bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation Study for Field Implementation of WAG as Initial Drainage Strategy
Authors: Larsen Alex , Førland Tormod , Øian Erlend , Olav Jarlsby Diderik
Published: October 2020

Water Alternating Gas (WAG) injection is normally implemented after water injection to increase microscopic and macroscopic sweep efficiency. The microscopic sweep is enhanced by trapping of gas, three-phase flow effects including lower residual oil saturation, and possible component transfer between gas and oil. The macroscopic WAG-effect is mostly associated with recovery of attic oil or other pockets of oil not yet swept by water.

This study presents the results from simulation of a WAG process for an offshore turbidite reservoir with undersaturated oil. The aim of the work is to qualify the increased drainage potential from WAG injection by understanding the reservoir response, which includes cycling of saturations in two- and three-phase flow as well as changes in fluid properties due to interaction between gas and oil.

A hierarchical workflow has been followed in order to conduct a systematic and comprehensive analysis of the WAG process. This includes the use of simulation models from full-field scale models (FFM), via box-models, to simpler 1D models. The full-field model, with appropriate well constraints reflecting top-side requirements, is the basis for estimating an extra WAG potential of about 3% of additional OIIP recovered compared to water injection. WAG injection is further modelled on the field scale without any topside constraints to get the free reservoir response of WAG, and to investigate the impact of relative permeability and fluid properties on production curves as well as comparison to water injection. Furthermore, grid effects, two- and three-phase flow properties and the effect of oil swelling is studied in smaller box models and 1D models.

The results from the workflow qualify the enhanced oil recovery from WAG compared to a water injection scheme. Approximately 1/2 of the increase in recovery factor is attributed to gas-oil relative permeability, about 1/5 is attributed to a change in fluid properties from oil swelling, and 1/3 is due to increased well-lift.
A Drilling Company's Perspective on Non-Productive Time NPT Due to Well Stability Issues
Authors: Krygier Natalia , Solarin Adebowale , Orozova-Bekkevold Ivanka
Published: October 2020

Non-Productive Time (NPT) is a key measure of cost-effective and successful drilling operations. NPT may be caused by various reasons, such as unpredictable weather or technical issues. Some of those cannot be reduced or controlled (weather), while others can be counteracted by proper preparation and careful planning. Industry experience shows that time lost due to well stability issues accounts, on average, for 10% of all NPT. This can be eventually reduced by appropriate measures and/or procedures.

Offshore drilling operators are collecting and analyzing large amounts of data but it is very rare to perform analysis on unstructured drilling reports like the ones produced and possessed by drilling contractors. This project's aim was to analyze the NPT of drilling operations from the perspective of a contractor. Data from 93 wells drilled by Maersk Drilling from 2006 to 2019 in the North Sea was used to classify, compare and analyze the causes of NPT, as registered in the daily reports, with a focus on well stability issues by the means of text mining and statistical tools. The analysis had shown that the average cost of wellbore stability NPT was in the order of 2,000,000 USD per well and losses were the most common cause of wellbore instability among the wells in the database.

This project was, to the best of our knowledge, the first attempt to extract and analyze information from unstructured daily operation reports collected by a drilling company. The analysis had shown that the contractor's reports very often do not contain the same degree of detail on the causes of NPT in general, and wellbore stability issues in particular, as the ones produced by the operator. The results of our analysis can be used as a framework for improvement of current practices for registration and categorization of NPT causes in the drilling contractor's reports. In future, these reports should be structured in a way that allows to record both the symptoms of wellbore stability issues as well as the issues themselves. A well-structured report will not only allow to analyze easier historical data but also may be used to facilitate the automatization of the drilling process. Based on experiences from previous wells, specific NPT symptoms and scenarios could be identified and compared with current situations therefore allowing to promptly forecast and counteract possible issues.

The ultimate goal of such structured reports is to create an efficient database and provide guidelines to the drilling crew on how to recognize and minimize NPT related to wellbore stability problem. Suc h database can help make drilling more predictable by providing field-specific wellbore related problem data, which can be used to ensure the planning of effective mitigating measures. The method used for this analysis can also be extended to other problematic areas.
Hybrid Approach for Drilling Automation
Authors: Aghito Manuel , Bjørkevoll Knut Steinar
Published: October 2020

Objectives/Scope

The objective of this work is to present a first step towards a hybrid approach between machine learning (ML) and physics-based modelling to provide decision support for drilling problems. The motivation for developing a hybrid approach is to obtain methods that are more reliable and easier to automate than physics-based models, while still have enough accuracy and predictivity. In this first step, we replicate the performance for predicting downhole pressure in a well of a high-fidelity simulator based upon physical principles by using ML methods. In addition, we also suggest a future roadmap.

