bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Evaluation of Seawater-Based Fracturing Fluid with High-Viscosity Friction Reducer and Different Polymers
Authors: Alessa M. M. , Alarifi S. , Murtaza M. , Aladi A. 
Published: November 2024

Abstract
Hydraulic fracturing, especially when employing water-based fluids, demands a substantial amount of water resources. Given the scarcity of freshwater sources in Saudi Arabia, the utilization and transportation of freshwater for hydraulic fracturing operations have become excessively expensive. Since 2012, efforts have been underway to explore the feasibility of utilizing seawater (SW) as the main component for fracturing fluid in Saudi Arabia. The paper aims to utilize seawater to replace freshwater as the base fluid in slickwater.
The study conducted a total of 22 experiments comparing DI water-based slickwater and seawater-based slickwater in different High Viscous Friction Reducer (HVFR) concentrations. After selecting the HVFR concentration with the best rheological performance in seawater, two polymers which are HPG and CMHPG were tested before the addition of Zr-Crosslinker using Anton Paar-MCR 702e high-pressure, high-temperature (HTHP) Rheometer under a consistent pressure of 500 psi, a shear rate of 100 1/s, and varying temperature conditions (70°C and 120°C).
In the first part of the study, different concentrations of HVFR were tested in both DI water and seawater (SW) at 70°C and 120°C. In SW, at 70°C, two of the concentrations showed promising results, while at 120°C, neither concentration maintained reliable viscosity. Conversely, all concentrations performed well in DI water. After adding the two polymers, HPG showed decreasing stability at higher temperatures in DI water. In contrast, at elevated temperatures, CMHPG exhibited greater stability compared to HPG and generally demonstrated higher viscosity. It was observed that in DI water, HPG is always the dominant polymer. On the other hand, in SW, at lower temperatures, HPG outperforms CMHPG in terms of viscosity and stability. However, at higher temperatures, CMHPG is the better polymer. Additionally, after adding the Zr crosslinker, viscosity and stability were improved for both polymers.
This paper presents novel insights into the utilization of seawater as a base fluid in slickwater hydraulic fracturing operations in Saudi Arabia. By comparing the rheological performance of seawater-based slickwater with freshwater-based slickwater, and exploring the effectiveness of different polymers and crosslinkers in seawater environments, this innovative strategy offers significant potential for economic savings while promoting sustainable water management practices in the petroleum industry.
Microstructural and Petrophysical Properties of Fault Rock from Outcrops in the United Arab Emirates
Authors: Al Hashemi Hussain H. , Fisher Q. J. , Yielding G. 
Published: November 2024

Abstract
There are many studies covering the RAK and Al-Ain area, but most of these studies focus on sedimentology, sequence stratigraphy and some structural events were conducted by ADNOC, ExxonMobil and PDO in the north of Oman. To date, no comprehensive field study of fault sealing has been conducted on these outcrops or in any place in the UAE. Fieldwork was undertaken in the valley/Wadi Rahabah, Wadi Al Bih and Jabal Hafit, which provides a good analogue for the fault in the Shuaiba, Thamama and Khuff reservoirs in the Abu Dhabi Onshore (OS) field. The results provided evidence that all fault rocks from outcrop exposures could act as conduits in the damage zone (DZ) and baffles in the Fault core (FC). The permeability contrast between fault and host within the reservoir suggest that all fault rocks from outcrop exposures could act as baffles in the DZ and seals in the FC. The FC is cut by open fractures, and thickness varies significantly over a short distance in the outcrop. If such fractures are present in the subsurface, they could allow the DZ to act as a conduit to flow and the fault could act as a baffle. The variation in the FC thickness ranges from 2 cm to 0.5 m within a short distance of around 10 m. In addition, the ranges span from 4 to 30 cm within a short distance of around 2 m from different outcrops, limiting the extent to which the FC can act as a seal. Three types of microstructural textures of FC were found in the UAE outcrops: first, a breccia located in the Jilh formation; second, a micrite-dominated located in the Khuff formation; and third, calcite cement located in the Shuaiba and Rus formation.
Prediction-to-Production: Implementing Machine Learning for Behind Casing Opportunities
Authors: Zaizakrani M Farid , Suali Dvercillia , Sidek Sulaiman , Surin Nicholas Aloysius , Jeffry Suzanna Juyanty M , Shah Jamari M , Hazali Nurdini Alya , Mokhlis Mohamad Mustaqim , Muniandy Satyaraj , Saifuddin M Nabil 
Published: November 2024

