bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Need for a Comprehensive Cement Database - A Novel Approach to Best Practices by Cataloging Cement Properties
Authors: Abraham Jocin James , Devers Cameron , Teodoriu Catalin , Amani Mahmood 
Published: December 2021

AbstractIn conventional well design, the cement sheath acts as one of the primary barriers of protection in the well integrity matrix. Once the wellbore cement is set, the well is exposed to various conditions and environments over time which can impact the integrity of the cement, the results of which are poorly studied and documented. Given that there are also multiple cement recipes and formulations – the task of studying downhole cement performance and categorizing said results becomes more complicated, requiring the need for an integrated database of information. The objective of this paper is to document desirable cement properties, develop an optimal method for presenting this data, and construct a database which integrates this information and allows streamlined data entry and retrieval.Multiple variables must be considered when aggregating cement recipes and its corresponding properties over time. To test the behavior of these cement recipes over time, samples are created and aged in various controlled environments, and the properties tested periodically. The database was developed with a suitable interface to provide intuitive data entry and practical analysis capability, with proper inputs for the types of cement used, additives added, properties of the cement mixtures over time and any corresponding analysis performed on the samples in order to maximize best practice.Differences in geology, drilling techniques and standards often require the use of different cement recipes with varied additives to cater to each job. These include accelerators, retarders, extenders, weighing agents, fluid and loss control additives, as well as special additives such as latex, fiber additives and foam cements. The database interface is designed to accommodate these variations in the cement recipes and track the properties of samples over time and give a comprehensive understanding of the behavior of the samples as they age. With information from the industry, literature, and laboratory experiments, properties such as the Unconfined Compressive Strength (UCS), thickening times, gel strength development, densities, to name few will be integrated in the data base. Data analytics strategies will also be applied on the information aggregated, and the properties of the samples over time will be correlated to field data as well as literature to ensure proper representation and accuracy of the data.The database and the knowledge collected will be utilized as a source of information to enhance common cementing practices, as well as develop and refine industry best practices, which will be applicable to any cement job in the world. Currently, the database presented in this paper contains over 1000 unique cement samples, prepared and documented by multiple individuals with an aim to create a unique cement repository and database that focuses on long term cement properties.
Value of Cement Bond Logs for Evaluation and Improvement of Cementing Practices in Extended Reach Drilling ERD Wells
Authors: Alvarado Romulo Francisco Bermudez , Habib Abdelkerim Doutoum Mahamat , Duguid Jamie Scott , Srivastava Manish , Medina Ruben A. , Rocha Michelle , Vican Kresimir , Jambunathan Venkataraman 
Published: December 2021

Abstract
This paper discusses the value of cement logs as the core input to analyze the cement quality and validate the improvements made to cementing designs and practices of the intermediate casing string in Extended-Reach Drilling (ERD) wells. The ERD wells are being drilled from artificial islands in a field offshore in the UAE. The primary cementing objectives are isolating the reservoirs from their sublayers and protecting the casing against possible future corrosion across an upper formation. Cementing challenges include higher angle deviation, higher mud weight requirements resulting from an anisotropic, unstable shale formation present above the reservoir section.
Effective reservoir management requires sound zonal isolation to eliminate crossflow between different reservoir units. In combination with standard cement bond logs (CBL), ultrasonic technology has provided detailed information about cement quality and a qualitative indication of casing position in the borehole. These have also led to valuable insight into how continued cementing designs and practices improved zonal isolation.
Improvements in cement quality seen as a result of enhanced casing centralization, optimized hydraulic model, modified cement rheology, displacement rate impact, among others, were confirmed with the cement log evaluation program. The paper will present the ultrasonic and standard CBL responses, which support the enhancements made to the cementing design and practices that yield the desired results.
The cement quality has been improved in the ERD wells intermediate section through strategic modification in cementing practices. Cement evaluation logs have played a significant role in validating the cementing methods’ development. Consistently improved zonal isolation results have opened up the opportunity for future efficiency gains by eliminating routine CBL.
Data Driven Virtual Flow Metering: Leveraging AI and Edge Computing for Continuous Multiphase Flow Measurement
Authors: Schnabl Helmut , Wimmer Helmut , Nirtl Michael , Blazekovic Sasa 
Published: December 2021

