bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Jet Nozzle Position on Mixing Time in Large Tanks
Authors: Oluwadero Timothy Ayodeji , Xuereb Catherine , Aubin Joelle , Poux Martine
Published: June 2023

The present investigation focuses on the impact of jet nozzle installation positions on mixing time in a cylindrical tank. The aim is to identify nozzle positions that achieve maximum mixing efficiency and to elucidate the governing parameters that enhance jet mixing performance. A water tank was employed for the experiment. The jet nozzle vertical inclination angle (α) and the horizontal inclination (β) determined the nozzle positions. Mixing time was determined by a tracer study, utilising a spectrophotometry approach. The findings suggest that the mixing time is significantly influenced by the jet nozzle positions and the extent of the swirling flow resulting from the horizontal placement of the jet nozzle. Regardless of the vertical inclination of the tangentially oriented nozzles, the mixing time increases with the frequency of the bulk fluid swirling flow, even when the free jet path length remains constant. The accuracy of existing models to predict mixing time was evaluated for both conventional centrally aligned (β = 0°) and newly investigated swirling flow-dominated configurations involving tangentially installed nozzles (β > 0°). Our results indicate that the turbulence jet and the circulation model provide accurate predictions for the conventional centrally aligned (β = 0°), upward-pointing jet nozzle installations. For the newly explored swirling flow-dominated configurations involving tangentially installed nozzles (β > 0°) at varying α, we present novel values for the constants of proportionality for both the turbulence jet model and the circulation model, which account for differences in mixing time. Other models largely exhibited either underestimation or overestimation of experimental data for both extreme nozzle installation positions.
Physiological and Transcriptional Analyses Provide Insight Into Maintaining Ion Homeostasis of Sweet Sorghum Under Salt Stress
Authors: Guo Huan , Nie Chun-Ya , Li Zhen , Kang Jie , Wang Xiao-Long , Cui Yan-Nong
Published: June 2023

Sweet sorghum is an important bioenergy grass and valuable forage with a strong adaptability to saline environments. However, little is known about the mechanisms of sweet sorghum coping with ion toxicity under salt stresses. Here, we first evaluated the salt tolerance of a sweet sorghum cultivar “Lvjuren” and determined its ion accumulation traits under NaCl treatments; then explored key genes involved in Na+, Cl−, K+ and NO3− transport using transcriptome profiling and qRT-PCR method. The results showed that the growth and photosynthesis of sweet sorghum were unaffected by 50 and 100 mM NaCl treatments, indicative of a strong tolerance of this species to salt stresses. Under NaCl treatments, sweet sorghum could efficiently exclude Na+ from shoots and accumulate Cl− in leaf sheaths to avoid their overaccumulation in leaf blades; meanwhile, it possessed a prominent ability to sustain NO3− homeostasis in leaf blades. Transcriptome profiling identified several differentially expressed genes associated with Na+, Cl−, K+ and NO3− transport in roots, leaf sheaths and leaf blades of sweet sorghum after 200 mM NaCl treatment for 6 and 48 h. Moreover, transcriptome data and qRT-PCR results indicated that HKT1;5, CLCc and NPF7.3-1 should be key genes involved in Na+ retention in roots, Cl− accumulation in leaf sheaths and maintenance of NO3− homeostasis in leaf blades, respectively. Many TFs were also identified after NaCl treatment, which should play important regulatory roles in salt tolerance of sweet sorghum. This work lays a preliminary foundation for clarifying the molecular basis underlying the adaptation of sweet sorghum to adverse environments.
Impact of Heavy Smoking on the Expression Levels of PGAM5, PTPRN2 and TYRO3 and Its Correlation to Male Infertility
Authors: Amor Houda , Alkhaled Yaser , Bibi Riffat , Hammadeh Mohamad Eid , Jankowski Peter Michael
Published: June 2023