Methods, Procedures, Process

A high-fidelity physics-based model for drilling and well control operations is used to generate vast amounts of data for two cases, drilling with not major events, and drilling into an over-pressured reservoir. Key simulation input parameters and assumptions are varied to create realistic scenarios. We replicate the high-fidelity simulator downhole pressure predictions by two supervised machine learning algorithms. Random forest (RF) and recurrent neural network (RNN). The hybrid approach is flexible and is also employed for kick detection and estimation of the mass of the influx. After using unaltered data from the high-fidelity simulator, we also demonstrate the ML methods on corrupted data with synthetic noise.

Results, Observations, Conclusions

RF and RNN obtained very high accuracy, predicting bottom hole pressure with small error margin. Good results were also obtained for the kick estimation and kick detection cases. Tested on corrupted data, RF trained with noise performed significantly better compared to RF trained without noise, at the cost of a slight reduction in accuracy in the error free scenario. Initials tests on real data are ongoing and further work is needed. Hybrid methods have the potential of performing well with noisy environments and are valuable tool to be used in drilling automation.

Novel/Additive Information

Combining highly advanced dynamic models for drilling and well control with modern ML methods has not been done earlier to the best knowledge of the authors. Demonstrating this on real data will be valuable because data-driven and physics-based approaches used separately are considered inadequate for future automated drilling concepts.
Implementation of Ensemble-Based Reservoir Modelling on the Ærfugl Field
Authors: Tveteraas Ørjan , Vey Geraldine , Hjellbakk Andor , Wojnar Konrad
Published: October 2020

An ensemble-based modelling method has been implemented and utilized in reservoir management of the Ærfugl field. Ærfugl is a 60-km long, 3-km wide gas condensate accumulation in a Cretaceous deep marine deposited reservoir with high net-to-gross. The field has a 6-year long production history from one test producer. A key objective of the modelling effort was to take an uncertainty-centric approach that could integrate all available data. The methodology of this approach is presented, and its benefits and challenges are discussed. The quality of the automated history matching is shown, and the resulting uncertainty ranges are discussed. Practical use of the ensemble for forecasting and decision making is also presented.

A set of 100 reservoir models was created with Monte Carlo sampling. All uncertain parameters that were deemed important were varied, such as geological input surfaces, geostatistical parameters, petrophysical parameters, relative permeability curves, etc. All models were then conditioned to dynamic pressure and production data using an ensemble Kalman based method. The updates made by data conditioning apply to all uncertain parameters as defined in the generation of the initial ensemble, including facies and structural modelling.

The data conditioning arguably produced an adequate history match for the entire model ensemble. The posterior uncertainty range (after integrating dynamic data) remained high for most variables, but uncertainty related to in-place volumes was shown to be highly constrained by the dynamic data. Production forecasts were generated, and probabilistic production profiles were extracted. Four deterministic cases that were representative of the range of important parameters were extracted from the ensemble for use in certain applications where probabilistic modelling is not appropriate, or time constraints impose limitations. The model ensemble and associated production profiles were used as input to project economics, for a value-of-information study, and for well planning.

In addition to going though the work process and results, this paper aims to shed light on the method's practical effectiveness, the potential impact on the way we do reservoir management, and to highlight some specific issues that arise in practical application. The overall set-up is effective at eliminating waste by enabling rapid model updates as new data comes in and making use of existing data that would otherwise be under-utilized.
Autoviscosity - A Vital Step Towards Automation of the Drilling Fluid Process on Drilling Rigs
Authors: Bjørkevoll Knut Steinar , Aghito Manuel , Lund Bjørnar , Blom-Jensen Bjarne , Torpe Geir , Brevik Jan Ove , Fjogstad Arild
Published: October 2020

Objectives/Scope

The paper describes what steps have been taken towards improved monitoring and control of the offshore drilling fluid process. These steps include the following:

Methods, Procedures, Process

The predictive viscosity model tracks the active fluids, calculates additives concentrations in the flow loop, analyses sensor data, calibrates viscosity response to additives, and estimates what is needed to obtain the set target rheology. Laboratory and full-scale tests both onshore and offshore have been performed using the automated rheology and density sensor. To prepare for automating the drilling fluid conditioning process, the predictive rheology model was integrated with the drilling fluid control system and sensor data. Additionally, integration with database and cloud service was implemented to facilitate transfer of data from the rig to the onshore.

Results, Observations, Conclusions

During laboratory scale experiments, the predictive rheology model represented topology flexibly and was configured to represent the active flow loops in laboratory scale experiments, full scale onshore test, and offshore test with two sensors in the loop. The data processing algorithm with data quality assessment and model calibration was tested with onshore full-scale data. Many challenges were addressed and solved by cooperating partners in the integrated fluid laboratory, which included two sensors, the predictive rheology model, drilling fluid control system and a flow loop with multiple active tanks and mixing lines with dosing units for adding liquids and powders. The first response tests for the calculated additive rate set-point were successfully performed. Full-scale onshore tests were completed while adding different additives and recording the rheology data. The calibrated predictive model showed good correlation with the recorded data. Temperature effects were also considered and modelled accurately. Two sensors were installed offshore, recording data during regular drilling operations.

