting fault symptoms into the twentieth static model, a retrospective analysis of the system state is conducted, thereby determining the initiation time of the fault. The research indicates that the model’s results maintain a high level of consistency with on-site fault logs, providing crucial support for on-site production. Furthermore, this also validates the effectiveness, practicality, and accuracy of the model.
Simulation and Optimization of Gathering Pipeline Utilizing Electric Tracing at the Pipeline Center
Authors: Li Zhuochao , Xue Jiaqi , Xu Ning , Yan Fengyuan , Guo Yi , Pan Shiyuan , Liang Yongtu , Xie Zhengfang
Published: December 2024

Electric Tracing Technology at the Pipeline Center is widely used in the development of peripheral oil fields. However, the problems faced include low simulation accuracy, high equipment investment, and high energy consumption for operation. Initially, we analyzed the friction and heating effects caused by the insertion of electric cables into the Pipeline Center. This analysis was based on the laws of mass, momentum, and energy conservation. Consequently, we established a model for calculating the steady-state temperature and pressure of the gathering pipeline, which utilizes electric tracing at the pipeline center. This model enables accurate computation of temperature and pressure along the pipeline at different power. Subsequently, we developed a Mixed Integer Nonlinear Programming model with the objective of minimizing the sum of investment and operational costs. This model considers constraints such as flow balance, hydrothermal performance, power, and the length of the electrical tracing line. It was solved using a genetic algorithm. The solution comprises the design scheme of electric tracing equipment and operational plans, such as the heating method (point heating or electric tracing), heating power, and temperature. This approach achieves the optimization of the electric tracing gathering pipelines (ETGP). Taking a certain oilfield in Northeast China as an example, the simulation model’s calculated results for temperature and pressure are compared with field data, with relative errors all within 10%. The optimization results show a reduction in the length of electric tracing lines by 13.8% and a decrease in total power by 45.7%. By heating the oil to the minimum transportable temperature and then utilizing the electrical tracing technology to maintain this temperature, we minimize energy consumption during transportation.
A Flow State Estimation Framework Based on a Novel Natural Gas Pipeline Network Transient Simulation Method
Authors: Jiao Jianfeng , Xu Hailong , Guo Jing , Yang Baolong , Zhang Yan , Liu Jiyu , Gong Jing , Wen Kai
Published: December 2024

In recent years, the collaboration between natural gas systems and electricity power systems (EPS) has become increasingly close. The frequent and highly random energy flow conversions pose great challenges to the online flow state estimation of natural gas networks. In response to the issues of poor adaptability under transient flow conditions, difficulty in establishing a comprehensive network model, and high time costs in existing flow state estimation models, this study establishes a novel transient flow simulation model for natural gas pipeline networks (NGPN) based on the Box-Jenkins model. The Box-Jenkins model, known for its computational efficiency in parameter identification of dynamic systems in the field of automatic control, is derived for the first time in this study based on the partial differential equations of natural gas flow. A framework for transient flow state estimation is further established, which enables optimal state estimation at instrumented locations based on Kalman filtering. Additionally, the framework can utilize optimal estimation values to modify the transient flow calculation model based on optimization theory. Through verification with actual natural gas networks and commercial simulation software, the proposed transient flow simulation model for natural gas networks is shown to efficiently achieve flow state observation and model modification.
Oil-Gas Multiphase Flow Surrogate Model Embedded With Mechanism Formulas
Authors: Pan Shiyuan , Xu Ning , Li Zhuochao , Niu Pengtao , Guo Yi , Liang Yongtu
Published: December 2024