Smoking have been linked to male infertility by affecting the sperm epigenome and genome. In this study, we aimed to determine possible changes in the transcript levels of the PTPRN2, PGAM5, and TYRO3 genes in heavy smokers compared with non-smokers; and to investigate their association with fundamental sperm parameters. One hundred eighteen sperm samples (63 heavy-smokers (G1) and 55 non-smokers (G2) were included in this study. Semen analysis was performed according to WHO guidelines. After total RNA extraction, RT-PCR was used to quantify the transcript levels of studied genes. In G1 a significant decrease in standard semen parameters in comparison to non-smokers has been shown (p<0.05). Moreover, PGAM5 and PTPRN2 were differentially expressed (P≤0.03 and P≤0.01, respectively), and downregulated in spermatozoa of G1 compared to G2. In contrast, no difference was observed for TYRO3 (p≤0.3). In G1, mRNA expression level of studied genes correlated negatively with motility, sperm count, normal form, vitality, and sperm membrane integrity (p<0.05). Therefore, smoking may affect gene expression and male fertility by altering DNA methylation patterns in genes associated with fertility and sperm quality, including PGAM5, PTPRN2, and TYRO3.
Effect of Carbon Nanoparticles on the Porous Texture of I-Carrageenan Based N Doped Nanostructured Porous Carbons and Implications for Gas Separation
Authors: Andrade Samantha K. Samaniego , Kállay-Menyhárd Alfréd , Klébert Szilvia , Mohai Miklós , László Krisztina
Published: June 2023

Tackling global warming and the consequent climate change in our planet is of urgent importance to our society. Renewable biomass derived carbons have been studied as promising adsorbents, but marine biomass derived carbons have not been explored extensively. S and N double-doped porous carbons were obtained from a marine biomass related precursor. Adding carbon nanoparticles (CNP) graphene oxide (GO), or carbon nanotubes (CNTs), in an early stage of the synthesis leads to a modified porous texture and surface chemistry. The carbons retained 2-4.5 at% nitrogen and 1.1 at% sulfur. The best GO and CNT added carbons had an apparent surface area 1780 m2/g and 1170 m2/g, respectively, compared to 1070 m2/g of the CNP-free matrix. The Dubinin-Radushkevich (DR) and Henry models were used to assess the strength of the interactions between various gases and the surface. The N2/H2 and CO2/CH4 selectivities were estimated with ideal adsorbed solution theory (IAST). While the CNPs, particularly GO, had a remarkable effect both on the porous texture and the surface chemistry, their influence on selectivity is more modest.
Groundwater as a reservoir for plant pathogenic bacteria : the case of Pseudomonas syringae complex in the alluvial aquifer of Avignon
Authors: Berge Odile , Nofal Salah , Razan Frédérique , Chandeysson Charlotte , Guilbaud Caroline , Cognard-Plancq Anne-Laure , Morris Cindy E.
Published: June 2023

There is significant lack of information on the presence of plant pathogens in aquifer, increasingly used for irrigation during last decades and considered mostly free of them. In this work, we report the presence of bacteria from the Pseudomonas syringae complex (referred as PsyC) including phytopathogenic bacteria, in groundwater of Avignon, France. Their concentration was variable and inversely correlated with water electrical conductivity. Their mean abundance were hundred times lower than in the river Durance, connected with the aquifer but surprisingly, their genetic structure were more homogeneous. Moreover, most strains (97 %) from groundwater were tested as potentially pathogenic on plants, when in the river they were only 71 %. Determinants of this low diversity and high aggressiveness remain to be identified. We conclude that aquifers must be considered potential plant pathogenic reservoirs even if more surveys are needed to understand the real impact on crops during irrigation. These results could be included in prediction models and new approaches to disease forecasting and surveillance and could lead to adaptation of agricultural practices.
On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models
Authors: Blachnik Marcin , Przyłucki Roman , Golak Sławomir , Ściegienka Piotr , Wieczorek Tadeusz
Published: June 2023

Scanning underwater areas, using magnetometers, in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a set enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To meet this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance of the numerical model with the results of physical tests. In addition, the paper discusses the methodology of simplifying the computational model, allowing for almost three times reduction of the calculation time. In addition, in the work we present the methodology of creating an appropriate data set, enabling the generation of any number of training samples.
Hydrogel-Forming Microneedles for Transdermal Drug Delivery
Authors: Hou Xiaolin , Li Jiaqi , Ruan Hang , Long Meng , Hong Yongyu , Feng Nianping , Zhang Yongtai
Published: June 2023

