bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.