bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multidrug Resistance Pattern and Plasmid Profiling of ESBL and Carbapenemase Producing E. coli Isolates of Bovine Origin in Awadh Region of Uttar Pradesh
Authors: Yadav V. , Joshi R.K. , Joshi Namita , Singh S.V. , Srivastva D.P. 
Published: April 2024

Background: Nowadays, the emergence and dissemination of Extended-spectrum β-lactamase (ESBL) and Carbapenemase producing Enterobacteriaceae (CPE) constitutes a major public health concern. The extensive and inappropriate use of antibiotics may lead to development of multidrug resistant (MDR) organisms. The antibiotic resistant genes are mostly situated conjugative plasmids, which are horizontally transmitted from one species to another in highly populous country like India. Methods: Total 240 samples were collected from Ayodhya and Sultanpur districts of Awadh region of Uttar Pradesh (India). Confirmation of isolates was done by PCR analysis using species specific Uid A gene. ESBL and Carbapenemase producing isolates were confirmed using DDST, ESBL-E-strip and MBL E-strip test. Plasmid profile of these isolates was performed using GeneJet Plasmid Miniprep Kit and gel-electrophoresis. Result: In this study total 186 (77.5%) isolates of E. coli were confirmed by PCR analysis. Total 130(54.16%) isolates were confirmed as ESBL and 10(4.16%) as Carbapenemase producer using E-ESBL strip and MBL-E strip test. All ESBL and Carbapenemase positive isolates were found 99-100% sensitive against chloramphenicol, polypeptides and aminoglycosides group of antibiotics and 80-100% resistance against 3rd, 4th group of cephalosporins, monobactam and penicillin group of antibiotics. Total 76.18% ESBL positive and 100% Carbapenemase positive isolates were found MDR. Plasmid profiling revealed total 63.84% ESBL positive isolates carried plasmid of molecular weight in range of 1.5 to greater than 10kb with 1-3 number of bands and 80.0% Carbapenemase positive isolates carried plasmid with molecular weight 3 to greater than 10 kb with 1-3 number of bands.
Comparison of the BI-RADS and Kaiser scales for assessing the malignancy of mammary gland formations
Authors: Surovtsev E. N. , Kapishnikov A. V. , Pyshkina Yu. S. , Galochkina M. V. 
Published: April 2024

Diagnosis of breast cancer remains a largely unsolved problem, despite the introduction of screening programs. Recently, magnetic resonance imaging has become more often used not only as a clarifying method, but also as a primary method for diagnosing breast cancer. Unified assessment systems have been developed as tools to assist in decision-making based on the results of radiation examination of the mammary glands: BI-RADS and Kaiser. The BI-RADS score provides a systematic approach to the description of breast images of various modalities. The Kaiser system, on the contrary, is highly specialized and is suitable for interpreting magnetic resonance imaging data only.Purpose. To determine the agreement of experts in assessing breast condition using the BI-RADS and Kaiser scales.Object and methods. We examined 40 patients (average age 45 years), we analyzed 87 neoplasms and areas of changes. The studies were performed on a magnetic resonance imaging scanner with a magnetic field of 1.5 T with appropriate scanning protocols, the results were assessed by independent experts with and without experience. Spearman's correlation analysis and Kendall's coefficient of concordance were used to analyze the results.Results. Spearman's correlation showed a relationship between BI-RADS scores and Kaiser in the inexperienced rater. The agreement between experts was higher when using the Kaiser system - 0.66, using the BI-RADS scale – 0.53.Conclusion. The lack of correlation between BI-RADS and Kaiser scores in an experienced examiner suggests a significant contribution of specific magnetic resonance features. The agreement between experts using the Kaiser system is higher than when using the BI-RADS scale.
Magnetic resonance imaging technique to quantify biomarkers for chronic liver diseases
Authors: Savchenkov Yu. N. , Trufanov G. E. , Fokin V. A. , Efimtsev A. Yu. , Arakelov S. E. , Titova I. Yu. , Meltonyan A. R. 
Published: April 2024