Transdermal drug delivery (TDD) is one of the key approaches for treating diseases, avoiding first-pass effects, reducing systemic adverse drug reactions and improving patient compliance. Microneedling, iontophoresis, electroporation, laser ablation and ultrasound facilitation are often used to improve the efficiency of TDD. Among them, microneedling is a relatively simple and efficient means of drug delivery. Microneedles usually consist of micron-sized needles (50-900 μm in length) in arrays that can successfully penetrate the stratum corneum and deliver drugs in a minimally invasive manner below the stratum corneum without touching the blood vessels and nerves in the dermis, improving patient compliance. Hydrogel-forming microneedles (HFMNs) are safe, non-toxic, no residual matrix material, high drug loading capacity, controlled drug release and are suitable for long-term, multiple drug delivery. This work reviewed the characteristics of skin structure and transdermal drug delivery; introduced TDD strategies based on hydrogel microneedles; summarised the characteristics of HFMNs TDD systems and the evaluation methods of HFMNs; and the application of HFMNs drug delivery systems in disease treatment. The HFMNs drug delivery system has a wide scope for development, but the translation to clinical application still has more challenges.
Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives
Authors: Distante Alfredo , Marandino Laura , Bertolo Riccardo , Ingels Alexandre , Pavan Nicola , Pecoraro Angela , Marchioni Michele , Carbonara Umberto , Erdem Selcuk , Amparore Daniele , Campi Riccardo , Roussel Eduard , Calio Anna , Wu Zhenjie , Palumbo Carlotta , Borregales Leonardo D , Mulders Peter F.A. , Muselaers Constantijn H.J.
Published: June 2023

No abstract available
Film Boiling Around a Finite Size Cylindrical Apecimen - A Transient Conjugate Heat Transfer Approach
Authors: Cukrov Alen , Sato Yohei , Boras Ivanka , Ničeno Bojan
Published: June 2023

The DNS of film boiling requires strong computational resources that are difficult to obtain for a daily CFD use by the practitioners of the industrial R&D experts. On the other hand, the film boiling experiments are associated with the usage of the expensive and highly sophisticated apparatus, and the research to this end is found to be relatively difficult due to high heat flow rates that are present in the process itself. When combined with a transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid has been proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, the turbulent flow has been found as an important parameter in obtaining the accurate solution of the temperature field evolution in a solid specimen, via the proper modeling of turbulent kinetic energy (TKE) value, that has been imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin-Helmholtz instability theory in conjunction with the boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10 % of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient has matched the error bands prescribed by the authors of the paper that has presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30 % higher. The further inspection has revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method has shown full capability for further pursuing the industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach.
A Comprehensive Review of EV Lithium-Ion Battery Degradation
Authors: Rufino Júnior Carlos Antônio , Sanseverino Eleonora Riva , Gallo Pierluigi , Amaral Murilo Machado , Koch Daniel , Kotak Yash , Schweiger Hans-Georg , Zanin Hudson
Published: June 2023

Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.
TRényi’s Entropy for the Past Life Distribution With Application in Inactive Coherent Systems
Authors: Kayid Mohamed , Shrahili Mansour
Published: June 2023

For a system that turns out to be inactive in time t, the past entropy is considered as an
uncertainty measure for the past lifetime distribution. In this study, we consider a coherent system
that includes n components and has the property that all the components of the system have failed at
time t. To assess the predictability of the coherent system’s lifetime, we use the system’s signature
to determine the Rényi entropy of its past lifetime. We study several analytical results, including
expressions, bounds, and order properties for this measure.
Recent Advances in β-glucosidase Engineering: A Brief Review
Authors: Zhao Xihua , Huang Yunhong , Ouyang Bei , Wang Guoping , Zhang Nian , Zuo Jiali
Published: June 2023

β-glucosidases (BGLs) play a crucial role in the degradation of lignocellulosic biomass as well as in industrial applications such as pharmaceuticals, foods, and flavors. However, the application of BGLs has been largely hindered by issues such as low enzyme activity, product inhibition, low stability, etc. Many approaches have been developed to engineer BGLs to improve these enzymatic characteristics to facilitate industrial production. In this article, we review the recent advances in BGL engineering in the field, including the efforts from our laboratory. We summarize and discuss the BGL engineering studies according to the targeted functions as well as the specific strategies used for BGL engineering.
New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making
Authors: Iftimia Nicolae , Pandya Raj , Mahoney Forest
Published: June 2023

(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations.
Outdoor Fitness Untuk Kalangan Penyandang Cacat/Disabilitas Di Kawasan Wisata Gunung Pegat, Desa Karangasem, Kabupaten Sukoharjo
Authors: Kristiawan Budi , Hadi Syamsul , Himawanto Dwi Aries , Yaningsih Indri , Budiana Eko Prasetya , Wijayanta Agung Tri
Published: June 2023