Accurate predictions of pressure and temperature drops in oil-gas multiphase flow pipelines are vital for the design and operational efficiency of oil and gas field pipelines. Mechanistic models, though precise, require lengthy computations, rendering them impractical for the extensive calculations needed during optimization. Moreover, their accuracy is often hampered by limited experimental data ranges. Machine learning surrogate models emerge as a solution, offering swift calculations with high accuracy, yet they typically overlook flow-specific PVT properties and gas velocity. Additionally, the “black box” nature of machine learning is often criticized. Addressing these gaps, our research presents a deep neural network surrogate model that offers faster computation speeds while maintaining nearly the same level of accuracy as mechanistic models. This model synergizes with mechanism formulas, including momentum conservation, and encompassing flow condition PVT properties. It adeptly predicts pressure and temperature changes in pipelines of any orientation, with input parameters including liquid flow rate, gas-oil ratio, inlet pressure/temperature, pipe length, diameter, inclination, and gas-liquid densities (at standard conditions). The outputs are outlet pressure/temperature, gas phase density (at a specific temperature and pressure), gas phase superficial velocity, liquid holdup, and two-phase friction factor. Training with the PIPESIM simulator data, it demonstrates a remarkable prediction accuracy of over 98%, significantly surpassing traditional machine learning and neural network approaches. Additionally, it boasts a computational speed three orders of magnitude faster than conventional mechanistic models, proving its efficacy for extensive, rapid calculations necessary for large-scale pipeline optimization.
A Novel Prior-Guided Hybrid VFM Framework Integrates Mechanistic and Data-Driven Approaches in Oilfields
Authors: Shen Siheng , Song Shangfei , Liu Daqian , Gong Jing , Wu Haihao , Shi Bohui , Kang Qi
Published: December 2024

Real-time assessment of oil well production stands as a pivotal factor in the effective management and optimization of oilfield production and the gathering pipeline networks. Precise and timely real-time quantification of oil, gas, and water phases is of paramount importance, serving as a compass for on-site personnel in steering decision-making processes. Developing methods to accurately measure the flow rates of individual wells without expensive hardware is crucial for reducing production costs in the oil field industry. The burgeoning appeal of virtual flow metering (VFM), leveraging on-site sensor data encompassing pressure and temperature, has surged within oilfield production systems. In this paper, we propose a novel Prior-Guided Hybrid VFM framework, engineered to glean a richer tapestry of information from signal data. Initially, we introduce a Prior-Guided VFM Module, assimilating the foundational Electric Submersible Pump (ESP) model as prior knowledge in the data-driven model. This integration of ESP model guides the model training purposefully and simultaneously adjusts coefficients within the ESP model during model learning. This module amalgamates mechanistic models with data-driven models to yield enhanced precision in the outcomes obtained. Subsequently, we formulated a Secondary Hybrid Training Module integrating self-attention mechanisms. This module aims to extract potential relationships of multidimensional data in a high-dimensional space and fuses the results of the mechanism-containing module to ultimately achieve hybrid prediction. The empirical validation of this framework is conducted using actual data derived from an offshore oilfield. Comparative analysis against five data-driven models substantiates the efficacy of the proposed framework. Results demonstrate that this approach not only performs better but is also suitable for few-shot scene. This work provides profound insights into the integration of mechanistic and data-driven approaches in VFM. The significance of this study lies in reducing the costly hardware and manpower expenses in oilfield development, which is crucial for the advancement of smart oilfields.
Integrated Design and Operational Optimization of Gathering Pipeline Networks Using Neural Network Surrogate Models
Authors: Fu Guangtao , Pan Shiyuan , Xu Ning , Chen Junzhuo , Zhao Huanyu , Liang Yongtu
Published: December 2024

The optimization of the surface gathering pipeline network (GPN) in oil and gas fields encompasses both design and operational improvements, involving economic and environmental benefits. Previous research often tackled these two optimization problems independently, which may lead to local optima. Furthermore, oilfield often involve multiphase flows, necessitating detailed hydraulic and thermodynamic calculations. Although the calculation time of current mechanistic models has reached the level of seconds, the total time required for the tens of thousands or even millions of optimizing calculations needed is still unacceptable. To address the issue above, an MILP method that integrated design and operational optimization is proposed. The proposed method employs the neural network surrogate model for hydraulic and thermodynamic calculations and utilizes the genetic algorithm to solve the optimization model. The obtained results include detailed topological structure, pipeline diameters, locations and types of devices, temperatures and pressures at each station, and the total construction and operational costs. The dataset is generated using PIPESIM software, and the predicted results are evaluated using RMSE, MAPE, and R2 metrics, demonstrating that the proposed neural network agent model meets the accuracy requirements for hydraulic and thermodynamic calculations and achieves a computational speed improvement of three orders of magnitude compared to mechanistic models. Compared with the two-stage algorithm (where design and operational optimizations are solved independently), the optimization method presented in this paper reduces construction costs by 2.7 million CNY and operational costs by 6.6 ten thousand CNY per year, representing relative reductions of 27.83% and 0.93%, respectively.
Portable Indentation Plastometry for Accurate and Immediate In-Ditch Material Verification
Authors: Campbell Jimmy , Dean James , Warwick Marcus
Published: December 2024