Novel/Additive Information

New elements required to automate the drilling fluid treatment process have been implemented and tested, including a predictive rheology model and data integration between different vendor's software/control systems and equipment.
Digital Twins for Well Planning and Bit Dull Grade Prediction
Authors: Gharib Shirangi Mehrdad , Furlong Edward , Sims Kelly Scott
Published: October 2020

In this work, we develop and apply an offset well data analysis framework to generate a digital twin that is representative of bit state. We also strive to produce performance maps for well planning. Our workflow involves three major elements: 1) offset well data analysis to generate detailed depth-based and time-based statistics 2) computation of wear on bit and efficient weight-on-bit (WOB) versus depth for all the runs 3) automated machine learning to generate an accurate predictive model for bit dull grade to deploy for real-time operations. We define and calculate the efficient WOB as the minimum WOB needed to fully engage all cutters on the bit and maximize depth of cut. The efficient WOB changes with both bit state and formation strength, so an indication of bit wear can be used to predict formation strength. Deployment of the bit dull grade predictor enables the drilling crew to monitor the bit state while drilling and decide whether a reduction in ROP is due to a worn bit or other factors. Application to field cases from US Land yielded promising results. After computing the bit wear, performance maps for different bit states (e.g., sharp vs worn bits) are created. These performance maps help identify the optimal drilling parameters in well planning.
Non-Newtonian Fluid Flow Measurement in Open Venturi Channel Using Shallow Neural Network Time Series and Non-Contact Level Measurement Radar Sensors
Authors: Noori N. S. , Waag T. I. , Viumdal H. , Sharma R. , Jondahl M. H. , Jinasena A.
Published: October 2020

Drilling is a costly operation, especially offshore, and disturbances caused by drilling fluid influxes (kicks or loss) pose persistent challenges and operational costs during drilling process. To minimize non-productive time induced by kick/loss of circulation, drilling systems incorporate methods monitor and measure inflow and outflow of drilling fluid for early detection of kicks/losses. Coriolis flowmeters are proven to be very accurate even under difficult mud properties. However, installation and maintenance and cost of these instruments are complex and high. Thus, new exploits of technological advances such as high-performance computations (HPC), artificial intelligence (AI), low-cost sensor technologies could enable real-time data acquisition and analysis for early kick/loss detection to improve the safety and performance of oil and gas drilling process. In this study, we focus on the outflow measurement using a combination of sensors (i.e. radar and ultrasonic sensors) to remotely obtain the flowrate in an open channel with Venturi constriction.
Optimizing Well Economic Limits Using Ensemble-Based Optimization on Olympus Field
Authors: Chang Yuqing , Nffivdal Geir , Lorentzen Rolf Johan
Published: October 2020

The Olympus field is an optimization case prepared by TNO to test different optimization methods and workflows for field development. The authors have joined the Olympus optimization challenges by solving various optimization problems for field development. Here, we consider the well control problem from a different perspective by using well economic limits as parameters to optimize the objective function - the Net Present Value (NPV).

The Ensemble-based Optimization (EnOpt) method applied to this problem has been shown to be simple and efficient to handle geological uncertainty in many literatures. In this work, the performance of EnOpt is illustrated on the Olympus benchmark case. The economic limit determines when a well will be shutin with respect to the water-cut, which directly affects the calculation of NPV. After optimizing the economic limits, we see that the producers are not necessarily following the fixed value of economic limits to give the best production benefit in terms of NPV. Given a well control scheme in the field, the NPV can be further improved by optimizing the economic limits of each producer.

Optimizing the economic limits is a novel aspect for Olympus case, as this was not within the original scope of the Olympus optimization challenge. This work also shows that the NPV is further improved compared to the values obtained by the authors during the Olympus optimization challenge by utilizing optimized well economic limits.
Development and Practical Application of an Analytical Well Failure Model that Improves Well Safety and Production Efficiency
Authors: Girling Stuart , McIntosh John , Lumbye Peter , Nielsen Hans-Henrik , Patterson Stephen , Lutzen Peter
Published: October 2020

A well failure model is constructed as a matrix that identifies the most common modes of well failure seen by the Well Operator. For specific well types, well anomalies are assessed before they happen, in a what-if risk assessment.

A traditional well failure model (WFM) includes a subset of possible anomalies and is based on subjective judgement. Total E&P Denmark's (TEPDK) new WFM uses quantitative analysis with an objective approach to risk assessment. This gives detail and clarity of actual risk (as a product of probability and severity) from well integrity anomalies not previously available.

This Analytical WFM assesses well integrity barrier compliance and risk. It uses individual component failure mode probabilities and severities and analyses thousands of potential leak paths throughout wells. It includes potential flow from multiple pressure sources and to multiple pressure sinks, providing a comprehensive understanding of all possible failure cases, and their associated impact on risk.

