bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estudo cinético de uma reação química
Authors: Andrade João Carlos de 
Published: April 2024

Existem reações tão rápidas que parecem instantâneas, enquanto outras são tão lentas que podem se estender por horas, dias, meses e até anos, como é o caso de muitos decaimentos radionuclídicos. Um exemplo interessante é a reação química entre os gases H2 e O2. Quando misturados em condições normais em um sistema isolado, esses gases reagem muito lentamente. Entretanto, se a mistura for ativada por uma centelha ou chama, ocorrerá em uma reação exotérmica descontrolada extremamente rápida, com grande liberação de energia, chamada explosão térmica. Foi o que aconteceu no histórico acidente do dirigível Hindenburg em 1937, em New Jersey, nos Estados Unidos. Por outro lado, ao ser conduzida em condições de queima controlada, essa reação pode ser utilizada como propelente. De fato, esta é uma das reações químicas mais energéticas conhecidas, capaz de fornecer energia suficiente para lançar grandes foguetes, como o Saturno V. O estudo da cinética química (e, por implicação, das velocidades das reações), por ter muitas aplicações científicas e práticas, merece um destaque logo no início dos cursos de Química. A introdução de experimentos envolvendo estudos cinéticos levam os alunos a reconhecerem a importância dos efeitos da temperatura sobre a velocidade de uma reação, bem como as influências que variações na concentração podem ocasionar. Um dos muitos experimentos didáticos utilizados para esse fim envolve a reação de decomposição dos íons tiossulfato em meio ácido, com a formação de enxofre coloidal. Por meio dessa prática de laboratório é possível verificar a influência da concentração e da temperatura na velocidade de reação, medindo-se o tempo decorrido entre a mistura dos reagentes (t≅0) e o aparecimento dos primeiros vestígios de turvação no meio reacional. Esta prática é interessante pelo fato da sua realização ser bastante simples e os resultados serem bem visíveis.
Advanced Realtime Early Kick and Loss Detection (EKLD) Through a Hybrid Data-Driven and Physics-Constrained Approach
Authors: Zhang Shang , Samuel Robello 
Published: April 2024

Abstract
In real-world drilling operations, rapid and precise detection of early signs of kicks and losses is crucial for safe and cost-effective procedures. Failure to promptly monitor and address kick and loss events can result in severe consequences, including drilling downtime, asset losses (including facilities and personnel), significant increase of drilling costs, etc. In this paper, we presented a novel, advanced method employing a hybrid data-driven and physics-constrained approach, with a specific focus on early kick and loss detection (EKLD) for real-time application. With the latest real-time technologies, simultaneous monitoring of various downhole and surface drilling parameters has become more efficient and convenient. However, the increasing data volume generated by complex systems with various sensing devices can often become overwhelming for the monitoring system. The constant alerts from alarm system can lead to user fatigue and, consequently, undermine the performance and reliability of the active monitoring system in practical use, as users become less responsive due to frequent false alarms. Although recent studies have focused on detecting kicks or losses using data from multiple sources collected by the rig, a gap persists between academic models and real-world applications. The hybrid model presented in this paper, utilizing the robustness of data-driven approaches and the efficiency of physics-based models, while also addressing field issues such as low data quality and inaccurate measurements. The model was tested with real-time drilling data, and field results demonstrated its strong performance in predicting both kick and lost circulation events during drilling operations from real-time data stream from the rig. A performance comparison was conducted between the traditional kick detection model and the presented model. The comparison reveals that the new model significantly reduces the false alarm rate and exhibits greater reliability than traditional models.
Monitoring Low and Intermittent Methane Emission from Orphaned and Idle wells
Authors: Daneshvarjejad Nima , Khodaparast Pooya , Cho Young , Paul Donald , Hauser Mike , Farhadi Shahram , Ershaghi Iraj 
Published: April 2024