A new tool has been developed for in-ditch verification of yield strength and tensile strength. The PLX-Portable from Plastometrex utilizes a method termed Profilometry-based Indentation Plastometry (PIP) testing. In 2023, to support its adoption on midstream oil and gas transmission pipelines, the tool was subjected to a period of validation testing in cooperation with several network operators, service providers and the Pipeline Research Council International (PRCI) with the results independently verified by RSI Pipeline Solutions.

In this paper, the blind testing data on full-ring sections of pipeline from tests carried out in-yard or in-ditch are presented. The results are compared with those obtained in the initial validation work using various statistical methods favored by different operators for approval of Non-Destructive Evaluation (NDE) tools on their networks.
Gap Analysis of Coating and Liner for Hydrogen Pipeline
Authors: Li Yuan , Huard Martin , Wong Kira , Wang Xinkui , Adane Kofi Freeman
Published: December 2024

Transportation of hydrogen through pipeline is currently being implemented as part of the energy transition strategy to achieve net zero emissions. The application of internal coating and liners to hydrogen pipelines is expected to address the unique challenges brought by hydrogen properties, repurposing existing pipelines and blending hydrogen into natural gas flow. The challenges associated with hydrogen pipeline are reviewed, finding that an internal coating and liner used for hydrogen pipeline is expected to have good hydrogen barrier performance, smooth surface finish and compatibility with pipeline activities. This paper explores various materials including polymers, metals, ceramics, and composites, highlighting their advantages, limitations, and potential applications in hydrogen pipeline infrastructure. Special emphasis needs to be placed on the evaluation of hydrogen barrier performance, flow efficiency enhancement, and compatibility with pipeline activities through standardized qualification protocols. Additionally, this paper discusses the current gaps and challenges in testing methodologies and criteria for internal coatings and liners in hydrogen pipelines, proposing areas for further research and development. Overall, this submission aims to provide valuable insights and recommendations for the effective design, implementation and qualification of internal coatings and liners in hydrogen pipeline systems, contributing to the advancement of hydrogen infrastructure for sustainable energy transportation.
The Role of Slurry Microtunnelling for Pipeline Installations in Hard Rock Conditions
Authors: Herrenknecht Simon
Published: December 2024

Pipe Jacking with Slurry Microtunnel Boring Machines (MTBMs) plays a pivotal role for challenging sections along the main Canadian pipeline routes, through the mountainous areas characterized by high rock strengths. In addition to the well-known pipeline installations with HDD and Direct Pipe, Slurry MTBMs for pipe jacking can be used for safe and precise excavation of small-diameter casing tunnels, also in challenging ground, where alternative methods reach their technical or economic limits. In small-diameter tunnelling through hard rock, the continuous further development of technologies plays a key role to overcome the limitations in rock strength and drive length and to improve performance.

The cutting wheel’s tooling composition is one of the critical aspects in rock applications. Tool size and arrangement are determined based on the rock properties, to obtain reasonable rock chip sizes which can be handled by the discharge system. Three main cutting tool types can be considered for MTBMs: disc cutters, TCI cutters or milled tooth cutters. The most common cutting tool remains the disc cutter, which is well known from large diameter hard rock TBMs. Small-diameter hard rock Slurry MTBMs need to have a capability of high jacking forces and both a strong main bearing and cutter bearing.

For the latest AVN 800 for hard rock, TCI cutters have been chosen. TCI cutters exert a point load force on the rock, resulting in numerous small chips. Due to the tungsten carbide insert, the TCI cutter is considered especially wear-resistant, which is beneficial for long drives in small diameters, where cutterhead interventions are not possible.

