bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proppant Pack Stability Enhanced by the Combination of New Fiber Technology and Resin- Coated Proppants
Authors: Salazar Franck , Artola Pedro , Bruno Salguero , Delgado Byron , Aguirre Santiago , Peñaherrera Luis , Boas Jacqueline , Ormaza Nancy 
Published: February 2024

Abstract
Hydraulic fracturing has been used for many years as an effective stimulation method in high- and medium-permeability formations where, in contrast to low-permeability formations, the objective is to create a highly conductive proppant pack that bypasses the formation damage. Proppant flowback can affect the stability of the proppant pack and lose the required conductivity in the near-wellbore area. The present work shows how the proppant flowback was significantly decreased by the combination of new fiber technology and resin-coated proppant (RCP) without affecting the productivity index.
Many methods to prevent proppant flowback have been used successfully, but when challenging production conditions are encountered in high- and medium-permeability hydraulically fractured wells, these methods have been proven ineffective. Low closure stress, high flow rates, high pressure drawdown, multiphase flow, high-viscosity fluids, and stress cycling are some of the conditions that favor proppant flowback. The combination of new fiber technology with RCP has been demonstrated to be effective in controlling the return of proppant in wells that were hydraulically fractured by an operator in Ecuador. The new fibers increase the frictional forces between the chemically resin-bonded proppant pack grains by creating a random network of fibers around the proppant grains. The proppant pack stability is increased by the combination of the two mechanisms. The fibers provide additional particle-to-particle mechanical bonding due to friction increase at the contact point, and the RCP provides chemical bonding.
Initially, wells were treated only with RCP but as more challenging conditions were encountered, RCP by itself became ineffective; then, the new proppant flowback fibers were introduced. The new fibers provided better adhesion between the fibers and the proppant, which significantly increased the proppant pack stability and created a long-lasting conductive proppant pack. The combination of the two methods was implemented starting in 2022 in 15 wells in the Oriente Basin in Ecuador. These wells have reported no proppant flowback under harsh production conditions like stress cycling and high production rates in intervals with few perforations. The incorporation of a more stable proppant pack has allowed the operator to flow back the wells at higher initial flowback rates. This has resulted in a cleaner fracture proppant pack and higher post-fracture production rates.
The combination of RCP and new proppant flowback fibers provided maximum flowback control without affecting the productivity index. The addition of the fibers provides better proppant pack consolidation, and this starts as soon as the fracture closes without requiring waiting for the resin bonding activation by temperature.
Mesophase Technology: An Efficient Breaker System for Thick Sludge Removal and Well Productivity Enhancement
Authors: Alabdulmohsen Zainab , Al-Taq Ali A. , Alsalem Ali , Anioke Chidiebere 
Published: February 2024

Abstract
Sludges and emulsions are common and challenging problems encountered in the near-wellbore area that can adversely impact productivity. Sludges, characterized by their thick, semi-solid mixtures of organic and inorganic deposits, have the potential to accumulate in wellbores, restricting the flow of hydrocarbons and leading to a partial or total loss of well productivity. Mesophase is a patented technology designed to efficiently break down sludges, emulsions, and other organic damage encountered in the near wellbore, thereby mitigating production impairment challenges. This technology is based on microemulsion science, offering high detergency and ultra-low interfacial tension properties. It contains organic acid to dissolve acid-soluble materials, facilitating effective chemical treatment—an intelligent, cost-effective fluid solution with minimal health, safety, and environmental (HSE) risks.
The laboratory work conducted in this study included emulsion and sludge breaking tests, sludging tendency testing, surface and interfacial tension measurements, and contact angle measurements. Analytical analyses, including XRD and SARA (saturates, aromatics, resins, and asphaltenes), were performed to characterize different sludge samples examined in this study. Acid-induced emulsions and sludges were generated using 15 wt% HCl and an oil sample. The impact of the presence of iron ions in the acid system was examined at 3,000 mg/L ferric ions. The lab results revealed that the sludge samples contained mixtures of organic and inorganic materials, including substances like iron sulfide and asphaltenes. The results showed that Mesophase fluid was effective in preventing acid-induced sludge and breaking/dispersing different types of sludges. The contact angle results showed that Mesophase fluid had the capability to interact with both water-wet and oil-wet surfaces, indicating its effectiveness in dealing with organic and inorganic sludges.
This paper presents a novel technology aimed at addressing the pervasive issue of heavy sludge formation in the oil industry worldwide.
AI HSE Monitoring & Digital Value Stream Mapping for Pre-fabrication in Sustainability Project
Authors: Shatiry M Sahir Ahmad , Ros Ikhranizam M , Matlan M Norman , Mazny D. E. Farah Shafiqah M , Chelliah Navaneethan , Asmadi M Noraznan , Chappell Jeremy Spencer , Rahim Zulhasni Abd 
Published: February 2024