Background. Recently, multiparametric magnetic resonance imaging (MRI) techniques have been developed to stratify clinically significant changes in chronic liver diseases (CLD). The advantage of multiparametric MRI is the visualization of the entire organ to eliminate the variability of the results during biopsy and the assessment of the quantitative characteristics of individual parameters of the liver parenchyma. A relatively new direction is the use of multiparametric MRI for the diagnosis of CLD with quantitative determination of fat, iron and fibrous changes in the liver parenchyma.Aim. To develop a multiparametric MRI technique for the quantitative assessment of biomarkers in CLD.Object and methods. A multiparametric MR study was performed in 57 patients with CLD using various pulse sequences.Conclusion. The article reflects the developed multiparametric MRI technique for quantifying biomarkers in CLD, based on data of which it is possible to abandon invasive interventions in the process of diagnosis and monitoring the response to treatment.
Developing interdisciplinary habits of hand, heart and mind for career development practitioners
Authors: Frigerio Gill 
Published: April 2024

Within an edition of this journal that is looking at the contribution of differing disciplines to the study and practice career development, this article explores the notion of career as explicitly interdisciplinary, and examines different understandings and implications of that. From here, it uses Schulman’s ideas of surface, deep and implicit signature pedagogies to consider the implications of our interdisciplinarity for the ‘signature pedagogies’ involved in the training of career development practitioners. It ends with scoping out some priorities for further consideration and arguing for NICEC’s role in articulating the habits of hand, heart and mind involved.
Using a New Gradient-Based Ultra-Deep Resistivity Inversion Model to Map Oil-Water Contacts in the Gekko Fields
Authors: Riofrío K. , Clegg N. , Chatterjee A. , Bang A. , Fjeld P. H. 
Published: April 2024

Abstract
This paper evaluates a new ultra-deep azimuthal resistivity (UDAR) inversion model in the context of transition zones above fluid contacts, these are common in gradational resistivity settings and typically cause difficulty with previous inversion models. The new inversion model is applied to oil-water contact mapping from within a thin oil column, and the results are verified against conventional methods for oil-water contact determination.
A new gradient-based enhanced focus UDAR inversion model allows for model layers with continuously sloped resistivity profiles. Previously, approximation using multiple layers with discrete resistivity steps was required to model formations without sharply contrasting boundaries, as in transition zones above fluid contacts. We compare the performance of previous and new inversion models using real data from an extensively drilled sandstone reservoir with stepped or tilted gas/oil and oil/water contacts. The new UDAR inversion model is applied to map the oil-water contact across the entire structure, which is then compared with proven depths in exploration and geopilot wellbores.
The new inversion model improves UDAR imaging of the Gekko reservoir of the Alvheim area in Norway, allowing for geosteering of nine horizontal wellbores within a thin oil column to maximize oil and gas recovery. The reservoir is comprised of stacked turbiditic sandstones ranging in configuration from thick and homogenous to complexly layered sands between shale sequences. The application of an enhanced focus inversion is shown to better define both subsurface geology and oil-water contacts than the previous inversion model. This allows more confident differentiation of the oil-water contact from shale beds. The oil/water contact derived from UDAR imaging is found to closely match results from formation pressure gradient analysis and contact penetrations in nearby wellbores. Confident mapping of the oil-water contact reveals it has an uneven tilt across the structure.
This paper demonstrates how a new inversion model improves UDAR imaging in a gradational resistivity setting. It presents data that practicing engineers can use to evaluate its performance and limitations and demonstrates the capability to confidently map unevenly tilted oil-water contacts in homogenous oil-bearing reservoirs from within the transition zone.
Design Configuration for Autonomous Inflow Control Valve Technology for Newly Drilled Well in a Mature Field in Malaysia – Model vs Reality
Authors: Bandara Yasas W , Ismail Ismarullizam Mohd , Ng Natasya , Siew Caleb 
Published: April 2024