In hard rock conditions with high rock strength and/or abrasivity, the applicability of Slurry MTBMs has been especially limited in the non-accessible diameter range in the past. With the latest AVN 800 for hard rock microtunnelling, Herrenknecht has developed a powerful machine concept, enabling drives of up to 200 meters without interventions. Longer drives make microtunnelling a more economic and more environmentally friendly construction method, as the number of shafts can be reduced. New opportunities for clients and consultants in the planning of tunnel routes for different pipeline installations can be considered, making trenchless technologies even more competitive and cost-effective, and improving public acceptance at the same time.
Application of IOGP S-616 Annex U for Qualification of HFW Pipe: A Case Study
Authors: Lam-Thanh Luc , Tazedakis Athanasios S. , Bouchaud Frederic
Published: December 2024

High-Frequency Welded (HFW) line pipe stands out as a preferred manufacturing method, renowned for its efficiency, precise dimensional control, and robust mechanical properties. However, historical challenges have emerged, particularly related to undetected flaws in the weld bond line and suboptimal heat treatment practices.

In a significant development, the International Association of Oil and Gas Producers (IOGP) introduced the S-616 Specification Revision 2 (August 2022), titled “Supplementary Specification to API Specification 5L and ISO 3183 Line Pipe.” This collaborative effort by 12 Oil&Gas companies establishes standards for line pipe procurement, introducing Annex U to address the qualification of HFW pipe-mills.

This article showcases the successful application of IOGP S-616 Annex U in the qualification of Corinth Pipeworks S.A. (CPW) HFW pipe mill.

Qualification, initiated by steel production at Arcelor Mittal, Fos, incorporates specific internal controls to ensure coils possess consistent properties and minimal segregation levels, supporting weld line soundness. CPW’s mill qualification involves producing line pipes under five combinations of squeeze/pressure, and HFW heat input. These conditions, including optimal and variable parameters, create a production envelope. Line pipes undergo automatic ultrasonic inspection and destructive testing.

Based on the specific qualification process outcome, it is concluded that the application of IOGP S-616 Annex U ensures the reliability and safety of HFW line pipes.
Toughness Evaluation and Integrity Assessment of Modern X80 Linepipe Welds for the Application of High Pressure Hydrogen Pipeline
Authors: Ishikawa Nobuyuki , Imayama Hikaru , Sakimoto Takahiro , Jia Dan , Wang Jiawei , Wang Yong-Yi
Published: December 2024

The fracture toughness test under 100% hydrogen with testing pressure of 21 MPa was conducted on a recently produced Grade X80 linepipe and its seam and girth welds. For comparison, two X70 linepipes and one X65 linepipe and their welds were tested as well. For the X80 linepipe, the seam weld heat affected zone (HAZ) exhibited the same level of fracture toughness as the base metal. The toughness is higher than the minimum requirement in ASME B31.12 when it is measured by the crack initiation toughness not by the conventional 0.2mm offset value in a J-R curve. The girth weld HAZ of the X80 linepipe showed higher fracture toughness than the seam weld HAZ.

Using the fracture toughness values obtained from the weld fracture toughness tests in 21 MPa hydrogen, the critical depth of planar axial flaw was determined for different measured values of toughness and design factor using assessment procedures in API 579. The critical flaw depth strongly depends on the residual stress assumption used in the assessment. The allowable planar flaw dimensions for girth welds were determined using the assessment procedure in Annex A of API 1104. It was shown that the girth weld of the X80 linepipe would have workable allowable flaw dimensions due to the high toughness measured in the tests. However, girth welds with the required minimum toughness of ASME B31.12 would have allowable flaw dimensions much smaller than the workmanship criteria in API 1104 or equivalent standards. There can be multiple causes for this outcome which should be investigated further.
Gas Turbines Automated Control Solution for Combustion Mapping
Authors: Bagni Gianni , Pasquotti Maura , Venturini Erica , D’Adamo Pietro , Bellini Enrico
Published: December 2024

In midstream, there is a continuously increasing need to operate applications with greater efficiency, availability, and reliability. Business operations depend on an uninterrupted stream and plant equipment, i.e., gas turbines, need to operate continuously and safely, maintaining high operational efficiency to protect assets value and generate profits. In addition, strict environmental standards must be adhered to while still being able to optimize the assets’ performance.

Aeroderivative engines (“LM”) with dry low emission (“DLE”) combustion system by Baker Hughes (“BH”), are common primary drivers used in midstream assets for their operational flexibility, high efficiency, and short maintenance outages. However, they require periodic combustion tuning, normally twice per year, to guarantee emissions compliance and to avoid combustion issues, e.g., high acoustics or blow-out, potentially affecting availability and reliability. Moreover, combustion tuning activities may require some time, and constraints of the operation may conflict with the ability to tune the engine. Therefore, BH identified the opportunity to improve the field engine availability by skipping the semi-annual DLE tuning activities.

