bluuebunny/crossref_metadata_2025_split huggingface dataset:
Installing Unsymmetrical Structural Extensions on Jackets: A Case study
Authors: George Charles John , Cherian Litty Dani , Kamal Faris Ragheb 
Published: November 2024

Abstract
The installation of unsymmetrical structural extensions on offshore jackets presents significant engineering challenges that necessitate meticulous planning and innovative solutions. This case study delves into the complexities and unique considerations encountered during the installation process, offering insights into overcoming these challenges.
Offshore jackets serve as foundation structures for oil and gas platforms, providing stability and support in harsh marine environments. When these platforms require modifications or expansions, such as adding unsymmetrical extensions, engineers must carefully assess the implications on structural integrity, stability, and safety.
Key challenges in installing unsymmetrical extensions arise primarily from the asymmetrical loading conditions they introduce. Unlike symmetrical modifications that evenly distribute forces, unsymmetrical extensions create uneven stresses on the jacket structure. This imbalance requires detailed analysis to ensure that the existing structure can withstand increased loads without compromising its integrity.
Moreover, the installation process itself poses logistical and technical challenges. Accessing offshore locations, often in remote and challenging environments, adds complexity to the execution of structural modifications. Engineering teams must devise strategies for transporting materials, managing logistics, while ensuring the safety of personnel throughout the installation phase.
This case study examines a specific project where an offshore jacket required an unsymmetrical extension to accommodate a new Topside (much large than the original due to enhanced facilities). It explores the methodologies used to assess the structural implications, including computational modeling. Special attention is given to the design considerations that influenced the selection of installation methods and equipment.
Furthermore, the study discusses the role of interdisciplinary collaboration between Engineers, Yard, Operations, and Project Managers in overcoming these challenges. Effective communication and coordination were essential in aligning technical requirements with operational constraints, ensuring the successful execution of the project.
Ultimately, this case study provides valuable insights and lessons learned for future projects involving unsymmetrical structural modifications on offshore jackets. By addressing these challenges proactively and leveraging advanced engineering solutions, industry professionals can enhance the efficiency, safety, and sustainability of offshore infrastructure expansions. The critical aspects detailed in this paper can be used as a reference by any EPC Contractor involved in the brownfield modification of jackets.
Integrated Online Monitoring of Piping Loops with Predictive Solutions Applied During Project Execution
Authors: Sahoo Subrat 
Published: November 2024

Abstract
The Integrated ‘Online Monitoring of Piping Loops with Predictive Solutions’ is a new technology that enables digital integration of project piping data with end-to-end tracking of each piping loop, system and unit completion in real time. The system enables transparent visualization of critical piping parameters and provides actionable insights to project engineers in an online interactive dashboard. It thus eliminates the days spent on spreadsheet analysis in conventional mode.
The piping loop calculations and workflow are coded to formulate the predictive algorithms, hosted in a cloud-based platform for implementation. The system assimilates the current data from multiple sources and then the algorithms generate updated reports on work progress, constraints and completion trends. Online material hold-up report acts as a ready reckoner for fast-track decision making on procurement action and prioritizes material allocation. Computation of probabilistic loop-system-unit and overall project completion date provides proactive approach to evaluate the work performance with attributable reasons.
The innovation has resulted in better adherence to the project critical path through extensive monitoring of piping loop data, starting form material availability to readiness of piping systems for unit startup. There has been considerable saving in time for information flow, resources and man-days spent during project execution. As per the case study done, there has been a saving of nearly two-third man-days, exclusively for the monitoring and control of piping works. The results reaped in the implementation of this technology are encouraging and paves its way for implementation in other refinery and petrochemical projects involving piping works.
With implementation of this technology, projects shall be executed with a difference, particularly the critical piping works, at a faster pace that will benefit petroleum industry companies directly. All stakeholders in the project shall be well connected with the work status and garner benefits in terms of superior project performance, improved predictability and project closure within schedule & cost or even better.
Challenges in Accurate Detection of the Main Cause of Blockage: Case Studies
Authors: Vatandoost A. , Tohidi B. , Hoopanah M. , Meybodi M. K. , Mozaffar H. , Joonaki E. 
Published: November 2024

