bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pile Drivability Assessment Through Rock Formations
Authors: Kadivar M. , Ghasemi P. , Christopher N. , Maynard A. , Barwise A. 
Published: April 2024

Abstract
This paper presents a comprehensive study on pile drivability assessment in rock formations, primarily focusing on the development and validation of an enhanced Soil Resistance to Driving (SRD) model. Utilizing a database of pile driving records from an offshore wind farm, located predominantly on Mercia Mudstone, this research introduces a novel approach in assessing pile drivability through rock formations. The SRD model, an advancement of the Stevens et al. (1982) method, incorporates modifications to better predict unit shaft and tip resistance in rock formations. The model's performance is validated using field data from several Wind Turbine locations, demonstrating its accuracy in predicting the energy required and the number of hammer blows for pile penetration in rock. This study provides valuable insights for engineers and practitioners involved in pile installation projects in similar geotechnical conditions.
CO2 Storage Complex in Shallow Waters of Campos Basin, Brazil: Capacity, Uncertainty and Risk Analysis of a Deep Saline Formation
Authors: Lira F. S. , Miranda D. , Freire E. B. , De Gasperi A. , Pinto I. A. , Cassino L. F. , Costa P. , Gonçalves M. S. , Netto P. , Pereira L. C. , Neto R. R. , da Silva P. F. , Musse A. S. 
Published: April 2024

Abstract
The role of Carbon Capture, Use, and Storage (CCUS) in reducing greenhouse gas emissions is crucial for achieving global climate targets. Brazil, as a significant emitter, is actively pursuing CCUS projects and the state of Rio de Janeiro, with its high emissions of industrial activities, presents an opportunity for establishing a CCUS hub. The potential storage reservoir, São Tomé Deep Saline Formation, located in the Campos Basin, offers favorable characteristics for CO2 storage. This srtdy aims to estimate the CO2 storage capacity, perform uncertainty analysis, and comprehensive risk assessments for the Sao Tome reservoir. The methodology involves mapping potential storage reservoirs, 3D geological modeling, and flow simulation to evaluate injectivity, storage potential, containment safety, and monitoring strategies. The results demonstrate a storage capacity of approximately 1 Gt of CO2, injectivity of 1.5 MMt C02/yr/well, and low leakage risk. However, further evaluation is required, including petrophysical characterization and geomechanical data. The proposal includes drilling a new well to acquire additional information and support more robust modeling. The establishment of a CCUS hub in Rio de Janeiro would bring economic, social, and environmental benefits, attracting companies seeking environmentally friendly solutions and facilitating the development of the shallow-water industry. To achieve the Net Zero Scenario by 2050, CCUS plays a crucial role in emissions reduction, and the Sao Tome reservoir presents significant potential for CO2 storage. Continued evaluation and data acquisition are necessary to ensure the feasibility of large-scale CO2 injection projects. The establishment of a CCUS hub in Rio de Janeiro would position the region as a center of environmental responsibility and attract investment and innovation in the energy sector.
Enhancing CPG-Derived CO2 Flux Measurements via a Robust Wireless Sensor Analysis Framework – A New Zealand Taranaki Basin Case Study
Authors: Katterbauer Klemens , Shehri Abdallah Al , Al-Qasim Abdulaziz , Yousef Ali 
Published: April 2024

