bluuebunny/crossref_metadata_2025_split huggingface dataset:
Successful Implementation of Integrated Forecasting Tool for Complex Near Critical Jurassic Reservoir Fluids of North Kuwait
Authors: Al-Enizi Nawaf , Ameer Ali Abbas , Al-Saffar Ali , Khalaf Sara Ben , Abdullah B. , Rao Narhari , Karamahmut Umut , Rice John 
Published: November 2024

Abstract
Gas Fields Development asset in Kuwait Oil Company (KOC) has recently achieved the distinction of generating an Asset Action Plan (AAP) utilizing a new forecasting tool. In the past, the AAP was generated by a tool developed with visual basic functions in Excel, relied on the vendor for permanent code maintenance, and lacked full control of KOC staff. The management steer was to ensure activity continuity in KOC by regenerating AAP with an industry-standard commercial tool.
An immediate need for utilizing an industry-standard AAP forecasting tool was initiated. The functional specifications required by the asset were shared with several vendors. After rigorous review, AAP new tool was selected as it satisfied most modeling requirements such as the ability to handle a high number of wells over many fields and reservoirs, flexibility to run multiple development scenarios, and reduced run time that will enable sensitivity runs to be completed within limited preparation schedule. A comparison of the previous tool's outputs with the new tool of one of the previous year's results showed similar results with much less effort.
AAP tool takes subsurface dynamic simulation model results, drilling, and facilities schedules as input through the Data Management Tool (DMT) and the Decline Curves Extractors (DCEs). The DMT sheet is the main interface for all the dynamic model data while the DCEs are the well-specific decline curves initially grouped to form equivalent tank decline curves based on reservoir pressure performance.
After the completion of one of the legacy AAP P50 scenario using new tool, AAP new tool and the previous tool's forecast profiles were compared. The resulting profiles and cumulative production are similar. The asset strategic gas plateau rate from AAP new tool vs the previous tool was very similar.
The differences are within the calculations tolerance showing the new and previous tools resulted in a similar forecast. The cumulative volumes are up to the economic threshold of 50 MMscf/d. Major advantage of the new tool is fast run performance which requires ~ 1/3 of the time required for the previous tool.
After completing the testing phase with the new tool, further improvements were agreed with the vendor. New functionalities will make AAP a standalone, automated tool that is capable of linking to official AAP platform of KOC, Corporate Portfolio Management System (CPMS). Post processing of the AAP profiles including generation of opportunity profiles and calibration to short term production forecast (STPF) are additional improvements. This results in generation of an optimized drilling sequence.
Advanced Geothermal Well Testing Proves Sustainable New Energy Potential
Authors: Pham T. , Kaipov Y. , Maurya A. , Alawagi A. 
Published: November 2024

Abstract
In the past decade, the oil and gas industry has been progressively shifting towards renewable energy sources. Companies within the sector are exploring a variety of renewable technologies with the goal of achieving carbon neutrality by mid-century. Among these initiatives, geothermal energy has emerged as a significant focus in recent years.
Geothermal well testing, akin to well testing in the oil and gas industry, is crucial for evaluating the economic viability of geothermal projects. Despite the similarities, there are fundamental differences in the hardware used for geothermal and oil-and-gas well testing. This necessitates a re-evaluation of existing geothermal well testing techniques to ensure safety and data integrity.
This paper analyzes the technological gaps in conventional geothermal well testing and introduces advanced well testing technologies and equipment designed to enhance the performance evaluation of geothermal wells. A novel approach to geothermal surface well testing has been developed, significantly improving the efficiency of reservoir performance assessments. This was achieved through a combination of redesigning well testing equipment and workflows, and digitalizing data acquisition, integration, and calculation processes.
A hybrid design, incorporating the best features of both conventional geothermal and oil-and-gas well testing, was employed to ensure operational safety, particularly in controlling unknown H₂S. The use of digital electromagnetic flow meters, alongside traditional weir- box methods for flow rate measurement, was key to ensuring data accuracy and redundancy. The real-time integration of digitalized surface well test data with downhole electrical submersible pumps (ESP) was critical for the rapid evaluation of well test data and making necessary adjustments to the testing program in real-time.
These advancements not only enhance the safety of geothermal well testing operations but also significantly improve data acquisition and the evaluation of well potentials. This paper discusses the development of these advanced well testing technologies, lessons learned, and recommendations for further system improvements.
Simultaneous Determination Technology for Water Content and SARA Group Composition of Oil in Water-Oil Emulsions Using NMR Relaxometry
Authors: Fazlyyyakhmatov M. , Bakhtigaraev R. , Varfolomeev M. 
Published: November 2024