Quantitative Risk Assessment (QRA) was used to calculate maximum Safe Service Life for wells following component failure. Outputs from the analysis were used to build a detailed WFM. Immediate and long-term actions were developed in workshops and suitable response times agreed based on the Safe Service Life calculation.

Engineers now spend 85% less time in well integrity risk assessments and failure management when anomalies arise because this work has been done in advance. This means engineers' time is freed for other valuable tasks. The company also saves management time, onshore and offshore, cutting down the need for management challenge and action justification. All personnel involved have a more consistent understanding of well issues, with routine anomalies dealt with much more efficiently. This means decisions are made at a lower level, including whether to continue to operate a well or shut it in.

TEPDK has reduced the probability of process safety incidents from wells. This is because it has a far more sophisticated, clearer understanding of well risks, and actions and timeframes for anomaly response. It has also improved production efficiency, safeguarding on average 2,700 boepd, through a reduction in well shut-in time. This has an equivalent value of approximately $50 million / year.
Optimal Well Placement Using Machine Learning Methods: Multiple Reservoir Scenarios
Authors: Mousavi Seyed Mahdi , Jabbari Hadi , Darab Mahdi , Nourani Meysam , Sadeghnejad Saeid
Published: October 2020

Determining the distribution of optimal injection and production wells along with their operating conditions is a complex problem. The objective of this study is to compare the effectiveness of an experimental based approach (central composite design, CCD) with a machine learning method (xGBoost) in a well-placement optimization application.

In this study, the well-placement problem consists of the joint optimization of drainage radius, well-operating conditions, and the number of injection and production wells. Our objective function is the net-present-value (NPV) for a specified operational life of the reservoir under study. Both algorithms (CCD and xGBoost) are applied to three different optimization scenarios, i.e., production from (I) homogeneous reservoir, (II) heterogeneous reservoir, and (III) waterflooding into a heterogeneous reservoir. Like all machine-learning algorithms, our methods need a training dataset. The fast predictor module (i.e., trained model) is obtained by running several numerical simulations by a commercial simulator internally called in an own developed Python code. Moreover, R-squared is chosen as the statistical quality measure in this study.

In the first scenario, both algorithms show satisfying predictions (R-squared of 0.943 and 0.999 for CCD and xGBoost, respectively). In scenario II, the CCD and xGBoost show a similar response again (0.948 and 0.997, respectively). As a sub-result, the optimum distance between the two producers was found approximately five simulation blocks in the first case, and three blocks in the second one. The CCD method reveals unreliable results (0.840) in scenario III, while the NPV predictions of the xGBoost algorithm are still acceptable (0.986). Moreover, unlike CCD, xGBoost could find the optimum solution. In other words, the CCD method does not always converge to an optimum solution. However, the number of required simulation runs for CCD is equal to that for the xGBoost model.

In this study, a machine learning approach and an experimental design method were compared together in detail. Their efficiency in predicting the NPV of a well-placement problem through multiple homogeneous and heterogeneous reservoir scenarios differ significantly.
Deep Reinforcement Learning Applied to Managed Pressure Drilling
Authors: ArnØ Mikkel , Godhavn John-Morten , Aamo Ole Morten
Published: October 2020

During drilling operations, maintaining a desired downhole pressure between pressure margins is crucial to avoid damage to the formation and the well. The process is highly nonlinear, changing with depth, and every section in every well is different. Standard solutions with PID controllers are widely accepted for this purpose, although methods such as deep reinforcement learning (DRL) could be investigated as an alternative approach.

A smooth update deep Q learning algorithm is used to train an agent embedded in a managed pressure drilling system. The aim is to control downhole pressure during pipe connections by use of a topside choke valve with nonlinear characteristics. The agent is trained on previously gathered data, from situations featuring step changes in pressure setpoint and changing mud flows, all at various well depths. After training, the agent is tasked with controlling BHP during connection, herein demonstrated by use of a numerically simulated low-order hydraulics model.

Through episodic training, it becomes clear that the agent purely through interaction with the environment, and without any prerequisite knowledge of system dynamics and reward design, converges to an optimal control policy. The trained agent is then tested on pipe connections with well depths in the lower and upper bounds of the training data. The pipe connection scenario presents changes in operating conditions in terms of changing mud flows with changing conditions like increased frictional pressure losses due to increased depth. Still, the results presented show the agent's ability to track a pressure setpoint at various depths in the changing conditions present during connection, while seamlessly incorporating controller constraints.

There are several advantages associated with this approach, among them eliminating the need for development of a complex dynamic model for the process. Also, the approach is applicable to both linear and nonlinear systems, deterministic and stochastic systems, and lower- and higher-level decision-making. These methods could possibly also be applied to other key challenges in drilling such as ROP optimization or autonomous directional drilling.
When Can a Stock-Tank Oil be a Condensate?
Authors: Meisingset Knut Kristian , Cely Alexandra , Bergo Cathrine , Yang Tao
Published: October 2020

Database plots of the distribution of C7+ to C40+ fractions, as well as plus fraction density and molecular weight, show that there is a clear statistical difference - with minor overlap - between stabilized hydrocarbon liquids from reservoir gas (condensate) and reservoir oil. In most cases, the origin of a stock-tank oil sample can be interpreted on basis of composition, density, molecular weight and reservoir pressure.

