Recent efforts to optimize oil recovery from Green River reservoirs, Uinta Basin, have stimulated the need for better understanding of the reservoir connectivity at the scale of the operational unit. This study focuses on Green River reservoirs in the Wells Draw study area where oil production response to implemented waterflood is poor and a better understanding of the reservoir connectivity is required to enhance future secondary oil recovery. Correlating the sand bodies between well locations in the area remains difficult at 40-acre well spacing. Thus, interwell connectivity of the reservoirs is uncertain. Understanding the reservoir connectivity in the Wells Draw study area requires integration of all static and dynamic data for generation of probabilistic models of the reservoir at the interwell locations. The objective of this study is two-fold. The first objective was to determine reservoir connectivity at the interwell scale in the Wells Draw study area. To achieve this goal, I used well log and perforation data in the Wells Draw study area to produce probabilistic models of net-porosity for four producing intervals: (1) Castle Peak, (2) Lower Douglas Creek, (3) Upper Douglas Creek, and (4) Garden Gulch. The second objective was to find readily applicable methods for determining interwell connectivity. To achieve this goal, I used sandstone net thickness and perforation data to evaluate interwell connectivity in the Wells Draw study area. This evaluation was done to: (1) assess and visualize connectivity, (2) provide an assessment of connectivity for validating / calibrating percolation and capacitance based methods, and (3) determine flow barriers for simulation. The probabilistic models encompass the four producing intervals with a gross thickness of 1,900 ft and enable simulation assessments of different development strategies for optimization of oil recovery in the Wells Draw study area. The method developed for determining interwell connectivity in Wells Draw study area is reliable and suited to the four producing intervals. Also, this study shows that the percolation based method is reliable for determining interwell connectivity in the four producing intervals.

Notre thèse est consacrée à l’étude des méthodes de rangements multicritères dans le cadre de la problématique de tri. Dans un problème de tri une personne, appelée décideur, désire assigner un objet, appelé action, à des catégories prédéfinies. Des problèmes de tri surgissent régulièrement dans la vie de tous les jours. Par exemple, un médecin ausculte son patient et sur base des symptômes observés, il assigne son patient à une catégorie de pathologies. Ainsi, le médecin peut prescrire un traitement approprié. Par ailleurs, on catégorise les cyclones tropicaux en fonction de leur vitesse, pression superficielle et de la hauteur de marée. En fonction de la catégorie du cyclone, des dégâts éventuels peuvent être prédits et des mesures de protection adéquates devront être prises. Dans un problème de tri, un décideur regroupe ainsi les actions qu’il considère similaires, à des fins descriptives, organisationnelles ou préventives. Nous supposerons en outre que le décideur exprime une relation de préférence entre les classes préalablement définies. D’autre part, les méthodes de rangement permettent de ranger les actions de la meilleure à la moins bonne. Nul étudiant ne peut nier l’existence des " rankings " d’universités. Une société ordonne les candidats à l’issue d’un entretien d’embauche. Une société désire par ailleurs établir des partenariats avec les fournisseurs les plus performants. Nous sommes tous confrontés à cette tâche délicate de ranger les actions de la meilleure à la moins bonne. Les méthodes d’aide à la décision proposent des techniques permettant à un décideur d’obtenir un rangement d’actions. L’objectif de cette thèse est d’étudier la possibilité de résoudre des problèmes de tri à l’aide de méthodes de rangement. L’approche adoptée est de ranger une action particulière par rapport à des normes ou profils définissant les catégories. L’assignation de l’action sera dès lors basée sur sa position dans ce rangement particulier. Quelles sont les hypothèses nécessaires pour un tel modèle ? Ces méthodes présentent-elles un biais ou ont-elles d’autres avantages par rapport aux méthodes de tri existantes? Est-il préférable de modéliser les catégories à l’aide de critères même si celles-ci ne présentent pas de relation de préférence ? Dans cette thèse nous donnerons des premiers éléments de réponse en développant de nouvelles méthodes de tri basées sur des méthodes de rangement existantes.

The central line-depths of absorption lines depend upon stellar temperature. By dividing the central line-depth of such a line with a central line-depth independent of temperature, a thermometer of relative star temperatures is obtained in the form of a line-depth ratio (LDR), once it is related to an effective temperature scale. Such thermometers are known to give precise results which is why the method is pursued. The purpose of this work is to rank LDRs according to a set of criteria to find the most suitable ratio to measure temperature. This is done based on a set of LDRs measured for a large sample of dwarf stars with known effective temperature, atmospheric pressure and chemical composition. Numerous LDRs are eliminated because their temperature dependence are limited to a short temperature interval. Further LDRs are eliminated due to dependence on the atmospheric pressure and chemical composition of the LDR. The remaining LDRs are ranked based on the strength of temperature dependence, the fit of the representative polynomial to the data points and the number of data points available. The best ranked LDR provides a temperature resolution smaller than 10 K over a temperature interval of 4500-6250 K, assuming an uncertainty in LDR of 0.01.