Abstract
This paper describes the use of data-driven virtual flow metering (VFM) for continuous multiphase flow measurement, which has been developed and tested in an oil field well pilot in Austria. 12 ESP (Electric Submersible Pump) wells have been modelled and fine-tuned within the pilot. Hardware-based test separators were used to conduct quality control evaluations on the predicted production rates and calibrate the well models as required. For the practical deployment of VFM systems, we have addressed the need for optimized learning and scalability of the artificial intelligence (AI) models by means of what we call soft-sensing and will explain how to successfully deploy this technology on wells with artificial lift. Notably, the application of this software-based, soft-sensing VFM in combination with hardware-based multiphase flow measurement bears the potential to significantly reduce the CAPEX cost for future metering infrastructure investments and even reduce the OPEX of existing metering hardware by extending the duration of metering cycles. This makes data-driven VFM an economical option even for low-producing wells. Details of the well pilot project conducted with OMV in Austria will be provided. The use of soft-sensing VFMs via cloud computing for continuous multiphase flow measurement is a step toward the closed-loop, fully autonomous operation of oil fields.
Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence
Authors: Vieira Ronald E. , Xu Bohan , Nadeem Asad , Nadeem Ahmed , Shirazi Siamack A. 
Published: December 2021

AbstractSolids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions.More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score.The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before.This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.
Maximizing Asset Value & Field Recovery with Advanced Completion Solutions – A Digital Twin Field Case Study with Multilateral, Intelligent Completion, and AICD Completion Technologies
Authors: Shahreyar Noman , Butler Ben , Corona Georgina 
Published: December 2021

Abstract
The drilling and completion of multilateral wells continues to expand and advance within the oil industry after three decades of accelerating adoption. The performance of these wells can be increased when integrated with advanced well completion techniques. The addition of intelligent completions (IC) and inflow control devices (ICD/AICD) enhances well performance and improves field recovery. This paper discusses a reservoir simulation case study that evaluates the productive impact these technologies provide when combined with multilateral technology (MLT), and the mechanism by which they achieve it.
A reservoir model is devised and simulates under dynamic reservoir conditions the field production of dual lateral and single bore horizontal wells. The simulation is conducted for three separate scenarios where AICD and IC are incrementally implemented. The results are compared across the scenarios and their value quantified.
The mechanisms by which estimated ultimate recovery (EUR) is increased will be discussed, including the increase of reservoir contact, drawdown distribution optimization, and the control and delay of water production.
The study will provide an overview on the theory behind the technologies. It will also review the workflow used to conduct the study, utilizing a combination of steady state nodal analysis software and dynamic reservoir simulation software. Additional information about the reservoir model, initial and boundary conditions are detailed, to provide insight into reservoir simulation methodology.
Contextual Failure Analysis Improves Reliability While Pushing the Envelope of Extended Reach Wells in a Giant Brown Field, UAE
Authors: Cantarelli Elena , Dang Khoa Le Pham , Escalera Hernan Melgares 
Published: December 2021

Abstract
The current combination of increasingly complex wellbores and tightening budgets forces operators to do more with less and find new ways to expand the drilling envelop. Often this pushes the parameters to the limit in order to achieve faster penetration rates. Operating at the limit or beyond impacts equipment reliability and project cost. A thorough failure analysis of the root cause(s) of every incident can help identify and address areas that need improvement. Identifying a cause fosters improvement while it simultaneously pushes the boundaries so the profitability of mature assets can be maximized.
Typical failure analysis attempts to determine the cause of a failure and establish corrective actions to prevent reoccurrence. In a large extended reach drilling project targeting a mature field, the approach to a single failure was expanded and projected in a proactive manner to anticipate the impact of current failure modes in future more challenging scenarios. This innovative method combines the classic failure analysis approach with a comparative approach designed to identify and classify each factor that contributed to the failure. This information is then compiled into a dynamic predictive risk matrix to improve the planning.
This method, thanks to the contextualization of individual failures and the multi-facet comparative analysis, revealed a pattern between reliability trends and environmental challenges. The pattern was correlated with the increased drilling difficulty over the lifetime of the project, and suggested that the long-established practices had to be revised to overcome the new scenario. The analysis contributed to the delineation of a strong action plan that immediately revealed a consistent service quality improvement quarter on quarter and nearly a 50% decrease in failure rate. The enhanced reliability had a direct impact on the performance that registered a significant reduction of the drilling time, thus lowering the overall well construction cost.
In today's economics where cost reduction, resource optimization and sustainability are at the top of the operator's priority list, failure analysis has become paramount to ensure continuous improvement. Effective analytic methods to identify and eliminate showstoppers are needed to minimize unplanned events and deliver within budget.
By digging deep into the root cause of incidents, this new approach to failure analysis enabled an enhanced, broader and more effective quality improvement plan that tackled service quality from multiple angles. From refining bottomhole assembly (BHA) design and risk matrix to drafting field guidelines and roadmaps, this approach also provided extra guidance and risk awareness for future well planning improvement. This particularly applies to mature fields where wellbore complexity increases at the same time budgets decrease and it's necessary to improve operational excellence to assure profitability.
Service Exchange or Major Overhaul. Which Philosophy to Implement for Gas Turbine
Authors: Youssef Karim Mamdouh 
Published: December 2021

