bluuebunny/crossref_metadata_2025_split huggingface dataset:
Autonomous Drilling Fluid Management System - Expansion of Fluid Advisory System Across Multiple Fluid Systems
Authors: Abdul Razak F. , Onegova E. , Postovalov S. , Knizhnik A. , Gilad Y. , Valtysson V. , Krishnan A. , Thompson C. , Ettehadi R. , Brevik J. 
Published: September 2024

Abstract
This research aims to develop a Fluid Advisory System that provides recommendations for the optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling fluid systems. Building on the foundation laid by the Fluid Advisory System for water-based fluids (Ettehadi et al., 2023), this project expands its scope to include both water- and oil-based muds.
To achieve this goal, a machine learning (ML) model is created to predict the target fluid properties, with separate models developed for water- and oil-based muds due to their distinct characteristics. These models utilize transfer learning techniques that allow existing models to serve as base models for similar fluid systems thereby enhancing efficiency. An integrated optimization framework determines the ideal quantities of chemical additives, ensuring precise mass calculations and optimal dilution methods.
The effectiveness of the Fluid Advisory System is validated through small-scale lab experiments which analyzed samples of water- and oil-based muds previously used offshore, despite the unknown exact compositions of these muds. The testing outcomes demonstrate the system's ability to regulate critical parameters such as mud weight, rheology, API fluid loss, pH, hardness, chloride concentration, oil-water-ratio, concentration of low gravity solids (LGS), excess lime, and electrical stability (ES).
In summary, this paper provides a detail account of the development and experimental validation of the Fluid Advisory System, which effectively manages multiple properties of drilling fluids across different fluid systems, offering substantial advantages to fluid engineers and marking a significant advancement in the drilling industry by improving accuracy and efficiency.
Production Optimization through Operate by Priority: The Development and Deployment of Downtime Advisory System to Permian Wells
Authors: Wang Yan , Kaus Joshua E. , Karantinos Emmanouil , Phi Tuan Thanh , Gast David 
Published: September 2024

Abstract
High-well-count Permian Basin (Permian) assets pose unique challenges in daily production operations. With hundreds of wells under the oversight of a single production engineer and a limited number of lease operators, it is very hard to accurately track a well's production uptime. In the Permian, large variances have been observed in day-to-day production volume (oil, water, gas) balances due to unrecorded downtime. Volume losses attributed to events such as liquid loading, gas lift underperformance, choking and offset frac'-hits are usually not reported. These challenges make it difficult to identify production optimization opportunities and to have confidence in allocated production datasets. This paper describes a downtime advisory system (DTAS) that utilizes data analytics and rule-based algorithms to automatically detect well production downtime. DTAS reports non-producing wells daily to the operation teams and populates a priority list with impacted volumes in order to facilitate investigation of production impacting events and ultimately achieve production uplift.
DTAS has been deployed to more than 2,500 ExxonMobil operated wells in the Permian with recommendations on daily well production downtime. DTAS identified additional production loss that was previously unrecorded, which was translated into opportunities that helped improve production optimization and allocations. The automated system has saved field personnel significant time by reducing time-on-tools and facilitating root cause diagnosis. This has enabled production engineers to focus on production uplift and achieve operational efficiency by priority.
Experimental Microfluidic Strategies for Enhanced Recovery with Ferrofluid
Authors: AlNasser Fatimah H. 
Published: September 2024