Abstract
In construction project management, the most critical part is monitoring and tracking construction productivity. The measurement of productivity in a previous way manually where the process and workers’ productivity are recorded and then analyzed. With the rapid rise of the Industrial Revolution 4.0 (IR 4.0), the application of digital technology will further enhance productivity and continuous process improvement. There is various digital technology available during this IR 4.0; among the technology potentially adopted in the Oil and Gas (O&G) construction is digitalisation through Internet of Things (IoT). With the availability of digital solution in the market, the research study to understand and investigate the application of Digital Value Stream Mapping (DVSM) to monitor and track construction productivity. After that, the investigation was conducted to develop the Digital Value Stream Map (VSM) framework for productivity monitoring and tracking for construction work in Oil and Gas (O&G) activity. Then, to validate the developed DVSM framework through expert interviews and case studies of the actual implementation of the DVSM. The application of the DVSM will come out with the analysis of the existing process flow, then to come out with the future state of the process flow by eliminating the waste of labour and material. Finally, the DVSM model success then the study recommends that the DVSM can further expand to utilize at the offshore environment for tracking and improving the overall construction productivity during the hook-up and commissioning phase for Brownfield and Greenfield projects.
Enhancing Reservoir Potential Assessment with Machine Learning Techniques: A Systematic Approach in The X Field
Authors: Suwandi Albertus , Gaol Ardhi Hakim Lumban , Chandra Steven 
Published: February 2024

AbstractDue to the limited resolution of well logging instruments, particularly older generations, and the presence of complex multiple hydrocarbon systems in a basin, pay zones may be overlooked during measurement and analysis of reservoir characterization. Moreover, while proven and standing the test of time, the conventional approach to stratigraphic correlation and the trial-and-error method for reservoir zone identification are tedious, time-consuming, and expensive. This study proposes a systematic, automated, and accurate reservoir prospect assessment, or reassessment, methodology augmented by machine learning algorithms. The case study investigates S Formation in the X Field, which is marked by a high water cut and the presence of discontinuous parasequence sets. Two machine learning models will be modeled and tuned to X field characteristics. The first machine learning model will assess reservoir stratigraphic relationships, where an unsupervised machine learning model will analyze patterns from log data to geologically describe the reservoir. This can help detect rock formations such as tight shales that conventional interpretation may miss. Third-party stratigraphic data will validate the model results. The second supervised machine learning model will identify hydrocarbon-producing zones. This machine learning model will use resistivity log data and potential zone information from the first model to detect hydrocarbon zones. Swab test data and stratigraphic analysis will confirm machine learning model predictions. The first model successfully captures the stratigraphic patterns of the formation, which aligns with geological validation. The best-performing first machine learning model was able to classify three different clusters of facies, namely shale, sandstone, and shaly sands. The sequence of rock strata and stratigraphic correlations between wells were shown to be consistent with the conventional analysis, with only a minor deviation (0.5-2ft) in the well-to-well correlation sand markers. The second model accurately identifies potential zones based on the given features. The best-performing machine learning model was able to identify the initial condition of the reservoir interval with an accuracy of 84%. Moreover, it could identify a total of 346 potential zone intervals under the same condition. Moreover, as a result of the reservoir re-evaluation using the proposed workflow that has been carried out, a total of 35 candidates for primary perforation and 70 candidates for secondary perforation are proposed in the X-Field. These proposed candidates highlight the results of the workflow implemented to maximize production in the X-Field.
Metaverse: Accelerates Upstream Growth with the Power of Immersive Collaboration
Authors: Al-Harbi Badr , Al-Readean Muhammad , Al-Ghamdi Amell , Al-Akeely Abdullah 
Published: February 2024

