bluuebunny/crossref_metadata_2025_split huggingface dataset:
AI-Enabled Annexure Parsing for Oil & Gas Project Exhibits
Authors: Mohamed Anwar Ali , Mohammed Azharuddin , Saud Shah , Veedu Anjana Kavassery , Zubair Syed Nizamuddin , Al Sayari Amal Salem 
Published: November 2024

Abstract
In the Oil & Gas industry, the effective management of capital project exhibit documents is essential for ensuring compliance, tracking progress, and making well-informed decisions. This paper presents a comprehensive methodology for developing a Large Language Model – Retrieval Augmented Generation (LLM-RAG) framework leveraging LangChain that enhances the accessibility and usability of these documents using advanced machine learning (ML) techniques. A Llama2-RAG model that incorporates Llama-2-70b large language AI model for advanced language understanding and generation is proposed in this paper. Project exhibit documents are uploaded via user-friendly Streamlit application, and the text is extracted and pre-processed to smaller chunks that are converted to vector embeddings and indexed in FAISS database for efficient retrieval. The integration of FAISS with LangChain creates a robust pipeline that retrieves relevant document sections and generates contextually relevant responses to user queries.
One of the primary issues addressed by this study is information overload, where the sheer volume of documents makes it challenging to find specific information promptly. The Llama2-RAG model alleviates this by providing precise and contextually relevant responses, reducing the time and effort required to sift through extensive documentation. Additionally, the study tackles the problem of version control, ensuring that stakeholders always have access to the most current information. The Llama2-RAG model also addresses the technical complexity of the documents, making them more accessible to non-technical stakeholders. By translating complex technical language into simpler terms, the model improves understanding and communication among project teams. The system is also designed to manage project-specific terminologies and document structures, offering a high degree of customization to meet the unique needs of each project. The study also highlights the development of the Streamlit application, which not only facilitates document uploads but also provides an intuitive interface for user interaction.
The integration of advanced AI and machine learning techniques ensures that this system remains adaptable and scalable, providing continuous improvements in accuracy and relevance over time. By addressing key challenges such as information overload, version control, cross-referencing, and technical complexity, the Llama2-RAG model promises to enhance the overall effectiveness of project document management, benefiting all stakeholders involved.
Advances in Inorganic Scale Prediction – Development and Validation of a Kinetic Model
Authors: Stalker R. , Nichols D. , Fyfe A. , Zarareh A. , Boyde D. , Graham G. 
Published: November 2024

Abstract
Understanding and predicting the impact of chemical kinetics and metastability on inorganic scaling in oil and gas production systems has been a significant challenge, particularly in many Arabian Gulf production environments where many systems are only moderately scaling. Development of a kinetic model can lead to significant improvements in predicting risk, occurrence and location of inorganic scaling in oil and gas production systems, resulting in significant cost savings when designing scale mitigation treatments.
A combination of fluid flow dynamics (FFD) modelling and laboratory tests were used to understand the influence of kinetics and metastability on the location and propensity of scale formation in oil and gas production systems. Extensive laboratory works were conducted using small scale high shear laboratory experiments and larger scale "pilot rig" tests under field representative conditions designed using CFD (computational fluid dynamics) modelling to recreate field flow environments using conditions prevalent in both Arabian Gulf and North Sea production environments. Subsequent validation of results was obtained by comparison with field experience.
In moderate or low scaling environments, under conventional (lower shear/lower turbulence) flow conditions mineral scale often either isn’t seen in the production system or forms high up in the production tubulars even though the produced fluids are oversaturated further upstream. However, if the well inflow patterns are modified (e.g. by the introduction of ICDs, ICVs and / or slotted liners), this can change the local fluid flow dynamics such that the location of inorganic scaling moves further downhole, often leading to scale formation in the most undesirable places – such as around SSSVs, ICDs/ICVs, ESPs etc. The impact of increases in shear and turbulence can also have important consequences for chemical scale inhibitor treatments or other remedial treatments, which now may have to be applied further downhole in the wells as opposed to in the production tubulars or topside equipment. The impact of kinetics and fluid flow dynamics on inorganic scaling can also require increased volumes of inhibitor to prevent scaling.
This paper focuses on the development of the first truly kinetic model for scale prediction, including fluid flow dynamics considerations and the concept of the Critical Saturation Ratio. The results presented here demonstrate how careful understanding of FFD, and use of a kinetic model can lead to significant improvements in predicting the risk, occurrence and location of inorganic scaling in oil and gas production systems, resulting in significant cost savings when designing scale mitigation treatments.
A Decade of Challenges and Successes in Well Construction at Majnoon Oil Field – Southern Iraq
Authors: Dange Mikhil , Ridha Hussein , Khudair Abdullah Alwan , Cabrera Rodolfo Carlos , Monterio Arnold , Mohamed Sherif , Kumar Animesh 
Published: November 2024