Current methods of ranking and selecting biomass fuels are based on short lists of factors. The objective of this thesis is to develop and demonstrate a fuel ranking tool. Existing fuel decision methods and bioenergy technology are reviewed. A fuel ranking tool is then developed and demonstrated. Finally, a procedure for evaluating the thermal efficiency of a pellet stove bioenergy system is developed and implemented. The tool is designed to be applied by an engineer working in cooperation with the actual fuel user. The user identifies a list of all available fuels which are compatible with their specific energy system. The ranking tool is suitable for users of any sized bioenergy system used for space heating, processing heating, or electricity generation. Through effective communication the engineer lists the user’s performance requirements. Requirements considered in this thesis are economic cost of fuels, required storage space, combustion equipment cleaning, and air pollutants emitted during biofuel combustion. Performance indicators corresponding to the user’s requirements are then selected or developed by the engineer. Data is then collected by the engineer to be used for the evaluation of these indicators. The indicators are then combined using weighting factors by the engineer to assign a single numerical score to each fuel. These scores allow the fuels to quickly and easily be ranked by the user according to how well they satisfy the user’s requirements. The ranking tool is demonstrated by applying it to a situation of a pellet stove user with 3 available fuel types. The three fuels are ranked in terms of their ability to satisfy the user’s requirements with respect to economic cost, storage space, equipment cleaning, certain air pollutant emissions, and supporting the local economy. A pellet stove thermal efficiency evaluation method is used to determine the percentage of fuel heating value delivered as space heat to the room housing the stove. Natural and forced convection as well as radiation heat transfers are modeled. The procedure results in a thermal efficiency measurement of 62% +/- 1% and 58% +/- 1% for premium wood and wheat straw pellets, respectively.

Current methods of ranking and selecting biomass fuels are based on short lists of factors. The objective of this thesis is to develop and demonstrate a fuel ranking tool. Existing fuel decision methods and bioenergy technology are reviewed. A fuel ranking tool is then developed and demonstrated. Finally, a procedure for evaluating the thermal efficiency of a pellet stove bioenergy system is developed and implemented. The tool is designed to be applied by an engineer working in cooperation with the actual fuel user. The user identifies a list of all available fuels which are compatible with their specific energy system. The ranking tool is suitable for users of any sized bioenergy system used for space heating, processing heating, or electricity generation. Through effective communication the engineer lists the user’s performance requirements. Requirements considered in this thesis are economic cost of fuels, required storage space, combustion equipment cleaning, and air pollutants emitted during biofuel combustion. Performance indicators corresponding to the user’s requirements are then selected or developed by the engineer. Data is then collected by the engineer to be used for the evaluation of these indicators. The indicators are then combined using weighting factors by the engineer to assign a single numerical score to each fuel. These scores allow the fuels to quickly and easily be ranked by the user according to how well they satisfy the user’s requirements. The ranking tool is demonstrated by applying it to a situation of a pellet stove user with 3 available fuel types. The three fuels are ranked in terms of their ability to satisfy the user’s requirements with respect to economic cost, storage space, equipment cleaning, certain air pollutant emissions, and supporting the local economy. A pellet stove thermal efficiency evaluation method is used to determine the percentage of fuel heating value delivered as space heat to the room housing the stove. Natural and forced convection as well as radiation heat transfers are modeled. The procedure results in a thermal efficiency measurement of 62% +/- 1% and 58% +/- 1% for premium wood and wheat straw pellets, respectively.

Centrally controlled search engines will not be sufficient and reliable for indexing and searching the rapidly growing World Wide Web in near future. A better solution is to enable the Web to index itself in a decentralized manner. Existing distributed approaches for ranking search results do not provide flexible searching, complete results and ranking with high accuracy. This thesis presents a decentralized Web search mechanism, named DEWS, which enables existing webservers to collaborate with each other to form a distributed index of the Web. DEWS can rank the search results based on query keyword relevance and relative importance of websites in a distributed manner preserving a hyperlink overlay on top of a structured P2P overlay. It also supports approximate matching of query keywords using phonetic codes and n-grams along with list decoding of a linear covering code. DEWS supports incremental retrieval of search results in a decentralized manner which reduces network bandwidth required for query resolution. It uses an efficient routing mechanism extending the Plexus routing protocol with a message aggregation technique. DEWS maintains replica of indexes, which reduces routing hops and makes DEWS robust to webservers failure. The standard LETOR 3.0 dataset was used to validate the DEWS protocol. Simulation results show that the ranking accuracy of DEWS is close to the centralized case, while network overhead for collaborative search and indexing is logarithmic on network size. The results also show that DEWS is resilient to changes in the available pool of indexing webservers and works efficiently even in the presence of heavy query load.