Typically, hydrocarbon liquid with density higher than 0.85 g/cm3 and C7+ molecular weight > 250 g/mol origins from reservoir oil, while hydrocarbon liquid with density lower than 0.8 g/cm3 and C7+ molecular weight < 170 g/mol origins from reservoir gas.

A main purpose of the present paper is to identify cases where composition, molecular weight and density show that a hydrocarbon liquid sample cannot origin from a reservoir gas at a given reservoir pressure. In addition to statistics from a large PVT database, a PVT simulation procedure is proposed, giving additional proof for such cases.
Accounting for Model Errors of Rock Physics Models in 4D Seismic History Matching Problems: A Perspective of Machine Learning
Authors: Luo Xiaodong , Lorentzen Rolf , Bhakta Tuhin
Published: October 2020

Model errors are ubiquitous in practical history matching problems. A common approach in the literature to accounting for model errors is to treat them as random variables following certain presumed distributions. While such a treatment renders algorithmic convenience, its underpinning assumptions are often invalid. In this work, we adopt an alternative approach, and treat model-error characterization as a functional approximation problem, which can be solved using a generic machine learning method. We then integrate the proposed model- error characterization approach into an ensemble-based history matching framework, and show that, with very minor modifications, existing ensemble-based history matching algorithms can be readily deployed to solve the history matching problem in the presence of model errors.

To demonstrate the efficacy of the integrated history matching framework, we apply it to account for potential model errors of a rock physics model in 4D seismic history matching applied to the full Norne benchmark case. Our experiment results indicate that the proposed model-error characterization approach helps improve the qualities of estimated reservoir models, and leads to more accurate forecasts of production data. This suggests that accounting for model errors from a perspective of machine learning may serve as a novel and viable way to deal with model imperfection in practical history matching problems.
Hierarchical Characterization and Modelling of Deep-Water Slope Channel Reservoirs
Authors: Soni Kishan , Manzocchi Tom , Haughton Peter , Carneiro Marcus
Published: October 2020

Oil reservoirs hosted in deep-water slope channel deposits are a challenge to manage and model. A six-level hierarchical arrangement of depositional elements within slope channel deposits has been widely recognized, and dimensional (width and thickness) and stacking (amalgamation ratio and volume fraction) data have been acquired from published studies to establish parameters for a representative slope channel system. A new static modelling workflow has been developed for building models of channel complexes based on a simplified hierarchical scheme using industry-standard object-based modelling methods and a new plugin applying the compression algorithm. Object-based modelling using the compression algorithm allows for independent input of volume fractions and amalgamation ratios for channel and sheet objects within a hierarchical modelling workflow. A base-case channel complex model is built at the resolution of individual sandstone beds, conditioned to representative dimensional and stacking characteristics of natural systems. Inclusion of explicit channel axis and margin regions within the channels governs bed placement and controls inter-channel connectivity where channels are amalgamated. The distribution of porosity and permeability within these beds mimics grain-size trends of fining in the vertical and lateral directions. The influence of various geological parameters and modelling choices on reservoir performance have been assessed through water-flood flow simulation modelling. Omission of the compression method in the modelling workflow results in a three-fold increase in oil recovery at water-breakthrough, because the resultant unnaturally high amalgamation ratios result in overly-connected flow units at all hierarchical levels. Omission in the modelling of either the bed-scale hierarchical level, or of the axial and marginal constraints on the bed placement in models that do include this level, results in a two-fold increase in oil recovery at water-breakthrough relative to the base-case, because in these cases the channel-channel connections are too permissive.
Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants
Authors: Brandt Christian , Bongcam-Rudloff Erik , Müller Bettina
Published: October 2020

Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.
High-precision digital terahertz phase manipulation within a multichannel field perturbation coding 2DEG meta-chip
Authors: Zeng Hongxin , Liang Huajie , Zhang Yaxin , Yang Ziqiang , Lan Feng , Liang Shixiong , Li Zheng , Wang Lan , Zhang Xilin , Gong Sen , Gong Yubin , Yang Ziqiang
Published: October 2020