Abstract
The global transition towards sustainable future influenced industries to create a fundamental shift in the way they conduct the business to improve sustainability. This paper presents a comprehensive immersive platform that enables multidisciplinary teams and management to make decisions. It connects professionals to collaboratively demonstrate and share findings, run cross-functions business processes in an intuitive way capitalizing on artificial intelligence and cognitive capabilities. All of this is under a unified platform that we called the Upstream Metaverse.
There are four pillars for the Upstream Metaverse: digital assets, real-time collaboration, intelligence, and venues. First digital assets, which include visualization of reservoir simulation static and dynamic properties (e.g., porosity, permeability, water, oil saturation, etc.), other datatypes such as core and well log data. Second is the collaboration and interactive capabilities that allows engineers to be immersed into their models, and run different scenarios. Third is the intelligent layer, which capitalizes on available Industrial Revolution 4.0 (IR4.0) solutions. Fourth is the collaboration venues represented by the Upstream collaboration centers to maximize the user experience.
A synthetic model was used in this paper to demonstrate well placement process, which is one of the Upstream complex processes as it involves many stakeholders. Upstream Metaverse streamlined the process by integrating the key data, and workflows needed for the decision process in a single platform. It enables multiple intuitive visualization methods of well logs to address the complexity. In addition, it provides the visualization of structured data including reservoir models that can be cross-referenced with well logs. Moreover, it provides seamless integration with an intelligent voice assistant to enrich the user experience. These capabilities allow multidisciplinary teams to run different business scenarios, conduct effective field reviews, and collaboratively discuss their findings in an interactive and immersive platform. This allows real-time collaboration around the data for all participants. Furthermore, coupling Upstream Metaverse with digital transformation solutions provides a rich platform that is capable to effectively facilitate the meetings. Also, capitalizing on the integrated data sources, and cross-organization functions combined with streamlined IR4.0 processes enabled on spot decision making.
The Upstream Metaverse established the main building blocks that are fully integrated with data repositories to address current and future challenges. This will directly increase the efficiency of conducting Upstream cross-functions operations through a single, unified, immersive, and collaborative platform. Upstream Metaverse is a sustainable environment that reduces the need for sequel meetings, supplemental silo efforts, and computation resources.
Considering Shear Dilution and Threshold Pressure Gradient Heavy Oil Reservoir Nonlinear Numerical Simulation and Residual Oil Characterization
Authors: Gao Yue , Ma Kuiqian , Liu Jing , Ling Dennis , Yang Bin 
Published: February 2024