Abstract
Majnoon Oil Field is situated in the southern region of Iraq and stands as one of the world's most prolific oil reserves. Its strategic importance lies in its contribution to meeting global energy demands. However, this field, once a battlefield, presents unique challenges and remarkable successes. Over 70 wells have been meticulously drilled and brought into production. Upon completion, the operator faces the critical task of ensuring well integrity, which hinges on the cement's performance against each section of the casing under scrutiny.
There are two types of well designs with an S-shaped trajectory: slim design and fat design (also known as big-bore). These well designs traverse fourteen separate hydrocarbon-bearing zones, primarily consisting of carbonate and clastic reservoirs with a host of challenges: Lost Circulation: Caverns, vugs, and large fractures,Cement Level Drop: In the intermediate section,Differential Sticking: The UER formation poses challenges,Washout Sections: Several washout sections impact the efficiency of cement displacement.Sustained Casing Pressure (SCP): Gas influx from the Ghar formationShale Instability: The Shiranish formation
Considering the challenges, multiple solutions were engineered from the initial design of cement slurries to the completion of the cement job. To combat lost circulation, a thixotropic and acid-soluble cement was tailored to remedy losses and prevent accidental sidetracking. The fluid parameters of the cement were optimized to minimize fluid loss (less than 10cc) in high-permeable zones. Additionally, shale stabilizers and anti-shrinkage additives were included to enhance the cement properties. A novel spacer system was introduced, enhancing displacement efficiency, and providing competent isolation in the intermediate sections.
Furthermore, the integration of an expandable liner hanger with carefully tuned fluid properties in the production sections, along with computer-aided simulation for analyzing torque and drag limits, ensures the highest cement coverage around the liner. This approach aims to achieve a competent cement sheath and meet the criteria for zonal isolation acceptance.
Case Study of Human Capital Dashboard and Robotic Process Automation (RPA) implementation at HPCL
Authors: Zode V. V. , Sorte D. , Sonawane S. 
Published: November 2024

The contemporary business landscape is undergoing a profound transformation, driven by the relentless march of technological advancement. This digital revolution has permeated every facet of organizational operations, demanding a corresponding evolution in strategic approaches. Among the critical functions within an enterprise, Human Resource Management (HRM) has emerged as a focal point for technological transformation. The traditional role of HR, once predominantly administrative and transactional, is now expanding to encompass strategic business partnering, talent management, and organizational development.
Central to this transformation is the increasing adoption of data-driven insights and automation. Human Capital Management (HCM) systems, with their ability to capture, analyze, and report on human capital data, have become indispensable tools. A subset of HCM, the Human Capital Dashboard, offers a visual representation of key HR metrics, enabling data-driven decision-making and strategic workforce planning.
Concurrently, Robotic Process Automation (RPA) has emerged as a disruptive technology, capable of automating repetitive, rule-based tasks. Its application in HR has the potential to significantly enhance efficiency, accuracy, and employee experience. By liberating HR professionals from mundane tasks, RPA empowers them to focus on higher-value activities such as talent acquisition, development, and employee engagement.
Oil Spill Detection and Understanding Using Deep Learning and Large Language Models
Authors: Tharayil Sarafudheen M. , Qarni Shouq Al , Thubaiti Leen Al , Dossary Basim Al , Shahada Marwa 
Published: November 2024