Abstract
Environmental concerns necessitate monitoring methane leaks from idle and abandoned wells in real time. The system proposed here incorporates Industrial Internet-of-Things (IIoT) capabilities to detect and monitor methane emissions from idle and abandoned wells, considering environmental effects such as wind, temperature, and relative humidity.
We conducted extensive laboratory, outdoor, and field experiments to refine and validate the system's effectiveness in detecting and monitoring methane emissions from abandoned wells. These experiments have provided datasets that were incorporated in our design strategy. The system integrates state-of-the-art methane detection sensors capable of identifying ultra-low methane concentrations. These sensors are part of a unit that combines communication capabilities with mobile power. Our innovative canopy design counters wind-induced dilution of methane concentration, balancing disruption with natural ventilation. In addition to the canopy, an IIoT with a wind sensor was placed near the methane monitoring IIoT to record wind speed and direction. This wind data informs necessary design modifications to minimize wind impact on methane concentration readings.
The solution proposed effectively detects methane leak rates of nearly one gram per hour. Our survey indicates that the solution proposed is the first to detect methane leaks at this low level. This solution can equip operators with real-time data on asset integrity and provides scalability and remote sensing.
Labeling Job Type and Technology for Large Operational Datasets
Authors: Hassig Fonseca S. , Jan A. , Prokopetc K. , Ugarte E. , Vodnikov N. , Spesivtsev P. , Ramondenc P. , Venkataramanan L. 
Published: April 2024

Abstract
Each year, coiled tubing (CT) well intervention fleets produce terabytes of multimodal data; these are recorded from surface and downhole sensors on each job. Among these data are job type(s) and technologies used on each job; traditionally, a field crew manually supplies this information. Given the diversity of data, many acquisition labels are often missing or inaccurate. A multimodal framework is presented that automatically identifies the job type and technologies used during an acquisition.
The proposed framework leverages different types of data depending on the job and technology to identify. Most job types (e.g., CT milling, CT fishing), and general technologies (e.g., downhole telemetry, jetting nozzles), are identified through a natural language processing (NLP) algorithm applied to operational reports. The presence of downhole technologies with greater granularity and certainty (e.g., downhole 2 1/8-in. pressure and temperature sonde) lies in the detection of meaningful information or noise on specific channels and follows a logic mimicking human interpretation. Finally, CT cementing and electronic firing heads are identified through statistical metrics and pattern recognition.
The framework leverages several tested methods. Primary job types and general technologies are classified using NLP. Of 366 acquisitions in the cloud archival system, the algorithm labels 97% with job type and 26% with one or more technologies. The second method extends the resolution and number of detected technologies to cover 12 unique real-time telemetry modules such as pressure, temperature, casing collar locator, and gamma ray sondes. For this method, over 50 acquisitions are analyzed with an accuracy of 94%. Electronic firing head signatures for three unique types of firing heads are identified successfully 97% of the time on 44 different acquisitions. This identification is sped up by automatically identifying three major stages of a CT well intervention (i.e., initial run in hole, service delivery, and final pull out of hole) and restricting the search space to the relevant stage. CT cementing job type identification is tested on 52 different jobs, with an accuracy of 92% and less than 30% of false negatives.
The innovative framework automatically classifies jobs and technologies. The inherent methods combine domain knowledge with the power of machine learning to enable efficient mining of data that would otherwise remain out of reach. By automating labeling, human error is largely eliminated and the reliability of the contextual metadata is significantly improved to provide crucial insights regarding operations.
Enhancing Continuous Well Flow Rates Estimation with Ensemble Machine Learning Models
Authors: Martinez V. 
Published: April 2024