Abstract
The paper presents a technology for the quantitative analysis of water-oil emulsions using low-frequency pulsed NMR relaxometry, which allows for online monitoring of water content and the SARA group composition of oil. This technology can be applied in hydrocarbon production to determine the effectiveness of enhanced oil recovery methods at a specific well and/or field by controlling water content in real-time to determine the quantity and rate of demulsifier addition in separation units and facilities for desalting and dehydrating emulsified oil. Additionally, it can be used to enhance the efficiency of crude oil processing at petrochemical plants.
The study presents the results of examining samples of artificial water-oil emulsions of heavy oil with water content ranging from 10% to 50% by mass. It is shown that the method can determine water content up to 40% with an uncertainty not exceeding 15%, without affecting the quality of the oil's group composition determination.
Using Ultra-Low Emission Burner for Zero Gas Flaring in Oil Fields
Authors: Magomadov I. A. , Balhasan S. , Wahid F. , Izturganov T. B. , Iskakov R. , Hamad A. , Lyakhovskaya V. , Hassan Z. , Duot M. , Ame Y. , Sageer I. 
Published: November 2024

Abstract
This study introduces an innovative ultra-low emission burner designed to eliminate gas flaring in oil fields, contributing to global zero-flaring objectives. The burner integrates advanced combustion technologies and surface combustion to significantly reduce harmful emissions, including NOx and CO, by more than 90%. Through mechanical design refinement and extensive field testing, the burner has demonstrated high adaptability across diverse operational conditions, ensuring minimal disruption to existing workflows. The use of previously wasted flare gas to generate energy has resulted in increased overall efficiency, aligning oil field operations with stringent environmental standards and sustainability goals. A case study conducted in a residential area demonstrates the ULEB's effectiveness, reducing CO2e emissions from 1440 metric tons (venting) to 158 metric tons per day. The burner's design ensures adaptability to various operational settings and minimal interference with existing infrastructure. The findings from the case study highlight ULEB's potential to contribute to sustainable energy practices and compliance with stringent environmental standards, positioning it as a critical solution for the oil and gas industry.
Using ML-Supervised Learnings Based-Algorithms to Create a Relative Permeability Model
Authors: Aggarwal Akshay , Kumar Saket , Kumar Subbiah Surej , Bajpai Prakash 
Published: November 2024

AbstractRelative permeability analysis in the field begins with compiling Special Core Analysis (SCAL) experimental data on core samples. Conventional methods categorize samples by parameters, including rock quality index (RQI), flow zone indicator (FZI), or Winland R35, based on porosity and permeability. Samples are binned by parameter ranges, and collectively analyzed to derive representative permeability curves. The curves receive endpoint analysis, normalization, and denormalization for different rock type bins as per the previously mentioned parameters. However, this relative permeability analysis is a tedious task and requires significant time. Hence, this paper presents a robust and effective machine learning (ML) based approach to derive the relative permeability data sets readily for reservoir engineering study tasks.This paper presents a Machine Learning (ML) based approach by compiling a database of laboratory-derived SCAL experiments. Thirty-seven experimental oil-to-water relative permeability datasets were collected, which comprised of around 350 data points specific to sandstone reservoir settings. Subsequently, residual oil (Sorw) and irreducible water (Swir) values were tabulated for each core sample. The ML regression models were trained to predict Sorw and Swir using core porosity and permeability as feature variables. Subsequently, core porosity, core permeability, and water saturation (Sw) from relative permeability (kr) experiments were incorporated as features to model krw and kro in the regression models. The trained ML models were then used to further predict the krw and kro curves for any core porosity and permeability for varying water saturation points/steps.It's often observed that multiple relative permeability curves arise when dealing with varying rock properties, such as permeability and porosity. However, when preparing a bin of rock types, we typically rely on an averaged relative permeability curve for each rock type based on porosity and permeability ratios. This averaging process often necessitates extensive manual calculations and can be quite time-consuming. In this paper, we present an approach that allows for the prediction of two-phase oil and water relative permeability across a range of datasets derived from specific reservoirs with different pore geometries. The derived curves can be effectively utilized in reservoir simulation exercises. We also compare these proposed curves to those generated using the conventional method of averaging relative permeability curves through a modified Brooks-Corey model.
SimOps Maps: A New Approach to Operational Efficiency in the Oil and Gas Industry
Authors: Russayev M. 
Published: November 2024

Abstract
During the active construction phase of the Future Growth Project (FGP), SimOps Maps have proven to be an indispensable tool. Integrated within the Geographic Information System (GIS) SimOps platform, these maps have significantly transformed project planning and execution. A standout feature of the GIS SimOps platform is its capability to enable planners from various groups to detect potential working activity clashes early in the planning stages. This proactive identification allows for timely adjustments during implementation, ensuring a smoother and more efficient workflow. By leveraging SimOps Maps, the FGP has achieved enhanced coordination and minimized disruptions, underscoring the critical role of advanced GIS tools in modern project management.
Advanced Oil Fingerprinting Technology for Enhanced Reservoir Characterization and Development
Authors: Shipaeva M. , Shakirov A. , Nurgaliev D. , Sudakov V. , Fazlyyyakhmatov M. 
Published: November 2024