Abstract
Flow blockages in oil and gas pipelines are critical challenges that can lead to severe operational disruptions and significant costs of blockage removal and production loss. These blockages may arise from various sources, including wax deposition, asphaltene precipitation, gas hydrate formation, and ice. Accurately identifying the root cause of a blockage is essential for implementing effective remediation strategies. This paper presents a detailed study of the challenges associated with the accurate detection of blockages in oil and gas production systems, focusing on three real-world case studies.
The first case study examines a subsea pipeline where initial suspicions of hydrate formation were complicated by the presence of ice, requiring advanced thermodynamic modelling to accurately diagnose the issue. The second case study involves a subsea light oil pipeline where wax deposition, rather than the initially suspected hydrate formation, was identified as the root cause of the blockage after comprehensive phase equilibrium analysis. The third case study discusses a chemical process plant where a blockage in a Cold Box, initially attributed to hydrate formation, was later confirmed to be due to ice, as revealed by a combination of phase boundary analysis and methanol injection testing. The findings underscore the importance of precise phase equilibrium modelling and a comprehensive understanding of the system's thermodynamic conditions in diagnosing and resolving flow assurance problems.
B-Factor Variation in the Application of Traditional Decline Curve Analysis in Unconventional Wells
Authors: Alowaid Abdulrahman. M. 
Published: November 2024

Abstract
This paper focuses on investigating the applicability of using the traditional Arps Decline Curve Analysis (DCA) in unconventional resources characterized by rapid decline rates. The study focuses on the variability of the b-factor at different stages of the production life cycle and provides a sensitivity study on the magnitude of change the b-factor has on the Estimated Ultimate Recovery (EUR) of unconventional wells. The methodology adapted in this paper utilizes a reservoir simulator to run multiple DCA scenarios on an application example, providing both a quantitative and a visualized sensitivity analysis of EUR forecasts driven by multiple b-factor values. The production of the application example well was segmented to account for well clean-up post stimulation, and various operational changes throughout the life of the well. Traditional DCA was applied on the segments that most represented the general performance of the well, then benchmarked against an EUR value obtained using Rate Transient Analysis (RTA) utilizing the same reservoir simulator. During the process, the b-factor was varied to achieve the best fit with production data that yielded the lowest error. Two different scenarios were studied. The first scenario applied traditional DCA on all production data without segmentation, while the other scenario was applied on selected intervals where well behavior was considered relatively stable. Both scenarios underwent a sensitivity study estimating EUR at four different b-factor values: 0.82, 1.0, 1.5 and 2.0. It was observed, for the first scenario, that EUR estimates were far from accurate when benchmarked against RTA results, attributed to the initial rapid decline and multiple transitional periods during the production life cycle. On the other hand, for the second scenario, when only the representative segments of the well were accounted for, results were comparable to that of RTA, with a perfect match using a b-factor value of 1.5. The findings from this assessment were indicative in qualifying the traditional DCA for use in an unconventional setting provided that operating conditions are understood, and intervals considered in the forecast are filtered towards a representative well behavior. The novelty of this work lies in studying the application of traditional Arps DCA in unconventional hydrocarbon resources, generally perceived as a challenging approach due to rapid changes in well behavior. The study investigated and confirmed the feasibility of applying the traditional Arps DCA in unconventional wells through introducing an intermediate step, to segment the production life and analyzing the representative portion of the production life cycle.
Strategic Enhancement of Well Integrity in Ultra-Sour HPHT Wells: A Multidimensional Approach to Operational Resilience, Decarbonization, and Sustainable Equipment Efficacy
Authors: Yakoot M. S. , Valecillos D. E. 
Published: November 2024