Abstract
In the Oil and Gas (O&G) industry, maximizing production from existing wells is crucial to staying competitive. Identifying and accessing Behind Casing Opportunities (BCO) for Production Enhancement and Idle Well Reactivation (PE-IWR) initiative, provides a cost-effective alternative than drilling new wells to increase the recovery and extend the life of a well. However, accurately predicting BCO in mature offshore fields, particularly in Field S located in offshore East Malaysia, remains a formidable challenge due to complex geological formations, remote locations, and aging infrastructure which have resulted in ambiguous logging results and high operational costs that complicate data collection and reduce the reliability of conventional logging methods.
The purpose of this paper is to discuss an innovative approach to predict BCO by leveraging Artificial Intelligence and Machine Learning (AI-ML) as a pre-screening tool for identifying PE-IWR candidates, showcasing a comprehensive analysis and method to validate the accuracy of the result predictions using Field S as case study. The BCO identification solution has expedited the discovery of multiple promising candidates, from previously untapped areas, resulting in a swift realization of its benefits and has made the decision-making process much easier. One case that stands out is the discovery of promising BCO in Well-01 of Field S, which indicated significant potential oil gain. A collaborative effort between the engineers and Subject Matter Experts (SMEs) was initiated, with the assistance of the solution, to quickly capitalize on this opportunity that involved comprehensive subsurface technical assessment, including detail analysis of well logs, investigation of well intervention and integrity history, evaluation of reservoir performance, and review of well production behavior.
The successful implementation in Field S, marked another favorable outcome that demonstrates the effectiveness and practicality of the AI-ML solution in enhancing BCO identification to optimize production performance and increase profitability. These implementations not only empower engineers to uncover hidden reservoir potentials but also foster a proactive approach to well and reservoir management and surveillance through real-time data driven approach.
A Digital Platform to Overcome Challenges when Analyzing Real-Time Drilling Performance with Surface Rig Parameters
Authors: Falini E. , Brillaud L. , Mahjoub M. , Menand S. 
Published: November 2024

Abstract
The opportunity to improve drilling performance can be capitalized on, by timely and accurate representation of the physics applied to the bit while drilling. The mechanical specific energy (MSE) has usually been used to recognize potential drilling dysfunctions, unfortunately, without direct downhole measurements, this indicator is often poorly estimated. Real-time measurements such as weight-on-bit (WOB), torque-on-bit (TOB) and motor differential pressure, among others, can be obtained downhole and transmitted to surface while drilling with specialized tools, and when high data density is required, these tools are deployed with wired pipe. The cost associated with these technologies are hard to justify for the vast majority of development work around the world. In many cases, only surface measurements are available in real time, but these measurements are subject to several sources of bias, which if not properly processed, could lead to wrong interpretations. Due to these limitations, downhole drilling parameters could be better determined with algorithms. The goal for this publication is to explore the challenges faced when using drilling surface data and propose a new methodology to improve real-time identification of drilling dysfunctions.
The first step of the process consists in, using surface measurements to identify and categorize at any given time, every single operation taking place at the rig. This rule-based algorithm segments the operational sequence, then takes surface measurements from several operations to automatically derive downhole drilling parameters such as WOB, TOB, and/or motor differential pressure. Finally, the MSE and other indicators such as drill strength (DS) are calculated and intuitively displayed to evaluate current drilling performance, facilitating the implementation of optimum drilling parameters.
Miscalibrations of weight-on-bit and differential pressure was identified after analyzing several past wells datasets, and in most of cases, torque-on-bit was missing. By applying the algorithm, accurate recalculation of these downhole drilling parameters from surface data was performed. The results after the recalibration, showed a very good correlation with actual downhole data, when compared to available measurements captured with downhole BHA sensors. Moreover, these accurate results were then used to identify drilling dysfunctions and to recommend optimized drilling parameters for the next well. Additionally, this methodology is used in a real-time operating center, advising rigs with ongoing drilling operations.
This algorithm, not only allows accurate estimation of downhole drilling parameters in real-time but, can also be applied to old datasets from offset wells. By removing human calibration errors, this methodology enables better understanding of the physics occurring at the bit. As our industry makes progress into drilling automation, this new model promotes consistency and performance.
Numerical Analysis of Hydro-Mechanical Behavior of Faults: Impact of Hydraulic Conductivity Variation on the Pressure Buildup Due to Co2 Injection
Authors: AHN Kyeunghye , BOURGEOIS Frederic , POUYA Amade 
Published: November 2024