Abstract
Design of an ICD (Inflow Control Device) completion is initially determined based on the static and dynamic models prepared during pre-drilling stage of the well. However, actual well properties available post-drilled are quite different from the pre-drilled properties, and the completion must be optimized. The objective of this study is to address the challenges on pre- and post-completion designs which was conducted for a gas and water control application in redevelopment phase infill well in a mature field in Malaysia. All types of ICD completions are designed based on the static simulation for selected time steps of a dynamic simulation. The autonomous inflow control valve (AICV) is an advanced ICD that can also choke/close unwanted gas and water. The number of AICVs are determined based on the initial maximum oil production rate, despite permeability and gas/water saturation. Therefore, several sensitivities were conducted to determine optimum production rate with an AICV completion. The compartmentalization and distribution of AICVs mainly depended on the well constraints. Although, the required number of AICV are same for the post drill completion, packers and AICV joint placement were adjusted based on the drilling log data. Drilling logs indicated that permeabilities of the post-drilling were about 1/10 of the expected permeabilities of the model and high permeability zones were concentrated to localized zones compared to distributed high permeability in the model. Localized high permeable zones create challenging situations to place the required number of AICV joints in respective compartments due to length constraints. The completion design was re-optimized based on the new permeability profile and new constraints. Packers were placed in low permeability zones to maximize the effect of zonal isolation. Additional packers were installed to delay the gas transport to oil producing compartments in case of a gas breakthrough in neighboring compartment. The number of AICV joints utilized in the well was lower compared to the originally predicted number of joints due to shorter TD of the well. For the well, the number of AICV joints utilized was 38 out of 45 joints. The well started production with desired oil rate at 1500 BOPD with minimal gas and water production with productivity index (PI) 2x better than expected.
A New Approach to Cost Effective Multilateral Solutions for Mature Fields in the North Sea
Authors: Kelsey Matthew , Barron Angus , Dyrseth Vegard , Larsen Lars Petter , Williamson Michael , Saetre Geirmund 
Published: April 2024

Abstract
Multilateral well design has established itself as being a reliable and well proven means of extending well life and increasing overall reservoir recovery factor. Modern multilateral junctions constructed within the Norwegian North Sea generally tend to be technologically complex, requiring hydraulic and mechanical integrity to allow for monitoring and flow control of up to four independent branches. As the technology has matured and the requirement to reduce well construction costs becomes apparent, novel methods of multilateral junction construction are being considered. One such method, involving creation of an open hole lowside exit in the reservoir, was recognized as a low risk solution in terms of complexity and cost.
This design removes the requirement for a cased hole window exit, however the potential compaction effects common on the field necessitate some means of mechanical support at the junction location to maintain lateral flow. This led to a collaboration between the operator, multilateral technology (MLT) vendor and advanced composite tubular developer resulting in a simple reservoir deployment system. This system allowed placement of a composite tubular within the primary mainbore, thereby enabling a means of lateral flow access and depth referencing for lateral creation. This system was deployed twice within the field with no non-productive time (NPT) associated with the operation, leading to successful drilling and completion of two dual lateral wells.
This paper discusses design considerations around the novel ML construction method including system requirements, configurations and a review of the operations performed.
Comprehensive Investigation of Near-Wellbore Damage in Produced Water Reinjection: Bridging Microfluidic Insights and Core Flooding Test Analyses
Authors: Saikia Tinku , Kurbasov Maksim , Øye Gisle , Feilberg Karen Louise 
Published: April 2024