Abstract
A large number of experimental results show that, the flow of heavy oil in porous media does not conform to Darcy seepage law. Heavy oil has the characteristics of threshold pressure gradient and its viscosity decreases nonlinearly with the increase of velocity. Heavy oil is a non-Newtonian fluid, but currently commercial reservoir numerical simulation simulators (ECLIPSE, CMG) do not have nonlinear numerical simulation function for heavy oil. In this paper, a new Hg Equation is introduced to match the nonlinear relationship between heavy oil viscosity and velocity, and a new heavy oil nonlinear reservoir numerical simulator is developed considering the threshold pressure gradient and shear thinning characteristics of heavy oil.
Based on the basic data of the SZ Oilfield, this paper established a well group conceptual numerical model, and used the newly nonlinear numerical simulator for heavy oil to study the effects of the threshold pressure gradient and shear thinning of heavy oil on the laws of remaining oil distribution and development characteristics. The numerical simulation results show that the remaining oil distribution law of the model considering the nonlinear seepage characteristics of heavy oil is very different from the normal model, which is reflected in the lower sweep coefficient and the lower remaining oil saturation of the mainstream line. Based on the numerical simulation results, this paper proposed an effective pressure gradient to evaluate the sweep range of heavy oil in the reservoir and an effective well distance calculation method is proposed. In the development practice of SZ oilfield, this method has achieved very good application results.
The novelty of this paper is that considering the characteristics of threshold pressure gradient and shear thinning of heavy oil, a new nonlinear reservoir numerical simulator for heavy oil is developed. The Hg equation is proposed to match the nonlinear change of heavy oil viscosity with shear rate. Based on the numerical simulation results a new calculation method of effective well distance is proposed.
Machine Learning Classifier for Detecting Non-1D Features Using Deep Directional Resistivity Measurements
Authors: Zhong X. , Jith J. , Chang Y. , Gremillion J. 
Published: February 2024

Abstract
Currently, most geosteering jobs can rely on 1D inversion to accurately capture the properties of multiple planarly layered formations in real time. However, when complex non-1D features are present in the formation, higher dimensionality inversions will be required, which need intensive computation resources and more data channels containing 3D information sent to the surface. When abnormal results from 1D inversion are observed, switching telemetry rates and starting higher-order inversions is usually late, which inevitably delays the real-time geosteering decision.
To address this, we have developed a machine learning classifier capable of early detection of non-1D features in the formation before the drill bit reaches the higher dimensional feature. A large number of synthetic formations were generated with and without non-1D features that included faults, water coning, and sand injectites, etc., and then fed into a 3D forward model to generate the deep directional resistivity measurements. We trained the classifier to distinguish between 1D and non-1D cases with high accuracy, specifically employing a multilayer perceptron classifier, which is both accurate and suitable for downhole deployment.
In practice, this classifier can signal the presence of non-1D features, dynamically select telemetry channels, and initiate specialized inversions in real time. Synthetic and field testing validated its effectiveness, achieving over 80% accuracy when the tool is even 10 m from a non-1D feature, with increasing accuracy as the tool approaches the feature. Field results aligned with geological interpretations, affirming the model's robustness and accuracy.
This innovation enhances the ability to recognize complex formations during drilling, automating non-1D inversions and improving bandwidth management in real time, which ultimately increasing drilling speeds. This is one step in the journey towards autonomous geosteering.
Enhancing Predictive Maintenance in an Oil & Gas Refinery Using IoT, AI & ML: An Generative AI Solution
Authors: Saboo S. , Shekhawat D. 
Published: February 2024

Abstract
Oil and gas refinery operations are under constant pressure to enhance efficiency and ensure uninterrupted processing. The adoption of predictive maintenance strategies has emerged as a pivotal solution, enabling real-time anomaly detection, predicting pressure fluctuations, and monitoring asset health. An illuminating example hails from a downstream operator in Western Australia that strategically harnesses the power of IoT and AI/ML. For them, revenue hinges on the streamlined delivery of gas processing services to customers, amplifying the significance of process efficiency gains. Leveraging on-site equipment data analysis, this approach significantly minimizes on-site maintenance requirements and automates back-office tasks, reducing manual data analysis and response generation in maintenance permit systems. The technical infrastructure involves wireless sensor-enabled data collection transmitted to a centralized hub, where machine learning algorithms detect equipment defects. Rapid reporting of these defects to decision-makers, accompanied by contextual insights, empowers swift, informed decision-making. This innovative solution has expanded business horizons, enabling the processing of gas for external entities alongside producing their reservoir gas in the downstream processing plant.
Analytical and Numerical Investigations of the Impact of Anisotropic Elastic Properties on Hydraulic Fracture Geometry
Authors: Shawaf A. , Dehdouh A. , Rasouli V. 
Published: February 2024