Abstract
This study introduces a novel method to enhance predictive model performance for estimating continuous well production flow rates using daily operational conditions. The approach leverages machine learning algorithms, employing bagging as ensemble technique, to consolidate predictions from multiple base models. The algorithms are trained and evaluated using operational data, including wellhead pressure, temperature, choke diameter, gas lift injection rate, and gas-oil ratio (GOR), among others. The predictive model is evaluated using historical data from a mature offshore field, where GOR increases over time. Results demonstrate the ensemble machine learning model's high accuracy, surpassing traditional Gilbert-type choke correlations, which are constrained by the applicable range of operations variables like GOR and choke diameter. These findings suggest that this approach provides a reliable and efficient solution for estimating well production flow rates, particularly when continuous flow rate measurements face limitations.
Applied Machine Learning for the Imputation of Missing Core Petrophysical Property Data in Clastic and Carbonate Reservoirs
Authors: Abdulkhaleq Hussein B. , Al-Mudhafar Watheq J. , Wood David A. , Sausan Sarah 
Published: April 2024

Abstract
Estimating missing petrophysical data, particularly in permeability/porosity core-analysis measurements is a challenge. The data gaps substantially increase uncertainty in multivariate statistical analysis degrading the accuracy of geomodeling. Data imputation algorithms can be used to estimate such missing values but their performance and reliability vary in providing usable estimates. This study comprehensively compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field a southern Iraqi field; one in the clastic Zubair Formation, and one in the carbonate Yamama Formation. Relative bias (RB) and robustness to outliers were used to assess and compare the estimates of each imputation technique. The results are presented in terms of outlier percentages and displayed using several statistical plots to compare the imputed and original data points. The results reveal that the MICE-CART data imputation method outperforms the other methods with the clastic and carbonate datasets studied. It generates low RB values, and realistic variable density distributions and demonstrates robustness to outliers. The novel workflow (combining relative bias, outlier robustness, and comparison of depth, variable distribution, and box plots) is suitable for application in both clastic and carbonate reservoir formations. It is effective at revealing the relative strengths and weaknesses of specific data imputation methods.
Challenges and Recent Advances in Modeling and Simulation of Geothermal Systems
Authors: Temizel Cenk , Odi Uchenna , Cetin Coskun , Pamukcu Yusuf , Yegin Cengiz 
Published: April 2024

The use of geothermal resources for electricity generation began in Larderello, Italy, in 1904 with an experimental generator producing 10 kilowatts of electricity (kWe) (Keller & Valduga, 1946). This initial effort led to the establishment of the first commercial unit generating 250 kWe from the geothermal fluids of two wells at 200-250 °C in 1913 (Lund, 2005). For decades, Italy was the only producer of geothermal electricity on an industrial scale until 1958 when New Zealand introduced an 11.2 Megawatts electric (MWe) facility at Wairakei (Stimac et al., 2015), which was also the first to use the "wet" steam (or flash steam) as opposed to dry steam, marking a significant expansion in geothermal energy (Lund, 2005).
After the end of World War II, the geothermal power industry saw a growth rate of 6.2%, and geothermal power plants spread globally during the 1960s. For example, Mexico, the United States, and Russia (the former Soviet Union or USSR) have adopted these energy solutions. In particular, Russia pioneered the world's first binary-cycle power plant in Kamchatka in 1967. Then, other countries including Japan, Iceland, and China joined the league of geothermal power producers by 1970 (Lund, 2005; Pambudi & Ulfa, 2024).
Initially, the geothermal sector mainly relied on steam from high-temperature sources above 150 °C. However, technological advancements allowed for the utilization of lower temperature resources at 90 °C. This was made possible thanks to using organic working fluids instead of water in the organic Rankine cycle process to improve efficiency. These are often integrated into district heating projects, expanding the range and effectiveness of energy applications. However, the optimal and flexible design of such systems under uncertainties of the future energy markets remains challenging (Wieland et al., 2023).
Hydrogen Storage in Saline Aquifers: Experimental Observations of Viscous-Dominated Flow
Authors: Zhou J. D. , Kovscek A. R. 
Published: April 2024