Abstract
This study aims to create a advanced numerical model to estimate the effect of fault reactivation caused by carbon dioxide (CO2) injection on the pressure field in nearby rock formations. The study also evaluates the hydro-mechanical stability of adjacent faults under different pressure conditions during underground CO2 injection. The investigation explores the hydro-mechanical response of faults, especially the changes in hydraulic conductivity with fault opening and how they influence fault reactivation.
Our methodology uses zero-thickness interface elements in a Finite Element Method, with adequate constitutive model, to precisely simulate single-fault planes behavior during coupled hydro-mechanical processes. This approach improves existing models in rock mechanics by allowing accurate sensitivity analysis of fracture transmissivity to normal stress variations and shear displacement. We incorporate Poiseuille's cubic law and also thin layer model law to account for changes in hydraulic conductivity due to mechanical deformation. The fault mechanical behavior is described using an elasto-plastic nonlinear model, which effectively captures the complex interdependencies between elastic and plastic strains under variable stress and fluid pressure conditions.
Using the Finite Element software, our numerical simulations provide important insights into the behavior of faults under seismic and aseismic conditions induced by CO2 injection. The study investigates various injection scenarios, focusing on the pressure buildup and subsequent dissipation during rest periods, with particular emphasis on the interaction between fault and reservoir permeabilities. Our findings reveal significant variations in pressure dissipation patterns, depending on the mechanical properties and initial permeability of the geological formations. These results contribute to a deeper understanding of pressure distribution dynamics and fault reactivation processes, offering valuable implications for managing induced seismicity in CO2 sequestration projects.
This study presents a new and comprehensive modeling approach to estimate fault reactivation and its hydro-mechanical consequences within CO2 injection contexts. By integrating mechanical and hydraulic factors in fault behavior analysis, the research enhances our understanding of fault responses to induced pressures, thereby aiding in the development of more effective risk assessment and mitigation strategies. The introduction of advanced numerical techniques and detailed sensitivity analyses provides a robust framework for future studies on fault stability and induced seismicity.
Flow Performance of Two-Phase Fluid in Carbonate Fractured-Vuggy Media under HTHP Conditions
Authors: Sun K. , Zhang G. , Jiang Q. , Fu J. , Liu Q. , Li R. , Jia W. , Zhang M. , Kong L. 
Published: November 2024