The proposed solution is a full software-based solution - Automapping - available for LM2500 GT family with DLE combustion system, capable of automatically tuning gas turbine combustion parameters to continuously keep emissions within required limits. Compared with existing solutions offered by Original Equipment Manufacturers (“OEM”) that are currently available in the market, this solution can work without any permanent CO/NOx sensor, with less complexity and costs to be managed.

Automapping software includes a proprietary algorithm that explores combustion solutions, capable of searching combustion adjustments keeping emissions below desired value. Algorithm is based on some emissions models for fast emissions evaluation, not being bounded to emissions sensor response time. The combination of the Automapping software and predictive emissions monitoring system (“PEMS”) allows the emissions to be controlled when required and validates the emissions are corrected to desired levels.
The Application of Deep Learning and Computer Vision Techniques to Magnetic Flux Leakage Data to Accurately Detect and Predict Corrosion Profiles
Authors: Mattio Dustin , Miller Scott , Rodway James
Published: December 2024

Over many years trends in the methods used for analyzing and reporting in-line inspection data have evolved from peak depth in a joint to peak depth for every feature, to clustering and river bottom profiles, and now grid level representations of corrosion defects are becoming a favourable reality. With these advances, comes a key benefit that the understanding of defect profiles has advanced from the well know “boxing” methodologies to detailed, direct representations of pipe defect shape, attributes, and characteristics which in turn opens a clear pathway to overcome the primary challenge of interpreting complex superposition of the magnetic signal responses in areas with complex morphologies and interactions.

This paper outlines how advancement in deep learning methodologies with “Big Data” approaches have been applied successfully to magnetic flux leakage data for predictive corrosion sizing at scalable, “life-like” grid level resolutions using computer vison techniques. The proposed methodology utilizes deep learned neural network architectures trained on extensive datasets to accurately characterize defects, thereby providing a more refined and precise analysis compared to conventional methods.

This paper will outline some background to the motivations in developing the solution, context of data volumes employed, discuss the challenges of adopting the methodology at scale across an entire pipeline and how cloud computing and advanced data engineering approaches have been applied to provide the scalability needed. The authors will also detail the essential role that vetting of truth data, it’s characterization beyond conventional industry standard approaches for defining defects shapes and types and development of new techniques for data comparison and validation play in ensuring success.

Lastly a perspective of how the availability of these advanced algorithms as a comparative view to traditional analysis and interpretation techniques can evolve and provide the means significantly improve the reliability and efficiency of corrosion threat determination in pipelines for the oil and gas industry.
Understanding the Impact of Correlated Corrosion Inline Inspection Uncertainties on Reliability
Authors: Langlois-Rahme Gabriel , Safari Miaad , Shabarchin Oleg , Di Blasi Martin
Published: December 2024

In an industry where evolving technology and data-driven decision-making are critical in effectively managing the integrity of pipeline assets, this paper addresses the need for validation of measurement uncertainties of pipeline inspection technologies. The study delves into the less explored domain of error correlation in Magnetic Flux Leakage Axial (MFL-A) Inline Inspection (ILI) tools. It identifies the extent at which various sources of random and systematic errors are correlated at different distance spans. Factors such as tool speed, sensor lift off, debris, material variations, and magnetic field inconsistencies influence measurement random error while the feature morphology and orientation can influence systematic errors in the sizing algorithms. The study utilizes Non-Destructive Examination (NDE) ultrasonic corrosion defect data to empirically conduct an in-depth analysis at various spatial levels — cluster, pipe joint, and pipeline — to understand the extent of correlation on measurement uncertainties. This includes a statistical assessment of over sizing and under sizing correlations in depth measurements.