Terahertz phase manipulation has always been based on direct coupling of the resonance of quasi-optical terahertz waves with metamaterials, which is accompanied by unnecessary amplitude modulation, thus limiting the accuracy of phase manipulation and its application in monolithic integrated systems. Here, we propose a coding meta-chip composed of transmission lines and two-dimensional electron gas (2DEG) meta-atoms, wherein local perturbation resonances are induced to manipulate the phase of terahertz waves. By controlling the electronic transport characteristics of the 2DEG with external voltages, the intensity of the perturbation can be manipulated, which affects the transmission phase of the waves. More importantly, the perturbation resonances induced by different meta-atoms can be coupled so that through digital coding of the perturbation state of 2DEG meta-atoms, the terahertz wave transmission phase can be manipulated with high precision. As a result, phase manipulation with different precisions from 2° to 5° is observed from 0.26 to 0.27 THz, where the average phase error is only 0.36°, and the maximum root mean square of the transmittance is 0.36 dB. This high-precision phase manipulation via field coding has great application potential in the fields of beamforming, wireless communication, and high-resolution imaging.
Finite Element Modeling and Vibration Analysis of Sprag Clutch-Flexible Rotor System
Authors: huang chuang , zhao yongqiang , jin guanghu
Published: October 2020

To study the overall vibration characteristics of the sprag clutch-flexible rotor system (SC-FRS) under high-speed operating conditions, a finite element model of SC-FRS considering rotor flexibility and bearing support stiffness is established based on the proposed calculation method of the stiffness matrix. According to this model, the natural frequency and mode shape of the system are calculated, and the correctness of the model is verified by comparing it with the calculation results of ANSYS software. Under the action of unbalance, the bending-torsion coupled vibration and the dynamic load of the inter-shaft bearings are analyzed, and it is found that the resonant peak in the torsional direction has the same resonance frequency as that in the bending direction. A test rig for the sprag clutch-rotor system is built, and the axis trajectory and critical speed are tested. The test results show that the finite element model of SC-FRS can accurately describe the vibration characteristics of the system.
Ocorrência de Anadenanthera colubrina (Vell.) Brenan (angico) no semiárido da Paraíba, Nordeste do Brasil
Authors: Anselmo Maria da Glória Vieira , Ferreira Ezequiel da Costa , Carvalho Thamires Kelly Nunes , Nunes Mariana Muniz , Fonseca Andrea Maia Fernandes de Araújo , Lucena Camilla Marques de , Souto Jacob da Silva , Lucena Reinaldo Farias Paiva de
Published: October 2020

A presente pesquisa objetivou registrar e analisar a distribuição e disponibilidade de Anadenanthera colubrina (Vell.) Brenan em oito comunidades rurais no semiárido da Paraíba. Foram realizados levantamentos registrando todos os indivíduos com diâmetro no nível do solo (DNS) igual ou superior a 3 cm, utilizando os métodos de coletas das parcelas, ponto quadrante e caminhamento livre. Nos parâmetros fitossociológicos para o método de parcelas registrou-se DeR de 0,04 a 4,2, FrR de 0,21 a 9,33, e VI de 0,27 a 36,18, com exceção da Comunidade Pau d’Arco (Município de Itaporanga), onde não ocorreu registro de nenhum indivíduo. Para o método de ponto quadrante, o DeR foi de 1,8, FrR de 3,01, e VI de 14,62 na Comunidade Santa Rita (Município do Congo), enquanto na Comunidade Coelho (Município de Remígio) não houve registro. No caminhamento livre foi possível identificar o angico em todas as comunidades estudadas, totalizando 694 indivíduos, e na maioria das comunidades os indivíduos localizam-se distantes dos núcleos domiciliares, estando presente em áreas de mata de Caatinga. Os resultados do presente estudo evidenciam a necessidade da implementação de medidas que avaliem os impactos causados pela utilização desta espécie, tendo em vista o seu baixo registro nas comunidades estudadas, principalmente nas áreas antropizadas, o que pode demonstrar que as populações não apresentam atitudes conservacionistas com o angico.
Uso de energia elétrica e o desafio da sustentabilidade ambiental nas escolas da rede municipal de ensino de Fortaleza, Ceará, Brasil
Authors: Carvalho Maria Laudecy Ferreira de , Oliveira Luan Gomes dos Santos de , Santos Joel Silva dos , Nunes Ana Bárbara de Araújo , Silva Valdísia Vieira dos Santos
Published: October 2020