Abstract
The integrity of downhole completion systems is paramount in high-risk wells characterized by Ultra-Sour conditions, extreme temperatures, and elevated pressures. This study proposes a proactive material testing initiative to fortify well integrity management strategies and mitigate the risk of catastrophic well integrity failures.
A comprehensive review of tubing materials for high-risk wells was conducted, revealing gaps in industry benchmarks and potential failure mechanisms. The proposed environmental material tests, adhering to NACE and ASTM standards, will assess the performance of the selected alloys in simulated operational conditions.
The material testing conducted revealed key findings. For tubing materials, the lab test duration was extended to align with industry standards. This addressed the previously limited duration and incorporated additional tests. The tests also considered higher environmental stress levels and uncertain factors.
The study on Elastomers revealed limitations in chemical compatibilities and temperatures, with recommendations for testing due to limited experience in such environments. The proposed minimum requirements include conducting material tests for an extended duration for both environments.
Rigorous testing and collaboration among specialists are instrumental in safeguarding the operational integrity of high-risk wells. The proposed testing initiative underscores the significance of a systematic approach to well integrity management, with special focus on high-profile wells and the harsh conditions they will encounter.
Hard-To-Abate Decarbonization Strategy Using Waste to Power (WTP) for Clean/Green Hydrogen Generation - Technoeconomic Case Study in Malaysia
Authors: Alias Nur Dalila , Zubir Ahmad Umair , Wazir M Aiman Afif M , Sariman Mior Zaiga , Othman M Hailmi 
Published: November 2024

Abstract
Limitation on reliable clean energy source to power the production of clean hydrogen has led the operator to come out with a business case for an onshore clean hydrogen production, using large-scale deployment of modular Organic Rankine Cycle (ORC) technology in converting industrial waste-heat to power (WTP) the Proton Exchanged Membrane (PEM) electrolyser. The primary objective of this study is to assess technical and commercial viability on large-scale deployment of modular Organic Rankine Cycle (ORC) technology in converting industrial waste-heat to power (WTP) at identified oil and gas plants within Malaysia.
A methodical selection criterion was used to identify suitable plants for assessment. Based on the steam and/or gas turbine heat generated from each plant, the estimation of power generation and its potential hydrogen generation was calculated. The operational parameters and commercial enveloped of the techno-commercial assessment were investigated in this study. These parameters include optimum number of ORC unit installed, tax pricing and Levelized Cost of Electricity (LCOE) as well as cost for hydrogen per kg was established for each scenario to achieve positive NPV. A comprehensive understanding of the commercial approach for this initiative to be implemented can be obtained with insights into the pathway of obtaining the best Levelized Cost of Hydrogen (LCOH) for each site.
The study shows maximum generation of 430mt of clean hydrogen can be generated per year, with maximum net power if 2.74 MW of clean electricity from the modular concept ORC technology. From the commercial analysis, the three (3) critical economic components identified from sensitivity study to make the economic positive are Carbon tax price, WACC % and CAPEX. One of the sites shows the most promising location for immediate implementation is with LCOH of 4USD, which is lower current hydrogen price i.e., 6-8 USD. The estimated payback period is 7 years with 3 mil USD NPV.
This study offers new insight on the best approach to obtain positive economic assessment within the hard-to-abate GHG emission in realizing the NZCE ambition. Setbacks and challenges will be discussed for the technology deployment to be well accepted within the industry, and recommendations on new financial elements and additional policies from various angles will be highlighted to boost large-scale decarbonization deployment within this region.
Troubleshooting of Repetitive Liquid Fuel Nozzles Clogging Due to Liquid Fuel Coking in Dual Fuel Gas Turbine
Authors: Anwar Zeeshan , Manikandan A 
Published: November 2024