Abstract
The objective of this microfluidic investigation is to identify and test two novel applications for magnetic fluids in porous media for subsurface oil field applications. In particular, the study assesses the possibility of utilizing water-based or oil-based ferrofluids as agents in enhanced oil recovery and temporary in-situ valves to divert multiphase fluid flow. Ferrofluid is a liquid dispersion of nanoscale ferromagnetic particles that exhibits dynamic response to changes in magnetic fields. Three forms of external magnetic fields were tested, a stationary magnetic field from a permanent magnet, an oscillating magnetic field from an electromagnet, and a rotating magnetic field from a magnetic stirrer. The micromodel porous medium consisted of two types that are simple trapping mechanism designs (converging-diverging channel and pore doublet) and sample rock-like designs (sandstone and fractured sandstone). The experimental results confirmed that ferrofluid is an effective enhanced oil recovery agent for both the wetting and nonwetting phases and showed five different mechanisms by which ferrofluids could improve recovery at the microscale that are (i) direct displacement by the magnetic body force (ii) pore-scale agitation (iii) mobility ratio reduction by viscosity alteration (iv) interfacial tension reduction, and (v) magnetic nanoparticle chaining.
Near-Wellbore Water Imbibition Study for Cyclic Injection of CO2 into the Subsurface
Authors: Pianos M. 
Published: September 2024

Abstract
The present work constitutes a comprehensive study on near-wellbore water imbibition in the context of CO2 cyclic injection for storage purposes. The research, conducted at INEOS Energy and DTU Chemistry, aims to provide a deeper understanding of the imbibition process and its implications for CO2 cyclic injection practices. It uses two simulation cases, modeled in Eclipse (SLB), starting from a relatively simple Black Oil model and inductively moving to a more complex compositional one. The models are two-phase, isothermal, homogeneous, and consist of a horizontal injection well and a vertical cross-section around it. Some of the most impactful parameters of the near-wellbore water imbibition process are investigated to enhance knowledge of the industrial approach of Project Greensand. Initially, the concept of CO2 cyclic injection and its potential as a tool for reducing greenhouse gas emissions are introduced. Then there are references to the key theoretical concepts around CO2 injection and expected results based on previous studies. Numerical simulations are used to investigate the dynamics of water imbibition near the wellbore during CO2 cyclic injection. The results of these numerical models are analyzed in detail, revealing key insights into the mechanisms of water imbibition and the factors that affect its rate and extent. Continuing, the impact of various parameters, such as injection rate, saturation function hysteresis, brine salinity, and well location, on near-wellbore water imbibition is examined. The findings of this study, which show how key simulation parameters affect water imbibition, have significant implications for the optimization of CO2 cyclic injection processes, contributing to enhanced operational efficiency and sustainability in the energy industry. Overall, this work provides a valuable contribution to the field of engineering, offering a deeper understanding of the complex dynamics of near-wellbore water imbibition during CO2 cyclic injection. The insights gained from this study have the potential to inform and improve industry practices, leading to more efficient approaches to the application of a crucial decarbonization technology.
The Effect of Silicon Dioxide Nanoparticle Functionalization on the Filtration Properties of Drilling Fluid and Porous Media Permeability
Authors: Ibrahim M. A. 
Published: September 2024

Abstract
Near wellbore permeability alteration can hinder well productivity and injectivity performance. Drilling-induced permeability alterations frequently receive criticism because it is the first human action to disturb the formation's chemical and mechanical equilibrium. Despite various mitigating techniques, conventional overbalanced drilling with drilling fluid including colloid is still preferred because of its benefits for well control and wellbore stability. Current research trend focuses on engineering nanoparticles in drilling fluid to reduce extended fluid and solid infiltration into the formation. However, there is a fundamental knowledge gap regarding the effect of the nanoparticle surface properties on drilling fluid filtration properties. Due to the substantial difference in the surface-to-weight ratio in nano-sized materials, there is a distinct difference in the properties that govern matter at nanoscale as compared to those of the micro and macroscale. Consequently, surface properties play a more significant role for nanoparticles to work as filtration control material. Literature data suggest a relationship between nanoparticle charge and potential and drilling fluid filtration properties, albeit with limited discussion. Additionally, the effect of these properties on fluid and solid infiltration into porous media has not been studied. This research aims to study the effect of nanoparticle charge and potential on the drilling fluid filtration properties and porous media permeability. Silicon dioxide nanoparticles, functionalized with carboxyl and quaternary ammonium groups, were tested. Return permeability experiments on Berea sandstone core analyzed filtration properties and permeability alteration. The drilling fluid was water-based, and the formation fluid was sodium chloride brine. Results revealed that larger nanoparticle sizes produced a more consistent result, while smaller nanoparticles had better filtration properties but were more susceptible to change in other variables. Positively charged nanoparticles showed slightly better filtration properties but adversely affected return permeability due to strong adsorption onto sand particles. Permeability alteration was up to 89% for positively charged nanoparticles, compared to 53% for base drilling fluid, 83% for unfunctionalized nanoparticles, and 21% for negatively charged nanoparticles. This study provides insights into the significance of nanoparticle surface properties on drilling fluid filtration and transport in porous media, affecting permeability.
Optimizing CO2 Sequestration in Coal Seams: A Machine Learning Framework for Wettability Prediction
Authors: Ifenaike A. O. 
Published: September 2024