Abstract
The disposal of produced water poses a significant challenge for the oil and gas industry due to its voluminous nature and the presence of diverse contaminants, rendering treatment and disposal processes costly and intricate. Adhering to stringent environmental regulations, reinjection of produced water into reservoirs emerges as an optimal solution, not only facilitating hazardous water disposal but also aligning with pressure maintenance and oil production enhancement strategies, such as water flooding. In the Danish North Sea offshore well scenario, as delineated in the 2022 report by the Danish Energy Agency, a substantial volume of produced water is generated alongside limited oil production. The magnitude is illustrated by the production of approximately 3.7 billion m3 of oil accompanied by 30.8 billion m3 of produced water. Nevertheless, challenges persist with produced water reinjection, particularly concerning near-wellbore damage during the reinjection process. To overcome this challenge, a comprehensive understanding of formation damage mechanisms and identification of primary contributors become imperative. Conventional methods, relying on core flooding experiments involving pressure readings during injection and analytical analysis of produced water, offer insights into potential damage mechanisms. However, microfluidic experiments, offering visual analyses of damage mechanisms and pore plugging materials, provide a more nuanced perspective. This research seamlessly integrates both core flooding and microfluidic experiments utilizing Danish North Sea Field-produced water and seawater. The core flooding results unequivocally identify inorganic sediments, notably Fe3Si, as the primary cause of pore clogging. In contrast, microfluidic experiments shed light on the substantial contribution of bacterial action, specifically biofilm formation, to the intricate pore plugging mechanisms. This multi-faceted approach enhances the precision of understanding and mitigating near-wellbore damage during produced water reinjection processes.
Exploration Robot Chat: Uncovering Decades of Exploration Knowledge and Data with Conversational Large Language Models
Authors: Mosser L. , Aursand P. , Brakstad K. S. , Lehre C. , Myhre-Bakkevig J. 
Published: April 2024

Abstract
Hydrocarbon exploration and carbon capture and storage (CCS) evaluation are inherently multi-disciplinary tasks that require the integration of knowledge from multiple datatypes set in a historical and geological context. The diverse nature of subsurface data is often represented by a combination of direct and indirect measurements, interpretations and observations documented in multi-dimensional datasets as images, and written reports. The fidelity of these images and reports can have an enormous variety, and different qualities leading to a challenging situation where explorationists need to determine the value of a source of information while combining these sources across large spatiotemporal contexts. Modern search engines today can not only search through document text but also images. These capabilities have improved our ability to find well-known concepts based on short phrases, or keywords, combined with significant meta-data. While these types of search engines have certainly benefited practitioners, the challenge of combining information from multiple data-sources, data modalities and languages remains an open problem.
With the advent of conversational large language model (LLM) systems such as ChatGPT (Achiam et al. 2023) that provide coherent textual information and are informed by their training data, have become a reality. While ChatGPT certainly has taken many industries and their disciplines by storm, the tool is not without its shortcomings. For industry applications, in many cases the information necessary to provide answers will be highly proprietary, not shared with third parties and not part of the training data of the popular LLMs. Furthermore, due to their probabilistic nature LLMs suffer from so-called hallucinations, where the model provides a confident answer based on the user provided input but is non-factual and often non-sensical.
To answer a given user-query with factuality it is important to provide relevant information as context to the LLMs. Lewis et al. (2020) proposes combining two systems: An information retrieval system that provide relevant information to answer a given question or to solve a specific task, and a second system being an LLM that is supplemented with the retrieved information as context to answer the user's question. This pattern of so-called retrieval-augmented generation (RAG) has become highly popular in the last year due to the strong conversational capabilities of systems like ChatGPT, accessible developer APIs for interfacing with LLMs, open-source software to orchestrate RAG-systems, as well as the rapid development of open-source LLMs (Touvron et al. 2023). Moreover, since the RAG pattern does not require fine-tuning or re-training a language model, it remains one of the most accessible ways to tailor LLMs to proprietary knowledge bases.
Data-Driven Predictions of CO2 EOR Numerical Studies Using Machine Learning in an Open-Source Framework
Authors: Lliguizaca J. R. , Landa-Marbán D. , Gasda S. E. , Sandve T. H. , Alcorn Z. P. 
Published: April 2024