Pariwisata adalah salah satu faktor pendorong ekonomi di sebuah daerah. Perkembangan sektor pariwisata suatu daerah dapat memacu pembangunan sarana infrastruktur yang lain, seperti hotel, minimarket, jalan raya dan lain-lain. Dengan mengembangkan sektor pariwisata, maka akan terciptanya lapangan pekerjaan yang baru dan mengurangi pengangguran. Salah satu pariwisata yang sedang menjadi minat masyarakat adalah wisata jasmani. Selain menjadi sarana menghilangkan stres, wisata jasmani juga bisa menjadi sarana masyarakat untuk tetap menjaga kesehatan dan kebugaran tubuh. Salah satu wisata jasmani yang belakangan ini menjadi minat masyarakat adalah sarana alat fitness outdoor. Setiap sarana fasilitas umum termasuk sarana pariwisata haruslah ramah terhadap pengguna disabilitas. Pengabdian kepada masyarakat di kawasan wisata Gunung Pegat Desa Karangasem bertujuan meningkatkan dan menggairahkan roda perekonomian melalui obyek wisata dengan menempatkan sarana alat fitnes outdoor yang ramah dengan pengguna disabilitas.

Kata Kunci: Outdoor fitness; disabilitas; pariwisata
Social Media Marketing and Its Influence on Brand Equity: The Mediating Role of Word of Mouth and E-Word of Mouth
Authors: Dhewi Titis Shinta , Kurnianto Mickhael
Published: June 2023

The purpose of this study is to examine the nexus between social media marketing and brand equity, and the role of word of mouth and electronic in mediating this connectivity word of mouth. A quantitative approach with a descriptive explanatory design is employed to estimate the link between the variables observed. The study adopts purposive sampling technique with a sample size of 200 participants, and data is analyzed using Path Analysis with Warp-PLS 3.0. The findings indicate that social media marketing positively influences brand equity, and both word of mouth and e-word of mouth play a vital role in escalating brand equity. The study also confirms the mediating role of word of mouth and e-word of mouth in explaining the connectivity between social media marketing and brand equity. The results suggest that the assimilation and educational-based tourism management can promote tourism through social media to enhance the value of tourist attractions’ names, encouraging the public to enlarge in word-of-mouth promotion.
The Negation of Divinity in View of Human Emancipation: Nurcholish Madjid Perspective
Authors: Prihambodo Dimas , Tambusai Muhammad Kharis
Published: June 2023

The RUU HIP is an abbreviation of the Rancangan Undang Undang Haluan Ideologi Pancasila (the Draft Law on Pancasila Ideology). The RUU was proposed by the DPR RI, Badan Legislatif Dewan Perwakilan Rakyat Republik Indonesia (the Legislative Body of the House of Representatives of the Republic of Indonesia). In its development, there was resistance from various groups which led to demonstrations from social groups or organizations. One of the criticisms is the reduction in the value of Pancasila which can lead to the de-emancipation of the Indonesian nation. The conclusion obtained is that the RUU HIP contains efforts to reduce divine values and only leaves human values, namely mutual cooperation. In addition, the interpretation carried out contains inconsistencies, disorientation, and the disintegration of Pancasila. Therefore, the efforts made will depart from the good goals that have been formulated. So that the expected emancipation will be inversely proportional to the de-emancipation of the Indonesian nation.
Autonomous Inflow Control Valve for Medium-Light Oil and its Effective Performance
Authors: Langaas Kåre , Gisholt Einar , Bjerke Anita , Mathiesen Vidar
Published: June 2023

Most horizontal oil wells will after a time start producing unwanted fluids. Autonomous Inflow Control Valves (AICV) may help to choke these unwanted fluids and consequently improve the carbon efficiency. This paper publishes new experimental data describing how an AICV handles a medium-light oil (6 centipoise), water and gas at full reservoir conditions. A further objective is to evaluate how the AICV might impact well performance under various conditions.

To verify the single and multi-phase flow behaviour of the AICV for medium-light oil viscosity, an extensive multi-phase flow loop campaign was performed. The test was performed under real reservoir conditions, i.e., with formation water, reservoir oil and hydrocarbon gas at the given reservoir temperature and pressure. Preceding the external and independent verification, internal laboratory studies were performed with model fluids. A simple conceptual reservoir model with realistic boundary conditions was built to explore and understand the impact of this AICV for various reservoir scenarios.