Abstract
In the face of escalating global climate challenges, Carbon Capture, Utilization, and Storage (CCUS) emerges as a pivotal technology in the quest to mitigate atmospheric CO2 emissions. Among the various geological formations suitable for CO2 sequestration, coal seams represent a unique and promising option, especially those that are unmineable due to depth, thickness, or other economic constraints. The success of this formation is closely tied to the intricate interactions among CO2, coal, and in-situ fluids, with wettability playing a crucial role. A deep understanding of wettability is pivotal for optimizing CO2 injection strategies and ensuring the long-term stability of the sequestered CO2. Over the past decade, researchers have increasingly turned to data-driven methods to predict rock-fluid interactions, yet the limited size of datasets has constrained the representativeness and applicability of their results. Additionally, traditional methods for assessing wettability, such as contact angle measurements, Amott test and interpolation techniques involving nuclear magnetic resonance, are costly and time-consuming.
In response to these challenges, this study employs a data-driven approach, leveraging a collection of experimental datasets to predict wettability in a coal/CO2/brine system. The framework incorporates features such as physical properties of coal, ambient conditions, CO2 characteristics, coal rank, and surface chemistry, utilizing advanced data analysis techniques such as heatmaps, cross-validation, feature engineering and importance analysis to enhance model generalizability. Four machine learning models were employed in this study: Bayesian Linear Regression, Explainable Boosting Machines (EBMs), Google's TabNet algorithm and a composite of the last two models (Voting Regressor.) The voting regressor model demonstrated superior predictability with a coefficient of determination (R2) score of 0.86, mean absolute percentage error of 5.32% and root mean squared error of 5.92 on the blind test set, outperforming the other stand-alone models. The strong correlation coefficient of 0.954 between measured and predicted wettability values underscores the model's robustness. Consequently, this study advances the predictability of key parameters for CO2 sequestration and underscores the feasibility of using coal seams for long-term CO2 storage, significantly contributing to CCUS research.
Low Salinity Water Injection Modeling by Craig-Geffen-Morse (CGM) Method: A Case Study in a Homogeneous Reservoir
Authors: Lubis Samuel Dacosta 
Published: September 2024

Abstract
Low Salinity Water Injection (LSWI) is widely used in the global oil industry for enhanced oil recovery (EOR) due to its operational simplicity, cost benefits, and environmental friendliness compared to other methods. However, the underlying mechanisms of LSWI are still debated, prompting the need for a more realistic model to simulate fluid displacement in reservoirs.
This study proposes the Craig-Geffen-Morse (CGM) method as an alternative approach to model low salinity waterflooding and reservoir fluid displacement. The study also investigates the impact of reservoir properties on oil recovery during low salinity water injection, considering factors such as pattern area, reservoir thickness, sand layer permeability, and fluid mobility ratio.
Using Visual Basic for Applications (VBA) codes in Microsoft Excel, the CGM method is implemented to calculate equations and correlations. The study conducts a sensitivity analysis to observe the effects of several parameters on reservoir performance.
The results show that injecting fresh water may increase oil recovery by 8.2% compared to high salinity water injection. While water salinity data is not directly considered, the study demonstrates a clear relationship between reduced water salinity and improved oil recovery based on certain reservoir property conditions. Additionally, variations in pattern area and thickness have minimal impact on reservoir performance, yielding similar oil recovery factors. Sand layer permeability only affects oil recovery when high salinity water is used, and higher fluid mobility ratios significantly reduce oil production.
The findings align with previous research, supporting the applicability and practicality of the Craig-Geffen-Morse method for modeling low salinity water injection. These findings contribute to further development in this field.
Evaluation of Dentin Tubule Occlusion Using Diode laser 976 nm and 0.5% Sodium Flouride gel using SEM: An Ex Vivo Study.
Authors: Siddig Reem Esam , Ellateef Abd Ellateef  Abbass Abd , Awooda Elhadi Moheildin 
Published: September 2024