Abstract
The primary objective of this research work is to harness the advanced capabilities of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLMs), to develop a comprehensive system for detecting and understanding the causes of oil spills. Our approach involves utilizing deep learning algorithms to detect oil spill incidents from images, extracting relevant factors from these images, and feeding these factors into LLMs to determine the causality of the incidents. This research is motivated by the increasing frequency and environmental impact of oil spill events globally, and the lack of existing mechanisms to accurately monitor and explain these incidents. By enabling rapid detection and causality analysis, this system aims to enhance environmental protection efforts and prevent future oil spills through informed decision-making and timely intervention. The methodology of this study involves several critical steps. We began by utilizing an industrial dataset comprising labeled images of oil spills. Initial preprocessing steps included resizing and normalization of the images, followed by extensive data augmentation to enhance the dataset's robustness. We then employed advanced deep learning models, where images are considered as a grid of cells, with bounding boxes. We trained the Convolutional Neural Networks (CNNs) model to identify oil spill incidents by extracting key features from each image. These factors were then fed into a Large Language Model (LLM), to analyze and determine the underlying causes of the oil spills. The study demonstrates the effectiveness of integrating deep learning and LLMs in environmental monitoring and analysis. Our approach achieved a considerable increase in the accuracy of oil spill detection compared to traditional methods. Additionally, we attained a better accuracy rate in identifying contributory factors to oil spills. These results underscore the ecological importance of promptly identifying and mitigating oil spills, highlighting the system's potential to significantly enhance sustainable resource management strategies. By moving beyond traditional methods that focus solely on visual data, our innovative approach leverages LLMs to conduct a comprehensive analysis of oil spill causality. This integration allows for profound insights into the multifaceted nature of oil spills, addressing an urgent environmental concern with advanced AI methodologies.
An Efficient Quantum Neural Network Model for Prediction of Carbon Dioxide CO2 Sequestration in Saline Aquifers
Authors: Rao Xiang , Luo Chenjie , He Xupeng , Hyung Kwak 
Published: November 2024

Abstract
This paper introduces the first-ever assessment method for the dissolution volume of carbon dioxide (CO2) in saline aquifers, based on Quantum Neural Networks (QNN). It explores the application effectiveness of QNN in the classification of reservoir flow issues. The QNN model in this study employs angular encoding and padding techniques to transform input layer data into corresponding quantum states. This is followed by a quantum circuit ansatz with variable parameters, and the output of the QNN is obtained by implementing a Pauli Z operator measurement on the first quantum bit. An optimizer in a classical computer optimizes the cost function of the sample data, updating the variable parameters in the ansatz, thereby achieving a hybrid quantum-classical training of the QNN. This paper designs two dual-variable classification problems for the dissolution volume of CO2 in saline aquifers, investigating the impact of reservoir temperature and mineralization degree, as well as permeability and saturation, on the CO2 storage potential in saline aquifers, with other parameters held constant. It is found that the QNN model in this paper can achieve a rapid decrease in the cost function and a swift rise in the accuracy of both the training set and the validation set within a smaller number of optimization steps. The well-trained QNN model can accurately reflect the influence of reservoir temperature, mineralization degree, permeability, and porosity on the dissolution volume of CO2 in saline aquifers, revealing the significant application potential of the QNN method in reservoir engineering.
CycloneCC: The Technology to Accelerate Industrial Decarbonisation
Authors: Bumb P. , Alhameli F. O. , Al Maqtari A. , Al Blooshi G. , Neeliesetty G. K. , Gillions L. 
Published: November 2024

Abstract
Carbon capture technology plays a critical role in reducing emissions. In retrofit projects, where space constraints are common, shrinking equipment size can offer significant advantages – making carbon capture possible for hard-to-abate industries.
Large, costly, and complex CCUS projects will only chip away at global emissions. Deploying modular, standardised carbon capture solutions across many industrial sites will deliver more for climate mitigation and local economies.
CycloneCC is Carbon Clean's next generation carbon capture technology that makes carbon capture scalable and more affordable – bringing it within reach of a huge number of industrial emitters. The technology overcomes traditional barriers to widespread adoption by reducing the overall capital expenditure (CapEx) and the physical footprint of carbon capture by up to 50% when compared to conventional solutions.
It achieves this through a combination of two proven process intensification technologies – Carbon Clean's advanced, proprietary amine-promoted buffer salt solvent, APBS-CDRMax®, and rotating packed beds (RPBs).
The RPBs contain a disk of packing material which rotates around its axis. The centrifugal force generated through this rotational motion is significantly greater than the gravitational force seen in conventional packed columns, making RPBs far more effective in mass transfer operations.
APBS-CDRMax® has been developed by Carbon Clean and tested at the world's largest CO2 capture pilot and demonstration facilities, capturing up to 240 tonnes of CO2 per day utlising our CaptureX technology. The solvent has been proven to require up to 30% less energy for regeneration when compared to the industry standard 30 wt.% monoethanolamine (MEA), at comparable capture rates. In addition, it has been validated that APBS-CDRMax® has low solvent and ammonia emissions which provides an improved environmental performance relative to benchmarked solvents. Whilst the improved solvent stability to oxidative and thermal degradation pathways, results in lower solvent make-up rates. Overall, the improved performance of the solvent means that operating expenditure (OpEx) of APBS-CDRMax® can be reduced, relative to that of 30wt% MEA.
Built on a pre-fabricated skid mount, every CycloneCC unit can be delivered ready to install, avoiding a lengthy and complex design, build and engineering process. There is minimal disruption to operations and safety risks onsite are significantly reduced compared to conventional solutions.
The technology has been successfully pilot tested at 1 TPD in the UK and US, where it has been proven to reduce the packing volume required to capture CO2 by 10 times. It is currently being demonstrated in the Middle East where it will be capturing CO2 from an industrial facility owned by ADNOC at Fertiglobe, their Fertilizer plant in Ruwais. The CO2 is being captured from Reformer flue gas stack and the captured CO2 is used for Urea production.
Mitigating Drilling Risks with NMR Technology and Oil-Based Mud: A Case Study from Abu Dhabi
Authors: Soliman A. , Deng L. , Elabsy E. , Sharma A. , Hammadi A. A. , Salawu M. , Ali Zuraiqi M. , Al Maskari S. , Seddik I. 
Published: November 2024