Abstract
Maintenance costs and machine availability are two of the most important concerns to gas turbine equipment owner. Therefore, a well thought out maintenance program that reduces costs while increasing equipment availability should be instituted.
The correct implementation of planned maintenance relying on preventive maintenance optimization through perfect inspection frequency and scope provides direct benefits in the avoidance of forced outages, unscheduled repairs, and downtime.
Major overhaul is carried out for each gas turbine every 48,000 firing hours which costs around 1 M USD for each engine and with more than 8 months unavailability for the unit.
To increase equipment availability and enhance cost and time efficiency, alternatives approaches were evaluated including Service Exchange of gas turbines. It is found that service exchange is the best option for optimizing time and cost of overhaul of such engines.
This paper is written to improve Major Overhaul practice for existing Gas Turbines from ongoing practice of routine major overhaul including engine strip down, inspection and repair to Service Exchange of Gas Generator and Power Turbine every 48,000 firing hours.
Enzootic Activity of Chlamydia in Farms Located in a Hotspot Area for Zoonosis Emergence
Authors: Vanderhoeven Ezequiel Andres , Mosmann Jessica P. , Díaz Adrián , Cuffini Cecilia G. 
Published: December 2021

Abstract
Chlamydias are obligated intracellular Gram-negative bacteria, considered important zoonotic pathogens, broadly present in several bird species and responsible for economic losses in animal production. We analyzed the presence of Chlamydial species with zoonotic risk in farm animals in a highly biodiverse area and with great human circulation, the Argentine, Brazil and Paraguay tri-border area. We surveyed nine farms in an area and nasally swabbed a total of 62 animals. DNA was extracted and specific PCR was performed to identify chlamydial species. We detected Chlamydia spp . in 6.5% (4/62) of the animals tested, positive samples belonged to cattle and none of them showed symptoms of respiratory disease nor had been diagnose with reproductive diseases. Specific nested PCR confirmed two samples belonged to C. pecorum and two to C. psittaci . We report for the first time Chlamydia circulation with zoonotic risk in the region. Surveys in birds and wild mammals could give a better understanding to know what Chlamydial species are circulating in the wild interface. The zoonotic potential should be taking into account as farm workers and the surrounding population could be silent carriers or have respiratory diseases being underdiagnosed, and therefore should be considered in the differential diagnoses.
Smartphone Addiction and its Associated Factors Among Freshmen Medical Students in China: A Cross-Sectional Study
Authors: Liu Huan , Zhang Ming , Zhou Zhiqing , Zhu Ergang Zhu , Huang Long 
Published: December 2021

Abstract
Background With the widespread smartphone use worldwide, this has brought about addiction and smartphone addiction recognized as an emerging epidemic. The study aims to investigate the prevalence of smartphone addiction among freshmen medical students and explore its association with personal factors, mental health and professional identity.Methods This cross-sectional study was conducted in October 10th to November 10th, 2020 and included 2182 first-year college students at Wannan Medical College, China.The Smartphone Addiction Test, Professional identity, 12-item General Health Questionnaire were used in this cross-sectional survey. Pearson correlation analysis was performed to examine the correlations between smartphone addiction with mental health and professional identity. Binary logistic regression analysis was conducted to assess examine the factors influencing smartphone addiction.Results Of the 2182 students, 866(39.7%) were identified as smartphone addiction. The logistic regression analysis shows that four factors (professional identity scale,poor mental health,whether or not smartphone use before sleep, and perceived study pressure) had a significantly associated with smartphone addiction.Conclusions This cross-sectional study showed that smartphone addiction was common among the chinese freshmen medical students. smartphone addiction was common among the freshmen medical students surveyed.The findings suggest that the promotion program aimed at enhancing the mental health and professional identity among freshmen medical students are beneficial to reducing their smartphone addiction.
Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration
Authors: Wei Wei , Haishan Xu , Rak Marko , Hansen Christian 
Published: December 2021