Our study is applicable to advanced probabilistic reliability models, such as performing reliability assessment with the Effective Area model, where the co-relations of uncertainties within a cluster could impact the results. The results of this study will help engineers better understand what level of correlations to assign for probabilistic burst pressure assessments. Our study seeks to determine whether depth measurement errors are consistently present along the entire pipeline, within short spans, or in specific clusters, thereby influencing the correlation of uncertainties and affecting the overall reliability model. If strong correlations are found across small inspection spans but are not consistently represented across the entire inspection, then API 1163 level 2 and level 3 validations require field data to be sufficiently spread across the inspection to be representative of the entire inspection. This finding would highlight the value of using field data spread across the pipeline, and that an operator’s confidence on tool performance can be impacted by both sample size and field measurement span. This study aims to help risk-informed decision making with a more accurate representation of depth measurement uncertainty.
The Increase in Effectiveness of an Emergency Pipeline Repair System (EPRS) Can Be Measured by the Reduction in Downtime of a Pipeline Subject to Random 3rd Party Events
Authors: Mcintyre Thomas , Alani Ali , Curson Nigel
Published: December 2024

This paper presents the typical steps taken to respond to an emergency pipeline damage scenario and the impact on downtime when the operator of the pipeline is not prepared. The industry standard method of preparing for an emergency damage event is an Emergency Pipeline Repair System (EPRS) which provides guidance on the likelihood of each type of damage event occurring, the best practices when assessing the damage and the best repair strategy for the damaged pipeline.

The paper focuses primarily on offshore pipelines and follows a hypothetical damage event with each stage of the emergency response and repair explored. The damage event example allowed for investigation of the emergency response and repair process for both an offshore pipeline with and without a comprehensive EPRS in place. For each stage, the tasks carried out when with and without an EPRS are detailed and an estimated time to complete each stage for the two scenarios is given. This allowed for comparison and discussion on the time saving benefits of an EPRS for offshore repair.

A discussion on the uncertainty of the estimated time to complete each stage of the emergency response and repair is given and highlights areas for potential further research. This uncertainty discussion is revisited in the conclusion where a plan to improve the time predictions made for the EPRS model is made.

Two case studies of third-party damage response to subsea pipelines are used as comparative cases to the hypothetical damage event case, the case studies cover a pipeline with a comprehensive EPRS in place and one without allowing for useful comparison and discussion on the accuracy of the estimated times given for each step of the EPRS.
Engineering Assessment of Hydrogen Embrittlement Susceptibility at Defects in Hydrogen Pipelines by Numerical Modeling
Authors: Xu Luyao , Finneran Shane
Published: December 2024

Conversion of existing natural gas pipelines to transport hydrogen and hydrogen blending gas is increasingly recognized as an effective method to decarbonize the energy system and accelerate the achievement of the net-zero emission goal in 2050. However, aged pipelines typically exhibit various defects such as corrosion, cracking, and mechanical damage. These defects can lead to uneven stress and strain distribution, and plastic deformation, which can serve as hydrogen traps to accumulate hydrogen atoms, thereby increasing hydrogen embrittlement (HE) risk at defects during hydrogen services.

Extensive research has been conducted globally, aiming to understand the degradation of the mechanical properties of pipeline steel and assess existing pipelines’ compatibility through theoretical and experimental methodologies and techniques, however, there are few studies on hydrogen atoms diffusion and accumulation behavior and the influence on the local degradation of material properties of steel at the defects. This deficiency in current research and industry standards underscores the urgent need for in-depth research and the development of reliable methodologies to assess the integrity of defects in hydrogen pipelines.

This paper demonstrates an advanced multiphysics field finite element analysis (FEA) approach to study the time-dependent atomic hydrogen diffusion, accumulation, and hydrogen-induced fracture toughness degradation at pipeline defects and evaluate the locally enhanced hydrogen embrittlement risk at the defect sites. It is expected that this research contributes to the further development of defect engineering assessment methodologies for hydrogen pipelines.
Consensus Engineering Requirements for Hydrogen Pipelines
Authors: Slater Simon , Shie Taylor , Noecker Fredrick F. , Gallon Neil , Sandana Daniel
Published: December 2024

In June 2023, the American Society of Mechanical Engineers (ASME) agreed that an update to ASME B31.12 was required. Especially relating to the technical standards for converting and operating hydrogen and hydrogen blend pipelines. It was acknowledged that the updates should be transferred over to ASME B31.8. To provide rapid development and implementation, the Emerging Fuels Institute (EFI), established by the Pipeline Research Council International (PRCI), developed a project to create Consensus Engineering Requirements (CERs) as a basis on ASME’s behalf. The CERs were completed in Q2 2024. Work is ongoing to progress the CERs through the ASME committee review process, with an expectation to implement the new guidelines for hydrogen pipelines in the 2026 version of ASME B31.8.