Abstract
Hydrogen (H2) storage in the subsurface is a potential long-term energy storage solution for renewable energy; however, hydrogen flow in the subsurface is not well understood and studied in the context of conventional reservoir engineering. A quantitative understanding of the similarities and dissimilarities between the transport behavior of H2, CO2, and CH4 is necessary to assess the feasibility of underground hydrogen storage (UHS) as well as guide the design and optimization of the storage site. A multi-phase flow experimental setup is introduced to examine the behavior of H2 transport in a brine-saturated core under different flow rates and flow regimes. Analog fluids of nitrogen (N2) and viscosified brine were used in lieu of H2 to obtain capillary number (NCa) and viscosity ratios similar to H2/brine flow at reservoir conditions. X-ray computed tomography (CT) was used to image the flow during gas injection and compute average gas saturation across the core sample. The small NCa experiment showed behavior consistent with capillary fingering and stable displacement based on the piston-like gas saturation curve. The high NCa experiment showed a mixture of viscous fingering and capillary fingering tendencies, necessitating the need for a liquid-liquid pairing study that allows for distinct viscous fingering behavior.
Fractal Approach for Naturally Fractured Reservoirs Characterization Using Reservoir Simulation
Authors: Silva-Escalante C. F. , Camacho-Velazquez R. G. 
Published: April 2024

Abstract
Fractals are present in the naturally fractured reservoirs, that implies a pressure response is affected by a power law behavior in the fracture properties showing characteristics in their pressure drop performance deviating the typical and idealized Warren and Root (1963) approach for reservoir characterization.
The work’s objective is to characterize fractal naturally fractured reservoirs for obtaining the fractal parameters and reservoir properties from a pressure drawdown coupling a black-oil reservoir simulator with a power-law based (fractal) distribution of permeability and porosity of natural fractures over the simulation grid plane, thus, analyzing the resulting pressures and production scenarios corresponding to the fractal behavior in naturally fractured reservoirs. The proposed methodology results in an alternative for fractal reservoir characterization applying reservoir simulation modelling.
A double-porosity model is constructed to match the pressure behavior with two field cases with fractal behavior identified from pressure drop performance, and noncommon flow regimes resulted because the fractal flow dimension differs from the Euclidian one, which is a fractal special case where the fractal dimension, dmf, is equal to the Euclidean dimension, d=1, 2 or 3. By a pressure history matching, the characterization of the fractal parameters is reached for both field cases as dmf = 1.386 and the connectivity index, θ = 0.17, and dmf = 1.162 & θ = 0.167, respectively. The rest of reservoir unknown parameters were obtained, and a sensitivity analysis of the fractal parameters shows the impact in the pressure responses and the underestimation in the production rate performance compared with the Euclidian 2D flow.
ESG Consciousness in Mutual Fund Advisory: Examining Attitudes, Intentions, and Moderating role of Fund Fundamentals and Herding Bias
Authors: Tiwari Kamesh , Sharma Meghna , Deka Jimnee 
Published: April 2024

Abstract

This study explores the relationship between mutual fund advisors' ESG consciousness, their attitude towards ESG funds, and their intention to advise ESG funds, while also considering the moderating influence of fund fundamentals and herding bias. Data was collected from 234 Indian financial advisors in the mutual fund domain using a Likert scale questionnaire. Amos based Structural Equation Modeling (SEM) was employed for data analysis. The results indicate a significant connection wherein advisors with heightened ESG consciousness tend to hold more positive attitudes toward ESG funds, which, in turn, positively predict their intention to provide advisory services related to ESG funds. Furthermore, fund fundamentals, encompassing financial and non-financial attributes of ESG funds, play a crucial moderating role, particularly when advisors perceive these fundamentals as robust and positive. Notably, herding bias among advisors also positively moderates the relationship, indicating that advisors with a greater inclination to follow industry trends and peer behavior are more influenced by their positive attitudes, resulting in a stronger intention to recommend ESG funds. These findings have implications for investors and advisors alike, shedding light on the dynamics of ESG-conscious investing in emerging markets and its potential impact on financial decisions.
Research on information security of urban traffic control system based on tripartite evolutionary game
Authors: Li Kailong , Pan Ke , Wang Li , Zhang Feng , Xiu Weijie , Li Min , Zhang Lili 
Published: April 2024