Abstract
Background and Objective: Ultrasound (US) devices are often used in percutanous interventions. Due to their low image quality, the US image slices are aligned with pre-operative Computed Tomography/Magnetic Resonance Imaging (CT/MRI) images to enable better visibilities of anatomies during the intervention. This work aims at improving the deep learning one shot registration by using less loops through deep learning networks.Methods: We propose two cascade networks which aim at improving registration accuracy by less loops. The InitNet-Regression-LoopNet (IRL) network applies the plane regression method to detect the orientation of the predicted plane derived from the previous loop, then corrects input CT/MRI volume orientation and improves the prediction iteratively. The InitNet-LoopNet-MultiChannel (ILM) comprises two cascade networks, where an InitNet is trained with low resolution images toperform coarse registration. Then, a LoopNet wraps the high resolution images and result of the previous loop into a three channel input and trained to improve prediction accuracy in every loop. Results: We benchmark the two cascade networks on 1035 clinical images from 52 patients , yielding an improved registration accuracy with LoopNet. The IRL achieved an average angle error of 13.3° and an average distance error of 4.5 millimieter. It out-performs the ILM network with angle error 17.4° and distance error 4.9 millimeter and the InitNet with angle error 18.6° and distance error 4.9 millimeter. Our results show the efficiency of the proposed registration networks, which have the potential to improve the robustness and accuracy of intraoperative patient registration.
Genome Mining Revealed Polyhydroxybutyrate Biosynthesis by Ramlibacter Agri Sp. Nov., Isolated from Agriculture Soil in Korea
Authors: Dahal Ram Hari , Kim Jungmin , Chaudhary Dhiraj Kumar , Kim Dong-Uk , Jang Hyein , Kim Jaisoo 
Published: December 2021

Abstract
A white-colony-forming, aerobic, motile and Gram-stain-negative bacterium, designated G-1-2-2T was isolated from soil of agriculture field near Kyonggi University, Republic of Korea. Strain G-1-2-2T synthesize the polyhydroxybutyrate and could grow at 10–35°C. The phylogenetic analysis of its 16S rRNA gene sequence, strain G-1-2-2T formed a lineage within the family Comamonadaceae and clustered as a member of the genus Ramlibacter. The 16S rRNA gene sequence of strain G-1-2-2T showed high sequence similarities with Ramlibacter ginsenosidimutans BXN5-27T (97.9%), Ramlibacter monticola G-3-2T (97.9%) and Ramlibacter alkalitolerans CJ661T (97.4%). The sole respiratory quinone was ubiquinone-8 (Q-8). The major polar lipids were phosphatidylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, and an unidentified phospholipid. The principal cellular fatty acids were C16:0, cyclo-C17:0, summed feature 3 (C16:1ω7c and/or C16:1ω6c) and summed feature 8 (C18:1ω7c and/or C18:1ω6c). The genome of strain G-1-2-2T was 7,200,642 bp long with 13 contigs, 6,647 protein-coding genes, and DNA G+C content of 68.9%. The average nucleotide identity and in silico DNA–DNA hybridization values between strain G-1-2-2T and closest members were ≤81.2 and 24.1%, respectively. The genome of strain G-1-2-2T showed eight putative biosynthetic gene clusters responsible for various secondary metabolites. Genome mining revealed the presence of atoB, atoB2, phaS, phbB, phbC, bhbD genes in the genome which are responsible for polyhydroxybutyrate biosynthesis. Based on these data, strain G-1-2-2T represents a novel species in the genus Ramlibacter, for which the name Ramlibacter agri sp. nov. is proposed. The type strain is G-1-2-2T (= KACC 21616T = NBRC 114389T).