Abstract
In the pursuit of more sustainable resource use, lithium has emerged as a useful resource for a range of uses, including batteries. Due to these difficulties, there is a growing need for improved field operations that preserve output levels while also making the operations more sustainable. The Fourth Industrial Revolution are greatly impacting the oil and gas sector, and as the need for lithium to be used for batteries and energy technologies grows, hydrocarbon reserves are becoming a more desirable source of these precious minerals. Lithium has been found in considerable amounts in the generated brines of a number of reservoirs worldwide. In this study, we provide a novel artificial intelligence (AI) optimization strategy for optimizing lithium recovery from reservoir operations while preserving reservoir oil production goals. A deep learning LSTM algorithm is integrated into the AI framework to estimate water injection amounts based on oil, brine, and lithium recovery. After that, a global optimization framework using the deep learning model is integrated to optimize the water injection levels in order to maximize lithium recovery while preserving reservoir oil production levels. For the purpose of recovering lithium from reservoir brine in an oil and gas reservoir, we have presented a novel AI optimization framework. The framework allows hydrocarbon recovery rates to be maintained while optimizing lithium recovery. The framework, which highlights the potential for a large improvement in lithium recovery rates from an enhanced injection procedure, was successfully demonstrated on the Volve field. This paradigm has the potential to yield substantial benefits for optimizing the use of reservoir brine, which might lead to improved sustainability in reservoir operations.
A Hybrid Physics Augmented Predictive Model for Friction Pressure Loss in Hydraulic Fracturing Process Based on Experimental and Field Data
Authors: Abdulwarith Abdulrahman , Ammar Mohamed , Kakadjian Sarkis , McLaughlin Nathan , Dindoruk Birol 
Published: April 2024

Abstract
During the hydraulic fracturing process, fracturing fluid leads to excessive frictional losses within the casing and formation which can be impediment to effectiveness of the operations and may result in screenout. Total pressure losses during hydraulic fracturing include wellbore pressure losses in pipe and the near wellbore pressure losses that includes both perforation and formation tortuosity losses. Wellbore friction pressure losses can be determined experimentally using flow loop test at different friction reducer concentrations and flow rates. Practically, fracturing engineers need to estimate and monitor the wellhead pressure and total friction losses during the operation and adjust the fluid, proppant, and friction reducer concentrations during the job. In this study, physics augmented data-driven models are used to predict the pressure losses in pipe using the flow loop test results and the well head pressures and total friction losses. Total friction losses used are based on field data of hydraulic fracturing stages from several wells. The flow loop test is modeled for specific friction reducer types at different concentrations and flow rates. The process of this model relies on analyzing the data obtained from the tests and establishing a correlation between the concentrations of the friction reducers, flow rate and their corresponding pressure losses. The second part is the modeling total pressure losses using field data considering fluid properties and the modeled lab results. Modeling approach incorporates all the key parameters such as fluid properties, proppant concentrations, friction reducer concentrations, slurry rates, perforation (Diameter and Density) and formation characteristics to accurately predict the frictional losses. Both models are tested using validation data. Outcome of the total friction pressure loss model and the forecasted wellhead pressure results exhibited a close agreement between the predicted and the recorded pressure values, indicating that the results can be used in actual field operations. This approach provides valuable information for optimizing hydraulic fracturing design and improving operational practices to minimize the down time. Utilization of the flow loop tests results eliminates the need for repetitive experiments, enabling parametric studies and sensitivity analysis and thus leading to more efficient operations. This unique approach, using the combination of physics-based and data-driven methodologies, represents a major step in predicting pressure losses in hydraulic fracturing operations. Integration of these models not only enhances understanding of the physical processes and the causalities, but also provides practical solutions for improving overall operational efficiency of the process.
Improving Wave Forecast Using Neural Networks
Authors: Primo B. , Achete F. , Salvio F. , Barbosa N. 
Published: April 2024

Abstract
Traditionally, wave forecasts are done using process-based models. These models may present inaccuracies related to their physical formulation, numerical approximations, unresolved small-scale processes, model parameterizations, or initial and boundary conditions. Recently, the combination of unprecedented large data volumes, computational power, and advances in machine learning algorithms offers opportunities to expand our knowledge of the oceanic system. In this context, the main objective of this study is to accurately predict significant wave heights, combining the results from numerical simulations and data-based models. The available data at a point in the Santos Basin, Brazil, was: 5 deterministic forecast models and 80 members from probabilistic forecast simulations and in-situ wave buoy measurement. Ensemble means and neural networks forecasts were calculated and compared to Hs forecast of individual deterministic models. Root mean square error was the chosen metric to compare the deterministic models and the data-driven forecasts. The results show ensemble means (EM) outperforming individual members for lead times higher than 24h. The neural networks forecast reduced even further the errors, especially for shorter lead times.
Data Integration Enabling Advanced Machine Learning ROP Predictions and its Applications
Authors: Wang Junzhe , Li Chengxi , Cheng Peng , Yu Jiaqing , Cheng Chris , Ozbayoglu Evren , Baldino Silvio 
Published: April 2024