Abstract

Background: Dentin hypersensitivity (DH) is a painful condition associated with challenging treatment options.
Aims: This study aimed to evaluate the effectiveness of a diode laser (976 nm) for occluding dentinal tubules in vitro and to compare the effectiveness of this laser with that of the conventional chemical agent sodium fluoride gel in occluding the Dentinal Tubules.
Materials and Methods: Ten extracted human premolars were utilized in the current in Ex vivo experimental scanning electron microscopic (SEM) study. Using a diamond saw with coronal dentin from each tooth specimen, dentin disks 1.0 mm in thickness were cut perpendicularly to the long axis of each tooth. Ten dentin disks were prepared, and each specimen was divided into four equal parts, pretreated with ethylenediaminetetraacetic acid (17%), and subsequently divided into four groups: (Group 1) the control group, (Group 2) the 976 nm diode laser with a power of 1 watt, (Group 3) the sodium fluoride only group and (Group 4) the combined laser and sodium fluoridearnish group. The samples were analysed using a scanning electron microscope at different magnifications (100 µm, 50 µm, 20 µm, and 2 µm).ANOVA was used to compare the differences in dentinal tube diameter among the four groups, with the level of significance set at a P value ˂0.05.
Results: Group 1 (control, no treatment) had all the dentinal tubules opened. Both the laser group and the sodium fluoride group demonstrated partial occlusion and narrowing of most of the dentinal tubules, while the combined group showed total occlusion of almost all of the dentinal tubules. There was a statistically significant difference between the four groups regarding the diameter of the dentinal tubules.
Conclusions: The combination of a diode laser (976 nm), 1 W, and sodium fluoride varnish significantly affected dentinal tubule occlusion, thus preventing DH.
Reproductive Health and Rheumatoid Arthritis
Authors: Ranjbaran Mohammad Erfan , kazemi Maryam 
Published: September 2024

Abstract

Introduction: The prevalence of Rheumatoid Arthritis (RA) has been steadily increasing over the last thirty years, particularly among women. Due to the conflicting results of various studies on the link between rheumatoid arthritis and reproductive health, we have undertaken this study to explore their relationship. Method: This cross-sectional study utilized primary data from women involved in the Fasa Persian Cohort. The study examined the frequency of RA and its association with various factors related to reproductive health, including age at first pregnancy, number of alive children, history of abortion, and infertility. SPSS software was utilized for data analysis, with the significance level set at p < 0.05. Results: 5557 women with an average age of 48.6 ± 9.5 years participated. Of these women, 7.2% were diagnosed with rheumatoid disease, with an average onset age of 38.2 ± 15.2. The study did not find a significant correlation between RA and factors such as number of pregnancies, age at first pregnancy, duration of breastfeeding and number of children. Conclusion: The study suggests that the experience of RA is independent of gynecological, obstetric factors, or reproductive life. This finding may provide reassurance to women affected by RA.
A novel QTL carrying NB-ARC family genes enhances grain protein content without grain weight penalty in wheat (Triticum aestivum L.).
Authors: Fandade Vikas , Mishra Ankita , Sharma Vinita , Kumar Prashant , Madhawan Akansha , Singh Dalwinder , Kumar Jitendra , Singh Pradeep , Das Deepak , Mantri Shrikant , Sharma Shailendra , Oak Manoj , Roy Joy 
Published: September 2024