Abstract

In recent years, emerging technologies such as smart cities, Urban Traffic brains, and car networking are vigorously promoting the intelligent development of urban traffic signals, which means that the Urban Traffic Control (UTC) system will interact with more and more network and physical terminals, which will lead to more malicious attacks and information destruction. It will be a great threat to the personal and property safety of participants in urban traffic. In response to this severe demand, this paper studied the network topology of UTC system and based on the tripartite evolutionary game theory (TEG), A Signal control security game model involving attackers, Upper Computer (UC) and Traffic Signal Machine (TSM) is designed, and the evolutionary stability of strategy selection of each participant is analyzed. This paper discusses the comprehensive judgment principle of each element on attack cost and defense cost, compares the influence of tripartite strategy selection, and further analyzes the stability of equilibrium point in tripartite evolutionary information security game system with Lyapunov's first method. Finally, by using real data and MATLAB simulation analysis, the impact of key parameters such as attack cost, defense cost and detection rate on information security game participants is verified, making theoretical contributions to enhancing the security of urban traffic signal control system.
Analysis of effects of Correlated Color Temperature (CCT) on the
performance of Visible Light Communication (VLC) Link.
Authors: Paliwal Rajat , Patel Piyush , Atieh Ahmad 
Published: April 2024

Abstract
CA system model for the Line-of-Sight (LoS) Lambertian radiation pattern has been designed with commercially used high power white phosphorous converted white light emitting diode (PLED) of CCTs 2200K, 3000K and 5700 K and three photodiodes of quite different specifications. This system has been successfully tested for the maximum data rate of 300 Mbps and 170 Mbps for RC constant and electron life time 0.5 ns and 1 ns, respectively. The CCTs 2200k and 3000k irrespective of any particular detector have provided maximum data rates. One CCT 5700K with detector model of S5107 or S7686 for the values 0.5 ns and 1 ns has provided slight lower data rate of 200 Mbps or 150 Mbps, respectively with required BER of 10-6 for stable communication. This CCT 5700 K performance with another detector S5973/-01 has been noticed altogether different and could not sustained the VLC link and signals were not detected by the detector. It is thus concluded that both the CCTs 2200K and 3000K have performed satisfactorily with all the three detectors but one CCT 5700K with combination of detector S5973/-01 found completely mismatched as this could not even establish the link. This CCT 5700 K despite of having higher brightness and transmit power due to lower responsivity of the detector as compared with the other two CCTs supported slight lower data rates. All the three detectors have varied responsivity and other specifications which have affected performance of VLC link. For CCT 5700 K both the detectors, S5107 and S7686 have almost equal responsivity support but the detector S5973/-01 provided slight lesser as compared to other detectors. This exhibited that all the three detectors have varied performance for CCT 5700 K due to dominating in blue wavelength beside other limitations of responsivity and photo sensing area of the detectors.
The Social Art of Wealth Creation: Unpacking Alternative Philosophy for Inclusive Socio- economic Development in South Africa
Authors: Kajiita Robert Mutemi , Kang'ethe Simon Murote 
Published: April 2024

Abstract

Generally, economic investment models are primarily premised on maximising profit and accumulating wealth, a development paradigm that benefits a few. This paper focuses on social entrepreneurship as an emerging socio-economic paradigm in South Africa. The objective of the paper was to demonstrate the philosophical nuances within social entrepreneurship as an alternative socio-economic paradigm in South Africa. Through a qualitative research approach, in-depth interviews were conducted with selected social enterprises in South Africa. The data was analysed thematically. The study established that social entrepreneurship is premised on people-centredness; cultural-embedded socio-economic investments; empowerment and socio-economic emancipatory; and innovativeness. These principles advance the social investment paradigm in social enterprises by reconceptualising and contextualising wealth creation and its concomitant social impact on communities. These findings imply that social services practitioners must undergo a paradigm shift in designing and implementing inclusive and equitable-orientated socio-economic models and approaches to development.