AbstractPredicting and optimizing the rate of penetration (ROP) is a crucial part of drilling optimization. While numerous machine learningbased ROP prediction methods exist, their practical utilization remains underexplored. This paper introduces an innovative approach, employing data integration from different sources with multiple machine learning algorithms for accurate ROP prediction. The proposed model is validated using field data and several potential applications are discussed and implemented.The data from multiple vertical wells (real-time drilling data, insights into formation drillability obtained through coring, information related to the Bottom Hole Assembly (BHA) and PDC drill bit) are meticulously collected, screened, pre-processed, and seamlessly integrated through a data integration process. This enriched dataset then becomes the cornerstone of advanced analytics. Leveraging the strength of six machine learning-based regression models, namely the Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine Regression (SVR), Polynomial Regression (PR), and Multiple Linear Regression (MLR), a comprehensive strategy for the Rate of Penetration (ROP) prediction is meticulously implemented.Field data from the Tarim basin in Xinjiang, China, was utilized to validate the proposed approach. Among all the methods, decision tree-based algorithms exhibit the best performance, with XGBoost standing out with a coefficient of determination (R-square) as high as 0.98, and a remarkably low mean absolute percentage error (MAPE) of 4.8%. Random Forest (RF) shows very similar results. Furthermore, both the Artificial Neural Network (ANN) and polynomial Regression demonstrate good performance, boasting R-square values around 0.93, and MAPE figures of approximately 10%. However, the Support Vector Machine Regressor (SVR) and Multiple Linear Regression (MLR) display comparatively lower scores with R-square values of 0.8 and 0.87, respectively. Regrettably, their MAPE values are considerably high at 22.39% and 19.98%, rendering them unsuitable for recommendation. Utilizing the fine-tuned ROP prediction model, practical applications such as on-the-fly ROP optimization, real-time drilling advisory systems, drill bit recommendation systems, etc. are explored and implemented. The majority of the analysis in this paper is the result of an automated data analysis pipeline, enabling a seamless deployment in future autonomous drilling operations.This paper presents a novel machine learning approach for ROP prediction, which incorporates integrated data encompassing formation drillability information as well as information about the drilling Bottom Hole Assembly (BHA) and drill bit. This approach not only revolves around ROP prediction but also emphasizes the practical application potential of the predictive model, an aspect that received limited attention in prior research.
Data Fusion and Visualization Approaches to Achieving a Common Operating Picture During Offshore Wind Construction and Operation Works
Authors: Doolittle Daniel , Eaton Ross , Parslow Marna , Donlan Paul , Jones Craig 
Published: April 2024