This thesis develops adaptive simulation control techniques that differentiate between competing system configurations. Here, a system is a real world environment under analysis. In this context, proposed modifications to a system denoted by different configurations are evaluated using large-scale hybrid simulation. Adaptive control techniques, using ranking and selection methods, compare the relative worth of competing configurations and use these comparisons to control the number of required simulation observations. Adaptive techniques necessitate embedded statistical computations suitable for the variety of data found in detailed simulations, including hybrid and agent-based simulations. These embedded statistical computations apply efficient sampling methods to collect data from simulations running on a network of workstations. The National Airspace System provides a test case for the application of these techniques to the analysis and design of complex systems, implemented here in the Reconfigurable Flight Simulator, a large-scale hybrid simulation. Implications of these techniques for the use of simulation as a design activity are also presented.

Recent efforts to optimize oil recovery from Green River reservoirs, Uinta Basin, have stimulated the need for better understanding of the reservoir connectivity at the scale of the operational unit. This study focuses on Green River reservoirs in the Wells Draw study area where oil production response to implemented waterflood is poor and a better understanding of the reservoir connectivity is required to enhance future secondary oil recovery. Correlating the sand bodies between well locations in the area remains difficult at 40-acre well spacing. Thus, interwell connectivity of the reservoirs is uncertain. Understanding the reservoir connectivity in the Wells Draw study area requires integration of all static and dynamic data for generation of probabilistic models of the reservoir at the interwell locations. The objective of this study is two-fold. The first objective was to determine reservoir connectivity at the interwell scale in the Wells Draw study area. To achieve this goal, I used well log and perforation data in the Wells Draw study area to produce probabilistic models of net-porosity for four producing intervals: (1) Castle Peak, (2) Lower Douglas Creek, (3) Upper Douglas Creek, and (4) Garden Gulch. The second objective was to find readily applicable methods for determining interwell connectivity. To achieve this goal, I used sandstone net thickness and perforation data to evaluate interwell connectivity in the Wells Draw study area. This evaluation was done to: (1) assess and visualize connectivity, (2) provide an assessment of connectivity for validating / calibrating percolation and capacitance based methods, and (3) determine flow barriers for simulation. The probabilistic models encompass the four producing intervals with a gross thickness of 1,900 ft and enable simulation assessments of different development strategies for optimization of oil recovery in the Wells Draw study area. The method developed for determining interwell connectivity in Wells Draw study area is reliable and suited to the four producing intervals. Also, this study shows that the percolation based method is reliable for determining interwell connectivity in the four producing intervals.

Large-scale socially-generated metadata is one of the key features driving the growth and success of the emerging Social Web. Recently there have been many research efforts to study the quality of this metadata - like user-contributed tags, comments, and ratings - and its potential impact on new opportunities for intelligent information access. However, much existing research relies on quality assessments made by human experts external to a Social Web community. In the present study, we are interested in understanding how an online community itself perceives the relative quality of its own user-contributed content, which has important implications for the successful selfregulation and growth of the Social Web in the presence of increasing spam and a flood of Social Web metadata. We propose and evaluate a machine learning-based approach for ranking comments on the Social Web based on the community's expressed preferences, which can be used to promote high-quality comments and filter out low-quality comments. We study several factors impacting community preference, including the contributor's reputation and community activity level, as well as the complexity and richness of the comment. Through experiments, we find that the proposed approach results in significant improvement in ranking quality versus alternative approaches.

Higher Education Institutions (HEIs), whose basic aim is the contribution of scientific developments and providing services have been showing an increasing interest in evaluation of productivity and quality. In fact, productivity and quality evaluation is essential for all type of organizations since the evaluation helps organization to set short and long term goals by defining the current situations, future expectations and the roadmap to fulfill these expectations. The studies on evaluation of academic productivity and quality have led to development of new academic fields such as Bibliometrics, Scientometrics and Informetrics. Consequently, new academic journals specialized on these disciplines have emerged. Moreover, during the last twenty years, as an outcome of these new emerging academic fields, several university ranking systems have been developed both at national and global level. Although these university ranking systems have attracted attention / they have been criticized due to a number of issues such as inappropriateness of indicators chosen, scoring procedure adopted, etc. In this study, an academic performance evaluation and ranking system has been developed and implemented. The new system which processes about 2,000 world universities is based on data from non subjective, reliable and universally accepted online sources. The scoring procedure includes statistical analysis and data has been collected via a tool developed for this purpose to eliminate human errors.