Abstract
In the middle-late stage of depletion development, water influx and channeling are usually unavoidable in deep carbonate fractured-vuggy reservoirs, which leads to a rapid increase in water cut of production wells, thus significantly reducing the total oil recovery. This study focuses on flow performance of oil and water in carbonate fractured-vuggy media under high-temperature and high-pressure conditions, the two-phase flow characteristics of porous vs. porous-fractured media and fractured vs. fractured-vuggy media are investigated and compared respectively based on lab tests. It is found that oil recovery of waterflooding in the porous-fractured media is only 37.36%, which is much lower than that in the porous media (60.69%), indicating that the presence of fracture can greatly reduce the co-seepage time of oil-water and result in a decrease of total oil recovery. The oil recovery of the fractured and fractured-vuggy media is 85.97% and 53.09%, respectively. The quite obvious difference shows that when the injected water flows into the vug structures along the fracture at a pretty low speed, only part of the oil can be displaced out by the effect of gravity differentiation. The other rest of oil would be widely distributed in the upper part of the vug structures and sealed as remaining oil, which is quite difficult to be driven out. This study reveals the flow law of two-phase fluid in fractured-vuggy media under HTHP conditions, providing novel insights into flow mechanisms of carbonate reservoirs with different scales of flow and storage structures.
Analysis of Clustering Machine Learning Algorithms and Generative Artificial Intelligence Tool for Visualization and Interpretation of Seismic Data
Authors: Supekar N. , Peddada S. , Reid J. 
Published: November 2024

Abstract
Seismic attribute analysis is often confounded by the complexity of the subsurface. Clustering machine learning algorithms reduce complexity and bring the most salient features of the seismic datasets to the fore. By conducting a comparative study of clustering algorithms applied to 2D seismic data, we can determine a set of optimal methods for visualizing particular seismic attributes. This paper presents an analysis of clustering algorithms, and a generative artificial intelligence-based large language model (LLM) customized for interrogating seismic data.
Based on data partitioning, density and distribution category, K-means, DBSCAN, and Gaussian Mixture Modeling (GMM) algorithms were used to analyze amplitudes in 2D stacked seismic data. To assess cluster quality, performance metrics such as the Silhouette index and Davies-Bouldin Index were determined. The analysis revealed that seismic data can be effectively divided into clusters based on the amplitudes and frequencies along each of the traces and times. These clusters allow interpretation geophysicists to readily identify potential geohazards such as shallow gas, faults, or other amplitude anomalies. It was observed that K-means successfully divided the data into clusters in a manner which was sensitive to the overlapping data and noise. GMM exhibited a probabilistic feature of partitioning the data effectively. DBSCAN and GMM provided well distributed and identifiable clustering and fitting of amplitudes.
The choice of clustering algorithm depends on the data size, data complexity, and performance metrics. Clustering is often regarded as a foundational technique in various types of data analysis. It may allow interpretation geophysicists to readily discern prominent and physically relevant amplitudes, amongst other seismic attributes. Further work, detailed in section 2 of this paper, focuses on developing a generative AI-based model that can function as a custom-made domain-interacting tool. This serves as a basis for developing additional tools through fine-tuning and additional training of AI models. These models then can be trained to predict subsurface structures and help processing/interpretation geophysicists to review and understand seismic data from a different perspective.
Scaling Autonomous Inspection Robots For Industrial Facilities: Challenges and Opportunities
Authors: Gjerde K. 
Published: November 2024

Abstract
Autonomous inspection robots have emerged as pivotal assets in enhancing operational efficiency and safety within industrial facilities. This paper explores the challenges and opportunities in scaling the deployment of such robots across diverse industrial environments.
Firstly, we delve into the technological advancements driving the evolution of autonomous inspection robots, including sensor integration, machine learning algorithms, and real-time data processing capabilities. These advancements enable robots to navigate complex environments, detect anomalies, and perform detailed inspections with high accuracy.
However, scaling the deployment of autonomous inspection robots presents multifaceted challenges. These include the need for robust infrastructure to support fleet management, efficient recharge, and seamless integration with existing industrial systems. Additionally, ensuring compliance and addressing cybersecurity concerns are imperative for widespread adoption.
Despite these challenges, the benefits of scaling autonomous inspection robots are substantial. Enhanced operational efficiency, reduced downtime, and improved worker safety are among the primary advantages. Moreover, by leveraging data collected by these robots, industries can make data-driven decisions, optimize maintenance schedules, and prevent costly equipment failures.
In conclusion, while scaling autonomous inspection robots in industrial facilities presents complex challenges, the potential benefits outweigh the hurdles. By addressing these challenges through interdisciplinary collaboration and technological innovation, the widespread deployment of autonomous inspection robots can revolutionize industrial operations.
Analyzing Methane Emissions Using Satellite Imagery and Artificial Intelligence
Authors: Schillaci Guido , Fiorucci Marta , Bonacchi Luigi Bono , Pencelli Manuel , Politano Andrea , Agostino Antonino , Pau Daniele , De Magistris Giovanni , Hanjra Raman , Sachdev Dheeraj , Parrella Ilaria 
Published: November 2024