AbstractThe offshore wind industry is promoting developments in environmental sensing, machine learning, and artificial intelligence, to better detect the presence of marine and avian species. Environmental sensing technologies (e.g., radar, video and infra-red imagery, passive acoustics, and radio telemetry) have advanced where wildlife are reliably detected and tracked, aiding their protection by minimizing conflicts with ships, other users of the ocean space, and other stressors.Significant marine ecosystem data is collected daily offshore from a wide range of reputable sources. These disconnected sources represent, in aggregate, a trove of Domain Awareness (DA) data and if cohesively viewed, provide opportunity to better de-risk operations, protect wildlife, and avoid delays in real time. Taking care and effort to assimilate these (often disparate) data sources into common visualization platform(s) provides both more granular and macro-scale situational awareness, while advancing opportunities to apply predictive Artificial Intelligence (AI) to the data. This can result in the application of regional (or broad scale) predictions and understandings of species activities. As this data base of predictions and observations grow, additional decision making and management mitigations can be applied, such as alerting specific vessels to the presence of protected species or initiating tailored dynamic management areas (DMAs) at appropriate temporal or spatial scales.Deployment of sensors on technically advanced host platforms, including autonomous underwater vehicles, uncrewed surface vehicles, and metocean buoys, is occurring regularly. Equally prolific are strategies to collect, analyze, and display data from each sensor, resulting in myriad data dashboards, digital twins, and immersive visualization environments offered to offshore wind developers and regulators. While accelerating technological innovation, these numerous, and often single-focus approaches can hinder the delivery of a unified picture of the worksite or regional environment, limiting conservation value of these efforts and increasing environmental and scheduling project risks.This paper reviews some of the extant initiatives to deliver environmental data and provides a suite of best practices and recommendations for developing a DA capability or a common operating picture (COP) of developer's projects, as well as a regional view that covers multiple worksites. This work will assist developers and regulators to understand a realistic state of technical readiness and how to appropriately scope data products that support data fusion consistently across visualization platforms.
Open Defecation, Livestock Ownership, and Child Nutritional Status in India
Authors: Luke Nancy , Acharya Yubraj , Faytong-Haro Marco , Yang Di , Xu Hongwei , Oommen Anu Mary , Rose Winsley 
Published: April 2024

ABSTRACT.
South Asian children are among the most severely malnourished worldwide. One prominent hypothesis is that open defecation in the local area exposes children to human fecal pathogens that can cause diarrhea and malnutrition. Much of the existing research uses district-level measures of open defecation, which could mask important local-area variation. A second hypothesis is that animal fecal matter is a major source of exposure. This analysis tested these dual hypotheses using census data collected from 949 villages in Tamil Nadu, India, and a survey conducted in a random sample of 5,000 households in the same area. The final analytic sample consisted of 2,561 children aged 0–10 years. We estimated the association between the measures of village- and household-level open defecation, household livestock ownership, and child height-for-age Z-scores in a regression framework, controlling for potential confounders. Results revealed that village- and household-level open defecations are negatively associated with child height. There was an estimated difference of approximately 0.5 height-for-age Z-score between children living in villages with no open defecation and children in villages where all households practiced open defecation (P = 0.001) and a 0.2 Z-score difference between children living in households that practiced open defecation and those living in households that did not (P = 0.001). Livestock ownership was not associated with child height. Overall, the findings provide evidence on the centrality of open defecation in explaining persistent child malnutrition in India and the higher risk of exposure to human fecal pathogens compared with animal feces in the south Indian context.
Attitudes of Graduate Students in the Faculty of Educational Sciences at Al al-Bayt University Towards using the E-learning Platform Moodle in light of the Corona pandemic
Authors: Al-Suroor Mamdouh Hayel 
Published: April 2024

This study aimed to identify the attitudes of graduate Students at the faculty of educational sciences at Al al-Bayt University towards using the e-learning platform Moodle in light of the Corona pandemic in Jordan, to achieve the objectives, the researcher designed an E-electronic questionnaire consisting of (20) items. The validity and reliability of which were confirmed. Distributed to the study sample, which consisted of (304) male and female students at the Faculty of Educational Sciences at the researched university. The study adopted the descriptive survey methodology. The results of the study showed that the degree of attitudes of graduate Students in the faculty of educational sciences at Al al-Bayt University towards using the e-learning platform Moodle in light of the Corona pandemic from Their point of view was high, With an arithmetic mean (3.74) and a standard deviation (0.86), and there were no statistically significant differences at the significance level (α = 0.05) in the estimates of the study sample members due to the variables (Gender, Department, Path), and in light of the results of the study, the researcher presented a set of recommendations. The most important of which are riving more attention to the application of the E-learning Platform Moodle, in Jordanian universities.
Habits of Mind among 12th Grade Students in the North region within the Green Line in light of some variables
Authors: Tuni Hala Suhail , Al-Shawashrah Omar Mustafa 
Published: April 2024