Com a degradação ambiental ocasionada pelos diversos padrões de consumo e práticas insustentáveis, como o uso crescente de combustíveis fósseis para a produção de energia, a capacidade de carga e suporte do planeta vem sendo comprometida. Nesse sentido, o aumento da pegada ecológica e a crescente poluição e deterioração de diversos sistemas ambientais compromete cada vez mais o equilíbrio ecológico e a sustentabilidade planetária, ocasionando assim, uma série de problemas socioambientais. Diante deste contexto, é urgente a alfabetização ecológica dos futuros cidadãos para (re)pensar e (re)construir um modelo de sociedade que esteja pautada no tripé da sustentabilidade ambiental: social, econômico e ecológico. É nessa perspectiva, que o presente artigo tem como objetivo principal de verificar as ações de sustentabilidade ambiental, no que diz respeito, ao uso da energia elétrica em três escolas públicas municipais, junto aos alunos da 3ª a 9ª séries do Ensino Fundamental, localizadas no Município de Fortaleza, Ceará, Brasil: Escola Municipal Ari de Sá Cavalcante, Escola Municipal Professora Vânia Maria Neves Facó Barros (Unidade II) e Escola Municipal Papa João XXIII. Para a realização da pesquisa, inicialmente foi realizado um levantamento bibliográfico a respeito da temática em questão. A metodologia de base qualitativa utilizada pautou-se em pesquisa documental, pesquisa bibliográfica e aplicação de questionário semiestruturado e entrevista contendo 12 perguntas direcionadas ao público alvo das três escolas investigadas, no que diz respeito aos hábitos e costumes referentes ao consumo de energia elétrica, assim como o acompanhamento mensal da conta de luz e discussões diárias nas salas de aula e na acolhida diária dos alunos no pátio da escola sobre o modelo de gestão e gerenciamento da energia elétrica utilizada pela escola. Os resultados sinalizaram que, os discentes na sua maioria deveriam reavaliar seus hábitos para com o uso da referida energia de modo a praticar a sustentabilidade socioambiental e econômica tanto na escola, quanto em casa e onde quer que esteja e que medidas de educação socioambiental como estas devem ser replicadas.
Na rubu igrivosti: egzistencija i transcendencija u filmu eXistenZ
Authors: Jerić Ante
Published: October 2020

Filozofija i znanstvena fantastika kao različiti diskursi na različite načine koriste protuintuitivne scenarije. Filozofija nastupa fundacijski u pokušaju utemeljivanja protuintuitivnih scenarija i dokazivanja njihove koherentnosti, dok znanstvena fantastika kao transmedijalni fikcionalni žanr pretpostavlja njihovu koherentnost i nastupa pragmatički prilikom pripovijedanja o tome kako bi to bilo kad bi se oni obistinili. Ovaj rad posvećen je analizi znanstveno-fantastičnog filma Davida Cronenberga eXistenZ (1999). U njemu branim dvije teze. Kritičko-teorijska teza: Cronenberg smatra da život u kasnom kapitalizmu biva organiziran sukladno logici igre, predočava tu logiku ovim fikcionalnim scenarijem i prokazuje je kao nepoželjnu. Metafizička teza: Cronenbergov dijegetski svijet donosi novu, iako ne originalnu, artikulaciju skepticizma kao starog filozofskog problema. Pokušaj fundiranja metafizičkih pretpostavki ovog dijegetskog svijeta reanimira kartezijanski interakcionistički dualizam i kantovski transcendentalni idealizam kao dvije dobro poznate i utvrđene filozofske pozicije.
Građenje raspoloženja filmom kao postupak poetizacije
Authors: Turković Hrvoje
Published: October 2020

Visokoumjetnička dostignuća u dugotrajnoj tradiciji zapadnjačke estetičke misli vezuju se uz postizanje poetičnosti, a ova uz pobuđivanje osjećaja, pa se i u filmskoj tradiciji poetičnost uspostavljala kao ideal paradigmatski umjetničkog dostignuća filma. No, među različitim tipovima osjećaja kojima raspolaže čovjek, poetska djela ciljaju u pravilu na pobuđivanje nekog općeg raspoloženja, a ne neke izričite emocije. U radu se prvo daje pregled razlika između emocija i raspoloženja, potom se na primjeru uvodne scene u filmu Građanin Kane demonstrira kojim se različitim sredstvima u opisno orijentiranoj uvodnoj sceni postiže određen ‘ugođaj’ (turobno raspoloženje). Pitanje je, međutim, kako se opisanim heterogenim stilskim sredstvima može pobuditi vrlo specifično, jedinstveno, holističko raspoloženje. Odgovor je, ukratko, u tome da jednako kao što raspoloženja utječu na selektivan odnos prema okolini, birajući u njoj za percepciju one aspekte koji su sukladni, kongruentni s osnovnim karakteristikama određenog raspoloženja, tako i filmski stvaratelji biraju onaj repertoar stilskih značajki danoga filma što je kongruentan s ciljanim raspoloženjima kako bi ih pobudili u gledatelja koji prepušteno gleda film.
Understanding the costs of urban sanitation: towards a standard costing model
Authors: Sainati Tristano , Zakaria Fiona , Locatelli Giorgio , Sleigh P. Andrew , Evans Barbara
Published: October 2020

There is a dearth of reliable cost data for urban sanitation. In the absence of high-quality global data, the full cost of sustainable implementation of urban sanitation remains uncertain. This paper proposes an approach for developing bespoke parametric cost estimation models for easy and reliable estimation of the costs of alternative sanitation technologies in a range of geographical contexts. A key requirement for the development of these models is the establishment of a large database of empirical information on the current costs of sanitation systems. Such a database does not currently exist. Two foundational tools are proposed. Firstly, a standard metric for reporting the costs of urban sanitation systems, total annualised cost per household. Secondly, a standardised approach to the collection of empirical cost data, the Novel Ball-Park Reporting Approach (NBPRA). Data from the NBPRA are presented for 87 individual sanitation components from 25 cities in 10 countries. Broad cost ranges for different archetypal systems have been estimated; these currently have high levels of uncertainty. Further work is proposed to collect additional data, build up the global database, and develop parametric cost estimation models with higher reliability.
Comparative life cycle assessment of four commonly used point-of-use water treatment technologies
Authors: Walsh Tara , Mellor Jonathan
Published: October 2020