The CERs are predicated on the latest research related to pipeline transport of hydrogen gas and blended hydrogen gas developed by global industry expertise. The CER project team consisted of over 40 members, the majority of who are gas pipeline operators. The group worked in close collaboration with key stakeholders, including the tripartite members of PRCI, European Pipeline Research Group (EPRG) and Australian Pipeline Gas Association (APGA).

This paper presents a summary of the process used to create the CERs and guidance published by the EFI. It focuses on the significant technical issues that had to be resolved.

Because ASME codes are used throughout the world, it was important to create guidance that balanced local requirements and challenges with best practice. Consequently, significant effort was taken to align with industry experts and distill the level of knowledge into practical, concise, and robust requirements. The paper will provide the reader with a summary of the work performed by the EFI to move the industry forward at a rate matching the demand for hydrogen as an energy carrier.
Risk Assessment of the Transmission Pipeline Appurtenance Threat
Authors: Fazli Samir , Sen Millan , Akonko Saheed
Published: December 2024

The Enbridge liquids pipeline system contains more than 28,000 KM of transmission pipe. Systemwide assessments are conducted to evaluate the risks associated with integrity threats, whereby historically the focus has been on crack, corrosion, dents, and geohazards. However, as reported in the API 2022 Performance Report, equipment failure is the leading cause of liquid pipeline releases. Correspondingly while release volumes are typically low, there are other threats such as appurtenances, which can lead to failure and warrant risk assessment.

Prior assessments used average historical failure rates for appurtenances which enabled high-level risk quantification; however, this approach was overly simplistic and did not differentiate between different characteristics of the same asset class. In 2021–2022 four appurtenance risk ranking models: valves, sleeves, flanges, and offtakes, were developed to enable comprehensive systemwide risk management. During model development, there was an objective to incorporate model inputs that are readily available from drawings and in-line inspection data. Correspondingly the model is easily implemental whereby the inputs included only: asset information (i.e. vintage, construction), site specific attributes (i.e. proximity to other appurtenances), and operational considerations (i.e. proximity to pump stations, pressure cycling, product types).

The steps conducted to enable risk assessment of the appurtenance threat included:

• Literature review of historical failures and failure modes based on operator specific databases and publicly available literature to identify key factors that lead to failures.

• Application of model factors based on appurtenance characteristics and subject matter expert input.

• Calibration of models so that the systemwide average and aggregated likelihood of failure align with those historically observed.

• Validation of models based on the predicted likelihood of failure for historically failed assets.

It was demonstrated that the models captured the key characteristics that impact possible failure modes for pipeline appurtenances.
Case Studies on Landslide Threats: Identification, Ground Modelling and Pipeline Structural Analysis
Authors: Senior Rebecca , Bainbridge James
Published: December 2024

Geohazard management of pipelines in areas prone to landslides is an integrity threat that pipeline operators must address. The management process involves three key elements: (1) the identification of a developing hazard, (2) the evaluation of the nature of the threat and, (3) deciding on the most appropriate actions that are necessary to address the threat and to protect the pipeline.

There are many available methods that can assist with the three parts of this process. However, the availability of multiple approaches can make it difficult to know what the most effective method is in terms of collecting the right data, making reliable predictions of pipeline integrity, or accurately monitoring future changes in integrity. Any deficiencies in the process can be wasted effort and cost at best, but could also result in failure, with adverse consequences for safety and the environment as well as severe financial penalties.

In this paper, we present what we consider to be an effective and efficient management process for landslide threats to pipelines based on in-line inspection data integrated with detailed numerical modelling and structural analysis to evaluate pipeline integrity. In-line inspection provides factual data on the presence and effects of displacement loads on the pipeline as well as significant additional data on factors that control the structural capacity of the pipeline. The alignment of integrity calculations with measured inspection data enables the process to be validated and improves confidence in the predicted results. The process is comprehensively illustrated through a range of case studies. The significant potential of a novel inertial measurement unit technology (PipeDrift) for monitoring at an increased interval and reduced lead time is also covered.