Abstract
Gas turbines are one of the most widely used technology for both power generation and mechanical drive applications. Their availability is a critical requirement for any plant uninterrupted operations. The subject case study details the root cause analysis done on repetitive clogging of liquid fuel nozzles due to liquid fuel coking on a dual fuel heavy duty gas turbine, that had an impact on the overall plant operation.
The site is configured with two Frame 5/1PA single shaft Gas Turbine with standard combustion system, driving generator for power production. The two Gas Turbine Generators (GTGs) are completely identical and running on same liquid fuel and gas fuel. These gas turbines can only be started with liquid fuel and then change over to gas fuel as required.
On one of the GTGs the issue of liquid fuel nozzles clogging due to liquid fuel coking happened twice in a span of six months. Few of the indications are Increase in exhaust temperature spreads and "failed to lite" during startup. Comparatively the other similar unit faced no such issue during its operations. Liquid fuel coking occurs if there is stagnant liquid fuel at high temperature over time at the Nozzles. For a duel fuel gas turbine, while it is running on gas fuel, constant air purge is provided in order to remove any residual liquid from the liquid fuel nozzles so it will not coke while running on gas fuel.
Innovative Isotopes Tracking: A Cost-Effective Approach for Monitoring of Injected CO2 Flood Advancement in Oil Reservoirs
Authors: Koksalan T. , Khan K. A. , Alwaeel R. S. , Aljneibi N. M. , Alblooshi M. N. A. 
Published: November 2024

Abstract
Assessing sweep efficiency and recognizing the point of breakthrough are critical for evaluating an Enhanced Oil Recovery (EOR) effort employing miscible CO2 Gas injection. This study aimed to employ CO2 carbon isotope composition and molar concentration to monitor sweep efficiency and breakthrough in production wells of an ADNOC Onshore field to optimize the project, such as adjusting injector and future production well placements. The integration of CO2 molar concentration data along with carbon and oxygen isotope readings, helped to assess the influence of CO2 Injection EOR. The project relied on the premise that using a distinct isotopic signature for injected CO2-deriving from a steel mill and notably lighter in comparison to CO2 associated with the oil-would aid in determining sweep efficiency and breakthrough levels. CO2 is injected through specific horizontal injectors in the north area of the field. Initial values of oil-associated CO2 carbon and oxygen isotope served as a benchmark to evaluate the progress of sweep efficiency and breakthrough. Frequently, compositions and isotopic values of injected CO2 are recorded. Subsequent periodic analyses of these components’ composition and isotopes in oil and water producing wells indicate the degree of breakthrough. Findings from CO2 molar and isotope measurements in both injector and producer wells demonstrate that injected CO2 tends to breakthrough preferentially in certain regions, signifying a varied reservoir quality across the field with superior reservoir quality, and CO2 injection distribution experiencing faster breakthrough. These compositional and isotopic metrics have proven sensitive enough to detect changes in producer wells. It is intriguing to note that the CO2 δ13C isotope signals a breakthrough sooner than the CO2 molar concentration. Data also indicates that CO2 WAG is a much more efficient EOR process compared with continues CO2 injection.
Enhancing Geological Studies Efficiency Through AI-Driven Analysis: A Case Study in Lower Cretaceous Time
Authors: Vaz R. , Al Muhairi B. , AlTamimi M. A. , Egorov D. , Yang F. , Li X. , AlZaabi R. 
Published: November 2024