AbstractThis study investigates the impact of transverse isotropic vertical (TIV) characteristics on fracture geometry, spacing, and stress shadow development in shales. Shales exhibit transverse isotropic characteristics due to their rich organic content and laminated depositional environments. The lamination planes are horizontal in shale formations having a symmetric axis vertical to the bedding plane. Neglecting the TIV nature of shale formations leads to erroneous in-situ stress estimates, resulting in inefficient fracture design and reduced recovery. The study employs analytical modeling and numerical simulations to analyze the effects of TIV medium properties. Analytical modeling shows that Young's modulus anisotropy significantly impacts fracture width, while Poisson's ratio has minimal influence. Stress anisotropy is also examined, revealing that high-stress anisotropy allows for closely spaced fractures, theoretically eliminating minimum spacing concerns. Numerical simulations confirm that higher anisotropic stiffness reduces fracture width in TIV formations, aligning well with analytical modeling results. Wide fracture spacing produces a uniform and symmetric geometry, while narrow spacing results in non-uniform and asymmetric growth, with some fractures failing to initiate. Fracture turning due to stress anisotropy was also investigated, and the fracture propagation results clearly demonstrated the starting of fracture turning between 1000 to 1500 psi stress anisotropy. Micro-seismic was used to validate and calibrate fracture geometry. Economic-based optimization determines the optimal fracture spacing.
Engineering Innovation of Sustainable Ideation for supper Corrosive Environment CO2 - First Non-Portland Cement for Primary Barrier in Middle East and First Non-Portland Cement Application for CCS well in World.
Authors: AL Menhali Shamsa Jameel , Ibeziako Chuma , Ramadhan Taha Mohamed , Al Sabi Haya Jamal , Arbadov Gurbandurdy , Anwar Mohamed , Paun Bogdan , Ziganshin Ramil , Al Shamsi Eissa , Hamdy Ibrahim , Sumaida Ali Sulaiman Bin , Hamdan Tariq , Abdelazim Mohamed Farouk , Siqing DR. , El Youssef Bassem , Saputra Tarmiji Darma , Abuirshaid Shadi , Amer Ala’a , Alade Ayokunle , Tambe Romana , Permana Erik , Zulkifli Amul Husni , Khan Pattan Noor Ahamed , Escalante Jose , Sandra Dewi Novita , Aljadid Mohamed , Siddiqui Faisal , Qassem Mohamed 
Published: February 2024

Abstract/Objective
The challenges associated with the construction of CO2 injection well for carbon capture sequestration (CCS) are cross disciplinary segments, but highly manageable. Starting from Site selection, reservoirs identification, drilling well, cement operation execution, geological formation evaluation and reservoir specialists that ensure detailed evaluations of the subterranean environment.
Improving sustainable Wellbore barrier CO2 Injection Wells required carful design cement slurry that can tailored withstand drilling risk (Mud Losses/Stuck/) and maximum storage in corrosive environments to enable carbon capture storage. Additional, work reviewed on enhancing properties Portland cement were by reducing permeability of cement sheath or lowering the concentration of materials in the cement matrix that react with CO2 or replacing the Portland cement with specialty cement to sustain long term CO2 well injection.
Effort have focused on sustainable technology to look further promote long term well seals to deploy non-Portland for the first time in casing with fiber optics installation along 9 5/8" intermediate section for better data acquisition system (DAS) across the aquifer, successful coring formation, mini frac operation and cement across aquifer with low fracture gradient tolerance in UAE.
This paper discusses a novel first application new sustainable solution of a cement selection in a supper corrosive environment CO2 injection well in Middle East general and UAE specially. Additionally, reviews the first CCS appraisal well in Syncline area targeting the deep saline aquifer formation CO2. This appraisal well is aimed to become an enabler for developing a saline aquifer CCS business/Commercial model. On the other side, the well is designed to provide operational learning and de-risking for potential future large scale CCS project and will be kept as part of a future large scale CCS project where possible.