Abstract
This study investigates the geochemistry of reservoirs in a mature oil and gas province, focusing on the use of oil and water fingerprinting for fluid source identification, production allocation, and reservoir characterization. The research aims to optimize field development strategies through advanced data processing methods. Over 100 oil samples from different fields were analyzed to determine the chemical composition of oil and its fractions. Gas chromatography and high-resolution mass spectrometry were used to identify various compounds. Advanced clustering methods allowed for effective interpretation of the results.
We found significant variations in hydrocarbon composition across formations within the same oil and gas province, providing insights into oil age, migration patterns, and reservoir heterogeneity. The integration of geochemical data into field operations has improved reservoir characterization accuracy and production efficiency, particularly in mature fields where traditional methods such as production logging (PLT) tools have proven less effective.
Additionally, the findings emphasize that no universal biomarkers exist; each oil field, even neighboring ones, has a unique set of fingerprints. This prompted the development of a chromatogram processing algorithm that clusters data for any oil field, further advancing geochemistry methods in the industry. This work introduces novel oil and water fingerprinting techniques, offering significant advancements in geochemical reservoir assessment and providing valuable tools for improving field development and management practices in hydrocarbon basins.
AI Seismic Interpretation: Improving Our Understanding of the Subsurface
Authors: Whittaker H. 
Published: November 2024

Abstract
Manual seismic interpretation has long been the backbone of E&P workflows however it is a process that demands significant time and a highly skilled interpreter. Artificial intelligence (AI) technology, specifically those that utilise deep learning convolutional neural networks have emerged as a credible alternative, producing results of comparable accuracy and detail.
A key area where AI solutions have already accelerated seismic interpretation workflows are in the delineation and mapping of faults and horizons, as well as identifying and defining features, such as karst platforms or injectites. These workflows have been tested on datasets of varying qualities and ages, as well as on both onshore and offshore datasets.
Once the faults can be detected by the AI networks, the next stage becomes horizon detection and this is done by identifying every single peak and trough in a seismic cube however, accurately correlating individual horizon patches proved to be a much bigger challenge than detecting the discontinuities. A new solution was developed that is entirely aware of faults and understands the interplay between the faults and the horizons it is analysing. The fact that the horizons are aware of the faults means these objects are the perfect input to generate a watertight model. The latest stage of the AI seismic interpretation workflow involved creating a solution to identify and extract geological features such as channels and injectites. AI networks enable the fast, accurate and unbiased analysis of dozens of volumes, applying the same criteria and rigour to every voxel in every volume. For example, every partial stack – angle stacks, azimuth stacks – can now be processed with the same AI network, allowing the human interpreter to focus on comparing and interrogating the resulting AI fault volumes to understand their significance. Furthermore, due to the AI Network's ability to assign more nuanced fault confidence values from 0 to 100%, we can investigate the uncertainty around the structural model based on fault confidence results.
Finally, all of these AI Networks can be fine-tuned by the addition of manually interpreted data labels to add extra information to the neural networks. This can result in increased fault recognition, reduced false positives and an increase in fault confidence amongst many other desirable results. These AI-based techniques significantly enhance the speed and quality of the interpretation of the subsurface. With the help of AI we can rapidly identify and extract faults, horizons and geobodies with unprecedented detail and accuracy. The time saved on manual interpretation can be used to better understand the geology and analyse feasible scenarios, enabling regional and field assessment to benefit from the most up to date information and ensuring high-quality exploration and development decisions.
Multi component biosorption of U(VI), Cu(II), and Ni(ΙΙ) in the fixed bed column: dynamic mathematical modeling, GWO optimization, and biosorption experiments
Authors: Keshtkar Ali Reza , Amiri Mehdi , Moosavian Mohammad Ali 
Published: November 2024

Abstract

In this study, the multi component biosorption by Cystoseria indica brown alga in a fixed bed column from a solution containing U(VI), Cu(II), and Ni(ΙΙ) was investigated at various flow rates (1.15, 2.3, and 4.6 mL/min) and bed heights (3.5, 5.25, and 7 cm). The results demonstrated that the biosorbent's affinity to metal ions follows the order of U(VI) > Cu(II) > Ni(ΙΙ). Furthermore, concentration overshoot was detected in all biosorption assays for Ni(II) ions. The best flow rate of 2.3 mL/min was selected, resulting in the maximum biosorption capacities of 133.2, 52.3, and 23.9 mg/g for U(VI), Cu(IΙ), and Ni(ΙΙ), respectively. The results also demonstrated that the biosorption capacity increases for U(VI) ion and decreases for Cu(IΙ) and Ni(Ι) ions as the bed height increases. The multi component biosorption process was modeled using the internal mass transfer model and the Gray Wolf Optimizer (GWO) algorithm. The modling results revealed that the mass transfer mechanism occurs through diffusion within the biosorbent's pores.
Antimicrobial Effect of Bio-Ceramic Sealer in Necrotic Primary molars
Authors: Aly Esraa Hamed 
Published: November 2024