Abstract
An open-source framework is presented for the development and evaluation of machine learning (ML) assisted Data-Driven models of carbon dioxide (CO2) enhanced oil recovery (EOR) processes to predict oil production and CO2 retention. This framework generated inputs and outputs for two cases of CO2 water alternating gas (WAG) injection using Python packages and a reservoir simulator, which were subsequently used to train and test the supervised learning algorithms. The main objective was to increase the speed, robustness, and accuracy of predicting oil recovery and CO2 retention using a complete open-source approach combining Python programming, reservoir simulation, and ML techniques. This framework incorporated the reservoir model of the SPE5 benchmark study. The geometry was built using the pyopmnearwell Python package, and the simulations were run in the open-source Open Porous Media (OPM) Flow simulator. The permeability and porosity of the top layer and the gas injection rate were selected as variable input parameters to generate different settings for which the cumulative oil recovery and CO2 retention were determined using the simulator. These inputs and outputs formed the training and test dataset created for the ML model. Finally, the algorithms were optimized through hyperparameter tuning to enhance the predictive scoring metrics R-squared and Root Mean Square Error (RMSE).
Two cases were created with one injector and one producer to develop and evaluate the predictive models. Case 1 was restricted to keeping the WAG injection of 20 years with injection slugs of 3 months, and Case 2 aimed to maintain the same injected volumes for each simulation job. The reservoir simulator allowed us to produce representative results, which were then used to generate a data set for training, testing, and validating the ML algorithms. Based on the predictive scoring metrics, Gradient Boosting and Random Forest regression algorithms performed best, followed by the Decision Tree regressor. In contrast, the K-nearest neighbors regressor had a bad performance. Furthermore, two approaches for hyperparameter tuning were used to get the algorithms’ hyperparameter values, improving their RMSE or R-squared. The optimal model architecture (i.e., the best number of estimators or neighbors) was determined by the hyperparameter tuning to increase the accuracy or reduce the error of the predictions. The predictions of the data-driven models generated by DT, RF, and GBR were reliable based on the accuracy metrics of the trained and tested models, exhibiting values for R-squared above 0.93 and RMSE below 0.05. This innovative and robust approach presents a powerful tool for predicting and assessing the sensitivity of parameters of CO2 EOR projects, delivering accuracy and speed compared to existing methods.
Driving Innovation in Caesar Tonga by Managing Big Data and New Interpretations with Modern Digital Workflows, Elevating the Engineering to Enhance Asset Potential
Authors: Paclibon E. Nuñez , Glassco E. , Demichelis J. , Windham L. , Nikolaeva A. , Ogunlana D. Ajose , Reza Z. , Link E. , Shan D. , Wang Y. , Ali S. , Jageregger W. 
Published: April 2024

Abstract
A co-owner in the Caesar Tonga (CT) field with 13 years of production sought to replace the outdated existing reservoir simulation model with a model that could show 1) the impact of a newly installed subsea expansion, 2) future operations such as acid stimulations, and 3) the effect of offtake from a nearby field sharing the same reservoir. The model conversion objective was to optimize reservoir management strategies, enhance reservoir understanding, and streamline workflows.
A significant result of the project is the ability to compare three surface network coupling approaches from different tools. This is the first example of a single asset and single project where such a comparison has been undertaken. Future well-work activity was implemented to find the optimum balance between efficient rates and maximum recovery of this mature asset. For example, the operator's acid stimulation program/operating procedure is triggered by a minimum productivity index (PI) value and the observed individual well performance. This multiphase and crossdisciplinary approach enables assessing each workflow to identify similarities and differences in the tools and implement agile learning to each stage in the modelling.
The results of this project are implemented immediately towards the asset's long-term forecasting. The integrated platform couples the operator's subsea production system with the reservoir dynamics to optimize production efficiency. Directly integrating the subsurface with the wellbore and network through a common platform enhances communications between the co-owners. Overall, this model conversion gave us the ability to address business needs while promoting a better understanding of the asset performance.