At various differential pressures the single-phase oil, water, and gas rates were measured. Performance at varying water and gas fractions were measured to get improved understanding and knowledge of multi-phase flow occurring in a well. The results show clearly that the AICV will choke gas and water effectively, both at single and multi-phase flow conditions. The external and independent verification are consistent with the internal laboratory evaluations with model fluids. The AICV shows roughly a linear transition from 100% oil to 100% gas performance, and similar for 100% oil to 100% water, implying that the AICV will always prioritize sections with the largest oil fraction. A mathematical model match of the AICV performance is possible via the 10-parameter extended AICD equation, that enables practical evaluation of the AICV in industry standard reservoir simulators. Various scenarios are explored with a conceptual reservoir model and the AICV shows its capacity to reduce water production and enable more gradual and controlled increase in gas-oil-ratio for most scenarios. AICV used in segmented reservoirs shows the largest potential to reduce unwanted fluids and in addition increase oil recovery. In cases with uncertain aquifer and/or gas cap strength, or large variation in effective permeability, the AICV will make an infill well more robust as it autonomously adapts to reality and chokes unwanted fluids and consequently enables more carbon efficient reservoir management.
Delivering NET ZERO– A Case Study of Minimized Carbon Intensity Production Using Autonomous Inflow Control Technologies from a Remote Location in the Peruvian Amazon
Authors: Moradi Mojtaba , García Willy , Amado Percy Martin , Konopczynski Michael Robert
Published: June 2023

Growing energy demand heightened by climate change challenges has seen the oil and gas industry tightly embrace smarter and more sustainable technologies. The motivation is to quickly grasp net-zero targets, while safely optimising oil-gas production.

By its nature, the industry has the ingenuity to eliminate unnecessary carbon emissions. However, traditional development plans relied on the use of wells with minimal or no emphasis on the well completion in terms of optimum deliverability. This would produce a mixture of oil and excessive unwanted fluids such as water and/or gas which requires costly energy-intensive processes. Although the process has been optimized to some extent and often re-injects these unwanted fluids back to the reservoir, there has been not enough attention to the environmental impacts as these repetitive treatment processes of the fluids results in discharging excessive and unnecessary Greenhouse Gas (GHG) into the atmosphere. The issue is now widely recognized to be one of the industry challenges in its drive toward net-zero energy delivery.

A case study of a heavy crude oil field with a strong water drive, located in a natural reserve in the Marañon basin of the Peruvian Amazon is presented. Here, the implementation of autonomous inflow control devices (AICDs) technology, through a knowledge management process, has made it possible to significantly reduce the volumes of water produced, which are reinjected again, thus generating significant savings in fluid lifting, treatment and energy consumption associated with the operations in this field.

The study introduces a workflow that uses a publicly available GHG footprint estimator to evaluate the carbon intensity of different oil and gas field development plans. The estimator predicts the amount of GHG emitted from any individual operation, process and treatment involved in a field development from exploration to delivery at the gate of a refinery. Having this calculation enables the operators to recognize the major GHG emitter operations and optimise the process toward net zero using new technologies, methods and/or workflows. The workflow has then been applied to the field located in the Peruvian Amazon to illustrate the significant impact of flow control technologies on the reduction of GHG emissions and achieving net-zero targets. For example, the amounts of carbon intensity, GHG emission and energy consumption from the field have been estimated to been reduced by up to 56%, 64% and 78% respectively with AICD completions compared to a case of non-AICD completion such as stand-alone screen (SAS) was installed in the wells instead.

This study provides the engineers with a workflow to quantify the impacts of the use of new technologies especially flow control devices. It also illustrates the significant role of flow control technologies in achieving net-zero production.
Machine-Learning Based Selection of Candidate Wells for Extended Shut-In Due to Fluctuating Oil Prices
Authors: Lobut Beyza , Artun Emre
Published: June 2023

Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant drops in the prices, shutting in wells for extended durations such as 6 months or more may be considered for economic purposes. For example, prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells. In the case of partial shut-in, selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved. In this study, a mature oil field with a long (50+ years) production history with 150+ wells is considered. Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates. We aimed to solve this decision-making problem through unsupervised machine learning with the help of the data obtained during production. Average reservoir characteristics at well locations, well performance statistics and well locations are used as potential features that could characterize similarities and differences among wells. After a multivariate data analysis that explored correlations between all parameters, K-means clustering algorithm was used to identify groups of wells that are similar with respect to aforementioned features. Using the field’s reservoir simulation model, scenarios of shutting in different groups of wells were simulated. 3 years of forecasted reservoir performance was used for economic evaluation that assumed an oil price drop to $30/bbl for 6, 12 or 18 months. Results of economic analysis were analyzed to identify which group of wells should have been shut-in by also considering the sensitivity to different price levels. It was observed that well performances can be easily characterized in the 3-cluster case as low, medium and high performance wells. Analyzing the forecasting scenarios showed that shutting in all or high- and medium-performance wells altogether during the downturns results in better economic outcomes. The results were most sensitive to the oil price during the high-price era. This study demonstrated the effectiveness of unsupervised machine learning in well classification, particularly for the problem studied. Operating companies may use this approach for selecting wells for extended durations of shut-in in periods of low oil prices.
Energy Transition Strategies to Implement Energy Security of Natural Resources: Albania Case Study
Authors: Hoxha Beso Buranaj , Ternava Trim , Bucaj Dafina
Published: June 2023

The staggering vulnerability of most conventional energy sources has caused the need to diversify the energy mix for most countries, if not all with most recent issues. Subsequently, numerous countries are experiencing a surge in energy demand and are confronted with the need to meet this increase in energy demand with a response to be ‘clean energy’ at an affordable price.

The scope of this paper focuses on defining the different drivers and strategies that developing countries are utilizing to transform their local and interconnected energy sector. For ‘developing’ countries, the primary (current-) focus is on the fundamental commitment for energy diversification (e.g., energy security) in order to achieve sustainable self-sufficient energy source/s that are less impacted by socio-economical or geo-political factors. Furthermore, in this paper, specifically, the case study of Albania will be explained and how its strategy aligns with the global energy transition pathway. Additionally, the paper explores new alternative energy sources; technologies that before were barely considered, and how/why they are being deployed for adoption.
Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells
Authors: Baronio Emanuele , Abdo Elia , Mauro Stefano , Troise Mario , Salamina Laura
Published: June 2023

Underground gas storage (UGS) are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. Today, with the advancement of continuous remote monitoring and digitalization, evaluating the integrity of UGS wells has become quicker and more efficient. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity.

UGS wells present additional criticalities with respect to normal production wells due to its longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for a safe management of the well barriers. The proposed digital twin has been developed using material and energy balances and considering each annulus as a separate control volume. Each control volume can exchange heat and mass through predesigned barriers. Simulating evolution in time of pressure and temperature in the control volumes., and comparing results with data from field, allows the identification of position and size of leaks. A genetic algorithm is applied to optimize placement of leaks on their specific barriers. The system aims to identify the position and dimension of possible leaks by matching historical pressure, temperature, and flow data. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned.

The system has been in use for little over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcased a clear readiness for predictive capabilities aimed to select, plan and design fit for purpose mitigating actions.

This paper highlights the power that a digital twin can present leveraging field data with advanced algorithms. The paper also showcases workflows that allow convenient, efficient, and timely evaluation of well integrity, which leads to safer operating conditions and lower operational costs.
High-Temperature DAP Treatments of Carbonate Rocks for Proppant Embedment Severity Mitigation
Authors: Samarkin Yevgeniy , Amao Abduljamiu Olalekan , Aljawad Murtada Saleh , Sølling Theis Ivan , AlTammar Murtadha J. , Alruwaili Khalid M.
Published: June 2023

Fractured carbonate formations composed of chalk and limestone rock lithologies develop several issues over time, reducing fractures’ conductivity. One such issue is the embedment of the proppant that happens due to the soft nature of the carbonate rocks. Reduction of fractures’ conductivity results in the need for refracturing operations that require pumping tremendous amounts of water. The refracturing operations can be avoided if the fractures are maintained conductive for a longer time. This research targets reducing the severity of proppant embedment issues in carbonate formations through rock hardening by diammonium hydrogen phosphate (DAP) treatment.

The chalk and limestone rock samples were treated with a DAP solution of 0.8M concentration at three temperatures, namely 30°C (ambient), 50°C, and 80°C. The samples were treated by immersion in solution, in which rocks were kept reacting for 72 hours. The treated samples were analyzed using the SEM-EDX technique to identify new minerals and changes in the morphology of the rock samples. Moreover, the changes in the hardness of the samples were analyzed by the impulse hammering technique. In addition, the proppant embedment scenario was mimicked in the rocks by utilizing Brinell hardness measurements before and after their treatment.