Abstract

Background
Pulpally infected primary teeth should be retained until exfoliation whenever possible, as deciduous teeth are ideal space maintainers. Removing bacteria from the root canal is essential for a successful root canal treatment. Therefore, the present study compared the antimicrobial effects of resorbable Bio-ceramic sealer with pastes of calcium hydroxide and iodoform mixed with zinc oxide and zinc oxide eugenol when used as intracanal medication on microorganisms of necrotic primary molars. The materials used in the study were: Paste A: Non-setting calcium hydroxide paste mixed with iodoform, Paste B: Zinc oxide eugenol, Paste C: Metapex with zinc oxide, Paste D: Metapex with zinc oxide eugenol, Paste E: Bio-C Pulpecto. Polymicrobial growth inhibition zone diameters around each of the five pastes were measured in mm to determine antimicrobial activity. The collected data was presented, tabulated, and statistically analyzed.
Results
Comparison among all groups demonstrated a significant difference between them (P < 0.0001) using the Kruskal-Wallis test. Furthermore, Dunn's multiple comparisons test revealed that: Paste C (0.93 ± 3.55) and Paste A (2.13 ± 2.67) exhibited significantly the lowest antibacterial activity with no significant difference between them. Subsequently, Paste D (5.2 ± 5.89) and Paste E (8.73 ± 1.36) displayed no significant difference between them. In contrast, Paste B (11.8 ± 1.58) demonstrated significantly the highest antibacterial activity.
Conclusions
Bioceramic sealers could be utilized as filling material for root canal in primary molars because of their antimicrobial and biological properties.
Community characteristics of benthic macroinvertebrates and ecosystem health assessment in ten Reservoirs of Henan Province, China
Authors: Zhao Jiannan , Gao Yunni , Zhang Jingxiao , Li Yongli , Gao Xiaofei , Yuan Huatao , Dong Jing , Li Xuejun 
Published: November 2024

Abstract

The eco-health assessment of regional reservoirs is important for ensuring the sustainable utilization of water resources and maintenance of water security, particularly in regions facing water scarcity. The present study aimed to construct a B-IBI based on the community characteristics of macrobenthos in ten large and medium-sized reservoirs across four major river basins in Henan Province, China. The results revealed the identification of 92 species belonging to 3 phyla, 6 classes, 18 orders, 47 families. The B-IBI was established based on five key metrics, namely the number of crustacean and mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). The total B-IBI score of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated two reservoirs (QTH and HKC in the Yellow River basin) were classified as poor, whereas only one reservoir (QP in Huai River basin) was classified as excellent. The B-IBI index demonstrates a strong capability to distinguish the impaired sites from the reference sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province.
A novel mitochondrial gene rearrangement delineate Magallana gryphoides species from other Magallana crassostreine oysters
Authors: Santhi Vineetha Vijayan , Chaube Shail K. , Antony Mano Mohan 
Published: November 2024

Abstract

Background
The Magallana genus represents commercially most important oysters globally. Due to phenotypic plasticity, members of Magallana genus are often confused morphologically and limit their taxonomical as well as conservation efforts. On this background, the complete mitochondrial genome of Magallana gryphoides was sequenced.
Methods and results
The complete mitochondrial genome (mitogenome) of Magallana gryphoides was sequenced for the first time employing the next-generation sequencing. Results suggest that the mitogenome of M. gryphoides is of 18313 bp in length (accession number OR177662). The mitogenome contain 13 protein-coding genes including ATP8 and 26 tRNAs with 3 new suppressor tRNAs as well as 2 rRNAs. Further, presence of two copies of rrnS and rrnL lack 3’ half are unique, unusual and absent in other Crassostreinae oysters. The positions of COX1, trnG, trnV, and ND2 genes in the mitogenome of M. gryphoides are significantly altered. The phylogenetic analysis indicates a close relationship of M. gryphoides with the native Indian oyster Magallana bilineata and other Asian oyster Magallana iredalei.
Conclusion
Taken together, these data provide a new insight into phylogenetic relationship of oysters and may help to place the correct taxonomical status of M. gryphoides, M. bilineata and M. iredalei that that are often confused in the Ostreidae taxonomy due to phenotypic plasticity.