The stratigraphic framework of the Lower Cretaceous in Abu Dhabi is significantly shaped by tectonic activity and eustatic sea-level fluctuations, which have governed sedimentation patterns and diagenetic processes. A comprehensive understanding of the facies distribution, diagenetic history, and depositional settings of these formations is essential for effective hydrocarbon exploration and production, as they represent key reservoirs in Abu Dhabi's prolific oil and gas fields. Detailed thin section analysis and microscale reservoir characterization are pivotal for elucidating these dynamic geological features.
During the recent years, artificial intelligence (AI) solutions have become widely adopted in the oil and gas industry and geology. Data science algorithms and deep learning models have empowered tools capable of rapid analysis and extracting insights from geological, geophysical and production data, proving their strong capabilities across various data and task domains. For instance, previous research has shown that such approaches are extremely useful for seismic faults identification (Egorov, 2019), well log data interpretation and localization of missed oil-bearing formations (Belozerov, 2018), core photos analysis to assess reservoir heterogeneity (Egorov, 2019), hydraulic fracture design optimization (Grigorev, 2020), among other applications. This research not only demonstrates that machine learning can optimize existing business processes optimization, but also highlights its potential to extract additional insights from existing data. Hence reducing geological uncertainties and mitigating associated field development risks. Despite the proven benefits of AI for geologists in daily tasks, research on the applicability of such models for thin section analysis is limited (e.g. Bukharev, 2018 and Leseur, 2022) and requires further investigation and its capabilities assessment.
The primary objective of this study is to further investigate the application of advanced artificial intelligence (AI) techniques, particularly supervised machine learning and computer vision, to enhance microscale reservoir characterization of thin sections from carbonate rocks in the Lower Cretaceous of Abu Dhabi's oil fields. This research encompasses not only the final AI models- including model architectures, data, training pipelines, and testing methodologies- but also the challenges encountered during the research and development process. The system's high accuracy is demonstrated through both qualitative and quantitative results.
Leveraging Artificial Intelligence for Prioritizing and Streamlining Maintenance Backlogs
Authors: Abdullah Muhammad , Rahnama Ramin , Radu Alina , Stewart Kevin , Buchan Gordon 
Published: November 2024

Abstract

Objectives/Scope
The safety of industrial facilities relies heavily on maintenance, yet planned tasks may face delays due to factors like surging work demands or insufficient resources. This results in a backlog of overdue activities, posing operational, environmental, and safety risks to assets. Pandemic-induced challenges, shortages of skilled staff, and inflation have exacerbated this situation, compelling us to seek innovative solutions. This suggested methodology prioritizes backlog tasks that improve reliability and eliminates non-value-adding items by combining artificial intelligence and technical expertise. This analysis considers multiple factors including resourcing capacity, aging infrastructure, marginal economics, and competing priorities.

Methods, Procedures, Process
The suggested methodology in this work combines artificial intelligence, automated data processing, and operations domain knowledge to empower asset owners. By eliminating non-value-adding backlog and providing targeted reliability improvement guidance, we enhance operational efficiency. This innovative methodology integrates natural language processing algorithms and generative artificial intelligence models, with advanced reliability assessment techniques. This methodology transforms raw data into actionable insights at significantly improved pace, accuracy, and consistency.
Reliability simulations are utilized to understand the next time a failure may occur. Reliability engineers then use this data to prioritize working on equipment with poor reliability, while work on equipment with good reliability is risk- assessed for cancellation. Done at scale and with pace, our backlog solution considers a range of factors within the assessment of overdue work, including criticality, reliability, consequences of failure, and remaining life of the assets.

Results, Observations, Conclusions
This methodology enables us to prioritize the most important work by understanding how backlog items affect asset safety and reliability. The strategic focus on value-adding work leads to improved safety, higher production, and lower costs.
A recent project utilized this process and examined over 200,000 hours of maintenance backlog tasks. The project eliminated 67,000 hours of maintenance backlog that did not add value, which reduced the operation cost by more than $5M. Utilising artificial intelligence allowed us to finish the project in 8 weeks, from the start of the project to the update of maintenance plans in CMMS.

Novel/Additive Information
Traditionally, maintenance engineers relied on manual review to prioritize backlog items. However, when faced with hundreds or thousands of activities in the backlog, this task became exceptionally challenging. Our innovative approach, leveraging artificial intelligence and automation, not only streamlines the process but also enhances accuracy and consistency, revolutionizing the landscape of backlog optimization.