The SEM analysis demonstrated that the treatment of carbonate rocks with a DAP solution results in the formation of hydroxyapatite (HAP) minerals. In addition, it was observed that the temperature of the treatment affects the crystallization patterns of the HAP minerals. Further results demonstrated that DAP treatment at elevated temperatures significantly improves the hardness of the samples. Young’s modulus of the rock samples increased by up to 60 - 80% after the treatment. In addition, studies have shown the improvement of rocks’ resistance to indentations. The sizes of the dents created by the Brinell hardness device were smaller than before the treatment. Overall, it was demonstrated that the Brinell hardness of the rock samples improved by more than 100%.

This research demonstrated that treating carbonate rocks with DAP solution results in their hardening and improved samples’ resistance to indentation. Moreover, the treatment of rock samples at temperatures similar to reservoir conditions even further improves the mechanical properties of the carbonate rocks. Upscaling laboratory DAP treatment techniques for reservoir applications will introduce new practical methods for maintaining the long-term conductivity of propped fractures. Such a procedure will help avoid refracturing operations, resulting in better and more sustainable management of water resources.
Role of the Hydromechanical Properties of Fault on Fluid Injection-Induced Seismicity with Rate-And-State Dependent Friction Model
Authors: Ma Tianran , M. Nick Hamidreza
Published: June 2023

Injecting fluid into subsurface strata has the potential to cause earthquakes by altering pore pressure and subsurface stress. To assess the seismic hazard associated with subsurface flow processes, it is necessary to understand the underlying mechanics of fluid-induced fault reactivation. In this study, we conduct a coupled hydro-mechanical modeling of fluid injection to a strike-slip fault with rate-and-state friction. We account for the fluid flow across and along the fault, as well as the hydromechanical properties of faults in the normal and tangential directions. We model the injection-induced slip of a strike-slip fault, and the simulation results indicate that there are two primary factors that affect injection-induced seismicity. The first factor is that the initiation of rupture is directly related to the diffusion of pore pressure in the near field where there is high shear stress and a large reduction in fault strength due to the significant pressure change. The second factor is that the transfer of shear stress from the nucleation zone promotes the advancement of the slip front to the near- and far field. Our results are quite conservative since the model chose pf as the relevant pressure when calculating the effective normal stress and the shear stress has a slight effect on the pressure variation. Finally, the sensitivity analysis indicates that greater tangential permeability values delay the onset of fault rupture and diminish the likelihood of fault reactivation. Higher stiffness induces fault slip earlier but reduces its magnitude.
Development of a Deterministic Total Organic Carbon (TOC) Predictor for Shale Reservoirs
Authors: Khan Mohammad Rasheed , Kalam Shams , Asad Abdul , A. Abu-khamsin Sidqi
Published: June 2023

Unconventional reservoirs like shale oil/gas are expected to play a major role in many unexplored regions, globally. Shale resource evaluation involves the estimation of Total Organic Carbon (TOC) which correlates to the prospective capability of generating and containing hydrocarbons. Direct measurement of TOC through geochemical analysis is often not feasible, and hence researchers have focused on indirect methods to estimate TOC using analytical and statistical techniques. Accordingly, this work proposes the application of artificial intelligence (AI) techniques to leverage routinely available well logs for the prediction of TOC. Multiple algorithms are developed and compared to rank the most optimum solution based on efficiency analysis.

Support Vector Regression (SVR), Random Forest (RF), and XGBoost algorithms are utilized to analyze the well-log data and develop intelligent models for shale TOC. A process-based approach is followed starting with systematic data analysis, which includes the selection of the most relevant input parameters, data cleaning, filtering, and data-dressing, to ensure optimized inputs into the AI models. The data utilized in this work is from major shale basins in Asia and North America. The AI models are then used to develop TOC predictor as a function of fundamental open-hole logs including sonic, gamma-ray, resistivity, and density. Furthermore, to strengthen AI input-output correlation mapping, a k-fold cross-validation methodology integrating with the exhaustive-grid search approach is adopted. This ensures the optimized hyperparameters of the intelligent algorithms developed in this work are selected. Finally, developed models are compared to geochemically derived TOC using a comprehensive error analysis schema.