The study aimed to identify the habits of mind among twelfth grade students in the North region within the Green Line in light of some variables. The study used the descriptive survey methodology, and the Ghablan Scale (2021) for data collection, which was applied to an available sample of (492) students. The results showed an existence of statistically significant differences in favor of males in the habits of Flexibility of thinking, perseverance, striving for accuracy, accurate thinking and communicating, Responsibility and risk-taking, questioning and posing problems, in thinking about thinking, staying open to continuous learning, flexibility of thinking, perseverance, reciprocal thinking, impulsive control, questioning and problem solving, in favor of students from families with less than three children, and in favor of families with (> 20,000 dollars) income in flexibility of thinking, gathering information with all senses, reciprocal thinking: joint work, creation, imagination, innovation, applying previous knowledge to new situations, questioning and posing problems.
The Strategies of Persuasion in Political Discourse: A Comparative Analysis of the Political Speeches of Arab and Non-Arab Leaders During Covid-19 Pandemic
Authors: Al Hawamdeh Reema Ibrahim , Qudah Mahmoud Ali 
Published: April 2024

This paper investigated the persuasive methods in Arabic and non-Arabic political speeches during the beginning of Covid-19 pandemic, as well as the similarities and differences between Arab and non-Arab leaders in using persuasive strategies. Data of the study consisted of the political speeches which were concerned with the Covid-19 situation in six different countries of the world, released between March and August 2020. The data analysis examines persuasive methods in political speeches and analyze them using Aristotle's triangle (Rhetorical Triangle). The analysis revealed that persuasive strategies based on Aristotle’s theory (400 B.C) were applied in the six leaders’ political speeches during the beginning of Covid-19 pandemic. Moreover, the results indicated that the most frequently persuasive strategies used by the Arab leaders are pathos and ethos strategies. Of the three strategies, it could be observed that logos and ethos strategies were the most employed by non-Arab in their speeches.
COGNITIVE AND PSYCHOSOCIAL FACTORS ASSOCIATED WITH THE PRACTICES OF BREAST SELF-EXAMINATION AMONG FEMALE YOUTHS IN THE UNIVERSITY OF IBADAN, OYO STATE.
Authors: Adeteye Damilola E , Ndikom Chizoma M , Ishola Adeyinka G , Oduyemi Rachael O , Adewole Abimbola D 
Published: April 2024

Introduction: Breast self examination, a screening technique for breast cancer, is advocated due to ease of use. However, several factors have hindered practice. This study aims to examine cognitive and psychosocial factors associated with practice of breast self-examination among female youths.

Methods: The study adopted descriptive cross-sectional design and was done in University of Ibadan. Structured sampling technique was used to select 149 respondents. Data was collected using structured questionnaire. Statistical Package for the social science (SPSS 21) was used. Data were presented as frequency and percentages while chi-square test and regression were used to test hypotheses.

Results: Mean age of respondents was within 21 and 25 years and 99.3% were aware of Breast self-examination. The mean knowledge of technique among participants was 52.45%. Association was found between knowledge and practice of breast self-examination. (x2 = 15.15, P=0.004). There is relationship between significant cognitive factors and practice of technique which were forgetfulness (x2 = 7.66, P=0.005) and lack of procedure demonstration(x2 = 7.93, P=0.005). Implicating psychosocial factors: lack of motivation (x2 = 1.31, P=0.050), lack of trust in performance (x2 = 1.65, P=0.040), and nervousness (x2 = 0.23, P=0.035) had statistical association in practice of breast self-examination.

Conclusion: There are cognitive and psychosocial factors hindering practice of breast self-examination among youths. Education addressing factors will increase regular practice.