Across the globe, billions of people lack access to safe drinking water. Many different point-of-use (POU) technologies have been developed that significantly reduce the disease-causing pathogens found in untreated water. With many different technologies available, it can be difficult to choose which technology to implement in specific areas. Beyond the cost of each technology, the environmental impacts could bring additional harm to a community. Life cycle assessments (LCAs) are used to make comparisons across different technologies. This study uses an LCA to compare boiling water, ceramic water filters, BioSand filters and POU chlorination as treatment options in the rural community of Thohoyandou, Limpopo Province, South Africa utilizing previously published, open-access data. Global warming potential, water use, energy use, smog formation, particulate matter and land use are the studied environmental impacts. Results found that boiling had the most impact on energy use, global warming potential, smog and land use; chlorination had the greatest impact on particulate matter and water use. A cost comparison found boiling water to be most expensive at 0.053 USD per liter and chlorination to be least expensive at 0.0005 USD per liter.
Water quality variation and its conditioning factors in the Three Gorges Reservoir, China
Authors: Wang Xiaoxiao , Bing Haijian , Wu Yanhong , Zhou Jun , Zhu He , Wu Yong , Sun Hongyang
Published: October 2020

The variation of water quality has been an issue of concern since the impoundment of the Three Gorges Reservoir (TGR). In this study, water samples from the TGR were collected in July and November 2015 and in May and July 2016 to reveal the variations of water quality and its key conditioning factors. The results showed that the concentrations of major ions, nutrients and heavy metals in the TGR's water body exhibited heterogeneous variations. Especially, the hotspot for major ions and heavy metals emerged at Chongqing downtown, and a decreasing trend from Chongqing toward the TGR dam was spatially observed. The heterogeneous variation of hydrochemistry in the TGR revealed the integrated influences of natural processes and human activities. Especially, the intense rainfall in the spring and early summer of 2016 promoted the transport of pollutants to the TGR, and further magnified the influence of large cities on the spatial variations of the hydrochemistry in the TGR. This study will give more insights into the change of water quality in large reservoirs in the context of the intense rainfall and the human activities.
Development of Aldose Reductase Inhibitors for the Treatment of Inflammatory Disorders and Cancer: Current Drug Design Strategies and Future Directions
Authors: Sonowal Himangshu , Ramana Kota V.
Published: October 2020

Aldose Reductase (AR) is an enzyme that converts glucose to sorbitol during
the polyol pathway of glucose metabolism. AR has been shown to be involved in the development
of secondary diabetic complications due to its involvement in causing osmotic
as well as oxidative stress. Various AR inhibitors have been tested for their use to treat secondary
diabetic complications, such as retinopathy, neuropathy, and nephropathy in clinical
studies. Recent studies also suggest the potential role of AR in mediating various inflammatory
complications. Therefore, the studies on the development and potential use
of AR inhibitors to treat inflammatory complications and cancer besides diabetes are currently
on the rise. Further, genetic mutagenesis studies, computer modeling, and molecular
dynamics studies have helped design novel and potent AR inhibitors. This review discussed
the potential new therapeutic use of AR inhibitors in targeting inflammatory disorders
and cancer besides diabetic complications. Further, we summarized studies on how
AR inhibitors have been designed and developed for therapeutic purposes in the last few
decades.
Plant Derived Silver Nanoparticles and their Therapeutic Applications
Authors: Roy Arpita
Published: October 2020

Background:

Nanotechnology involves the synthesis of materials that are in nanometre sizes
(1 to 100 nm). Nanotechnology has appeared as a new area of fundamental science and is getting
worldwide consideration due to its extensive applications. They have exceptional physicochemical
properties due to their unique size, shape and size distribution.

Methods:

Conventionally, nanoparticles were synthesized using physical and chemical methods. These
methods have various disadvantages; therefore, biological methods possess great interest. Biological
synthesis uses bacteria, fungi, algae and plants.

Results:

Biosynthesis of nanoparticles using plants emerges as nanofactories as they offer nontoxic, clean
and eco-friendly methods with various physicochemical properties. Out of all the nanoparticles, silver nanoparticles
gain special attention due to their various therapeutical and environmental applications.

Conclusion:

In this review, a summary of various reports within the last ten years where plants were
utilized for silver nanoparticle synthesis has been discussed. Further mechanisms involving synthesis,
factors affecting the process of synthesis and therapeutical applications have been discussed.