Methane has a significant impact on climate change as it is a potent greenhouse gas. The energy sector is responsible for 30% of methane emissions worldwide. Effective and continuous monitoring and mitigation of methane emissions are crucial in mitigating the increase of global temperatures. However, current methane emissions monitoring technologies present challenges due to their reliance on the deployment of sensors and personnel near the emission sources, which can be difficult and costly, particularly in the case of large-scale and remote industrial facilities such as those in the energy sector. Methane emissions from these facilities can span vast areas, making traditional monitoring methods time-consuming and resource intensive. Therefore, there is a pressing need for advanced technologies that can provide accurate and real-time methane emissions data from remote and hard-to-reach locations.
The dispersion of methane over large sites poses challenges in the emission measurement and quantification. Satellite imagery, such as those provided by the National Aeronautics and Space Administration (NASA) or by the European Space Agency (ESA) with the Copernicus program, can provide a global map of methane concentrations every day. However, interpreting and analyzing such data is still an open challenge.
The contribution of this work is multifold. First, it analyzes how the adoption of artificial intelligence and computer vision techniques can support the monitoring and quantification of methane concentrations from satellite imagery, with the aim of supporting mitigation activities for reducing methane emissions. State-of-the-art computer vision and artificial intelligence methods for methane emission quantification using satellite imagery are reviewed. A particular focus is given to methods for enhancing the accuracy of methane quantification using satellite imagery, and for addressing the lack of ground truth data for assessing the quality of the measurement.
Second, this paper provides a review of existing data sources for methane emission monitoring and quantification. Satellite imagery datasets from public and private entities are reviewed, as well as tabular datasets that contain processed information from satellite imagery about methane emitters and their geographically referenced information.
Renewables for Subsea Power – Successful Completion of Transformational Low Carbon Subsea Power Project
Authors: Slorach P. , Robertson G. , Douglas A. , Crossland I. , Retzler C. , Money N. , Birch Michael 
Published: November 2024

Abstract
The Renewables for Subsea Power (RSP) project is a first-of-its-kind commercial-scale demonstrator project, presenting a novel way to provide fully autonomous offshore power generation from renewable energy, delivering a cost-effective and flexible alternative to power and communications from shore for operating subsea equipment. The project showcases how net zero can be enabled in an offshore environment through the reduction of greenhouse gas emissions associated with power delivery to assets.
RSP has completed a three-phase program to demonstrate the delivery of low-carbon power and communications to assets offshore. After competition of Phase 1 (a detailed front-end-engineering-design (FEED) study) in 2021, Phase 2 consisted of system communications bench testing, assembly of the subsea systems, and onshore commissioning of both the wave energy device and energy storage system.
Phase 3 included integrated system deployment off the coast of Orkney in the UK North Sea, combining a wave energy converter and seabed energy storage system for local, reliable subsea power and communications delivery to a subsea electronics module and a residential underwater vehicle.
This paper builds on the findings of SPE-216124 (Slorach et al., 2023) with particular focus in this paper given to the demonstration of power export to the subsea energy management system, which provides power to both subsea controls equipment and the underwater vehicle residency. This has been demonstrated through a remote wireless communication link to the subsea equipment.
The conclusion of the at-sea trials has provided evidence of the benefits of directly integrating renewable power into subsea systems. The offshore trial has also provided invaluable lessons learned from over a year of at-sea operation, covering an extensive testing regime including all aspects of seasonal environmental impact on the equipment. These will lead to commercial deployments planned for 2024/5.