The proposed models are teted for veracity by applying them on blind dataset. An error metrics schema composed of root-mean-squared-error, and coefficient of determination, is developed. This analysis ranks the respective AI models based on the highest performance efficiency and lowest prediction error. Consequently, it is concluded that the XGBoost and SVR-based TOC predictions are inaccurate yielding high deviations from the actual measured values in predictive mode. On the other hand, Random Forest TOC predictor optimized using k-fold validation produces high R2 values of more than 0.85 and reasonably low errors when compared to true values. The RF method overpowers other models by mapping complex non-linear interactions between TOC and various well logs.
Estimation of Permeability, Skin, and Inflow Profile in Multilayered Systems from Temperature Transient Data Using a Coupled Nonisothermal, Transient Reservoir and Wellbore Model
Authors: Alan Cihan , Cinar Murat , Onur Mustafa
Published: June 2023

The objective of this paper is to investigate the estimation of layer permeability, skin, and inflow profile from observations of production-logging-tool (PLT) and/or distributed temperature sensing (DTS) for a multilayered system where the layers communicate only through the wellbore. To achieve this objective, we develop a thermal, transient coupled reservoir/wellbore simulator that numerically solves transient mass, momentum, and energy conservation equations simultaneously for both reservoir and wellbore. The simulator accounts for the Joule-Thomson (J-T), adiabatic expansion, conduction, and convection effects for predicting the flow profiles across the wellbore. A comparison of the developed model with a commercial simulator is provided for the single-phase fluid flow of oil or geothermal brine from partially penetrating vertical or inclined wells with distinct fluid and formation properties. A sensitivity study on transient pressure, rate, and temperature profiles to identify the effect of the layer petrophysical properties and the layer thermophysical parameters is also conducted through synthetically generated test data sets from the developed simulator. In addition, nonlinear parameter estimation with the use of both profiles is shown to be useful to reveal permeability and skin information about individual layers. The results show that temperature transient data are more reflective of the properties of the near wellbore region, while wellbore pressures are determined more by average reservoir parameters. The simulator proves practical for designing a PLT test provided that limitations such as single-phase fluid flow having vertical or inclined well equipped with a thorough fluid characterization (EOS) are met. Such design tests may provide a good source for cross-checking PLT flow profiles and validating the fluid contributions from layers that are open to flow. It is often that the spinner of the field PLT tool does not operate properly at very low flow rates. Also, the spinner may fail to calculate and construct PLT plots accurately at very high flow rates. To the best of our knowledge, this is the first study that presents a coupled transient reservoir/wellbore model for predicting layer permeability, skin, and inflow profile of a well from observations of pressure, temperate, and/or rate data from production-logging-tools (PLTs) and/or distributed temperature sensing (DTS) fiber optic cables.
Flow of Viscoelastic Polymer Solutions in Porous Media: Influence of Molecular Weight and Dispersity
Authors: Langanke Nils , Leblanc Thierry , Fadili Ali , Hincapie Rafael E. , Ganzer Leonhard
Published: June 2023

The properties of polymeric materials are commonly modified by adjusting the dispersity of the molecular weight distribution, since polymer properties are dominated by intermolecular interactions. We utilized this approach to alter the rheological behavior of polymer solutions for application sub-surface and other porous media flow. We correlate the molecular weight distributions with screen factor measurements and in-situ rheological behavior. Aqueous solutions were prepared using mixtures of partially hydrolyzed polyacrylamide (HPAM) having different molecular weights. The behaviour of the solutions was studied in single-phase flooding experiments using Bentheimer and Berea outcrops, as well as a glass-silicon-glass microfluidic device that mimics porous media. The in-situ rheological behavior determined from flooding experiments was monitored by differential pressure measurements. To improve data accuracy, the core flooding experimental set-up was equipped with multiple pressure sensors along the core. Polymer solutions of same shear viscosity but significantly different dispersities were utilized for the investigation. Elongational viscosities were determined by screen factor measurements. We show that the apparent viscosity during polymer injection is significantly altered for polymer solutions of same average molecular weight but different dispersity. Namely, the onset of shear thickening occurs at lower equivalent shear rates when dispersity is high. Furthermore, the flow of polymer solutions in porous media was correlated to screen factor measurements. This effect of the dispersity of the molecular weight distribution can be used to optimize polymer solution applications in porous materials.