Abstract
In this paper, we will discuss the use of Nuclear Magnetic Resonance (NMR) technology in oil-based mud to drill through the overburden section, along with thick shale cap rock. This approach allowed us to land the well in the reservoir and attain important formation evaluation without radioactive source.
Drilling through overbalanced environments and reactive shale layers poses a sizeable danger of having the Bottom Hole Assembly (BHA) stuck, which is increased while the BHA carries a radioactive source. If the BHA becomes caught and can't be retrieved, the radioactive source can be lost or deserted in the wellbore, posing a serious threat to employees, the surroundings, and device. To mitigate those risks, oil-based mud may be used to save you the swelling or weakening of reactive shales, lowering the likelihood of mechanical sticking. Additionally, changing radioactive LWD with NMR can provide similar formation evaluation information even as casting off the risk of dropping a radioactive source inside the wellbore. NMR logging measures the reaction of hydrogen nuclei inside the formation to a carried out magnetic field, presenting accurate data on fluid properties and distributions within the pore space. Logging While Drilling (LWD) NMR is a specific utility of this technology that entails taking measurements even as the well is being drilled.
Our case includes many wells that used the same workflow and BHA to attain their objectives without the need for a radioactive source in an oil-based mud environment. We will reveal how NMR, with oil-based mud, mitigated the threat of shale reactivity and eliminated the risk of having the BHA stuck while containing a radioactive source and achieving well placement as well as formation evaluation objectives. This approach provides a safer and extra efficient method for drilling through complicated geological formations.
NMR is an effective device for formation evaluation. In addition, LWD NMR also can be used for landing wells. By the usage of LWD NMR in conjunction with oil-based mud, it's possible to mitigate the threat of shale reactivity and mitigate the hazard of getting the Bottom Hole Assembly (BHA) stuck whilst containing a radioactive source. This approach offers a safer and even more informative method for drilling and evaluation of complicated geological formations.
Downhole Power Generation: Status, Problems, and Prospects
Authors: Tao Fu , Qinghai Yang , Bairu Shi , Deli Jia , Chenglong Liao 
Published: November 2024

Abstract
Downhole power generation is an important technology for the oil/gas industry. It provides a steady source of power for all types of operations, including drilling and production applications. The technology has been used in the oil/gas industry for many years, but recent advancements have made it increasingly popular and efficient. This paper will discuss the current status of downhole power generation, its benefits, the challenges it's facing, and its potential impact on the industry. Because of the different working environment, technical requirements and energy sources of downhole power generation devices, the way of downhole power generation varies. This paper introduces in detail the existing downhole power generation technologies from the aspects of technical principles, classification, structural design, application status, advantages and disadvantages, focusing on two of the most promising downhole power generation technologies, turbine generator and piezoelectric generator. Based on the current research and application status of downhole power generation technology, combined with the actual production status of the oilfield, recommendations for improvement of downhole power generation technology are proposed. The first is to improve the technical adaptability and reliability of existing traditional equipment based on requirements of field application. The second is to improve the efficiency of power generation by combining multiple power generation methods. The third is to combine power generation equipment with other downhole monitoring/control equipment to develop intelligent self-powered downhole tools. The fourth is to extensively absorb the research results of power generation technologies in other fields and use new materials and new technologies to promote the advancement of high-efficiency and high-energy density downhole power generation technology. With the advancement of the technology, more and more operations can take advantage of reliable, efficient, and safe power generation. This will lead to improved safety and efficiency in operations, increased cost savings and improved efficiency in production, and reduced downtime. Moreover, with more reliable and efficient power available, operations will be more resilient and can better weather disruptions and unexpected events.