Abstract

Wheat (Triticum aestivum L.) is a global food crop with low protein content of 9-12%. Enhancing grain protein content (GPC) without compromising yield is challenging largely due to the negative correlation between yield and GPC. Genome wide DNA markers, high-throughput phenotyping, genome-wide association studies (GWAS), and QTL mapping have facilitated the identification of quantitative trait loci (QTLs). This study utilized a stable recombinant inbred line F7-9 (RIL) population, genotyped via high-depth genotyping by sequencing (GBS), and conducted multi-environmental trials to identify stable QTLs for GPC. In contrast to the inverse relationship of GPC and thousand grain weight (TGW), the RIL population showed positive correlation of 0.143 (P<0.001) with TGW. The RIL population also showed significant genetic variation in GPC, with a heritability of 0.72, and identified ten QTLs for GPC on chromosomes 2B, 5B, 5A, 4B, and 1D. Among these, QGPC.nabi-2B.2 and QGPC.nabi-5B.1 were identified as major and stable QTLs. Precise mapping of QGPC.nabi-2B.2 identified NB-ARC domain-containing proteins as potential candidate genes for GPC regulation. Three SNPs from the QGPC.nabi-2B.2 region were converted to Tetra-ARMS-PCR markers. Subsequently these markers were used to validate in Indian wheat varieties and in a genetically different RIL population. This study provides a foundation for further research into the genetic regulation of GPC in wheat and suggests that NB-ARC genes could play a significant role in improving GPC, potentially enhancing wheat's nutritional quality without yield penalty.
Soil Moisture Prediction Based on Long Short-Term Memory Networks and Meteorological Data
Authors: Jiang Ping , Niu Guizhen , Li Guangming 
Published: September 2024

Abstract

As an important factor directly affecting the growth of crops, a reasonable and accurate prediction of soil moisture can effectively improve the quality of crop growth. The deep learning model LSTM is often used to predict soil moisture, nevertheless, the predictions generated only by LSTM exhibit a lack of accuracy. To resolve this issue, in this study, we propose a method that combines meteorological features and correlation of soil at different depths to predict soil moisture. By verifying the autocorrelation and cross-correlation of soil at different depths, it is determined that the soil moisture characteristics can be regarded as a smooth time series, and the meteorological data can be used to predict the soil moisture. Afterwards, the LSTM model was used to predict soil moisture from meteorological data, and it was found that there was a significant disparity between the actual value and the predicted value, and the accuracy of prediction was improved by reconstructing the model. To validate the proposed model, we selected moisture data from six monitoring points in unirrigated (rainfed) wheat fields in the designated areas of the Yellow and Huaihai Seas to validate the proposed model, and obtained accurate moisture prediction values, proving the validity of the model.
Determinants of Profitability in the Indian Inorganic Chemical Sector: An ARDL Empirical Analysis
Authors: Mahor Nirbhay , Banerji Amit 
Published: September 2024

Abstract

This research study into the impact of various factors on the profitability of India's crucial inorganic chemical industry, examining the interplay between a company's profitability and key variables such as Research and Development Intensity (RDI), net fixed asset turnover ratio, leverage, Export Intensity, and net working capital requirement. Utilizing data from the Prowess financial database spanning from 1998 to 2021, using the autoregressive distributed lag (ARDL) method, to assess both the short-term and long-term dynamics between return on assets (ROA) and these independent variables. Findings from the ARDL analysis reveals a short-term negative impact of RDI, net fixed asset turnover ratio, and leverage on ROA, whereas export intensity and net working capital requirement positively influence profitability. Over the long term, RDI continues to exert a negative effect on ROA, whereas positive impacts are observed from export intensity and net working capital requirement on profitability. The empirical investigation within the inorganic chemical sector, this study sheds light on how strategic financial management of factors such as RDI, asset turnover, leverage, export intensity, and working capital can significantly bolster a firm's financial performance, offering valuable insights for companies use to refine their strategies and enhance profitability.