Les travaux présentés dans ce mémoire concernent l'exploration de l'espace de conception des architectures SOC pour des applications orientées télécommunication. L'évolution importante des semi-conducteurs a permis l'implémentation de systèmes complets sur une puce. Cette implémentation a été rendue possible par des méthodologies de conception basées sur la réutilisation des composants existants (IP - Intellectual Property) qui, combinées ensemble, constituent le système. La différentiation des systèmes est obtenue par l'ajout d'IP propriétaires rattachées au système. L'apport des technologies classiques basées sur le modèle en Y (Y-chart) et les techniques de co-design se sont avérées insuffisantes dès lors que ces IPs initialement sous forme dure (hard IP) donc non modifiables ont étés proposées dans leur version paramétrable (Soft IP), pour garantir un meilleur dimensionnement du système. En effet, la modularité des IPs soft par leurs paramétrisations, créent un espace d'exploration qui s'avère extrêmement important et donc inexploitable par des techniques de conception ad hoc ou interactives. Le problème posé est l'optimisation mathématique des paramètres de l'ensemble des IPs soft constituant le SOC. Ce problème multidimensionnel en performance est aggravé, dans le cadre des SOC pour systèmes embarqués, par la prise en compte de la consommation d'énergie et de la surface en silicium. Le problème devient alors une optimisation multiobjectifs. Cette thèse propose une résolution de ce problème en plusieurs étapes : Dans une première étape, des techniques d'exploration pour le dimensionnement d'IP de processeur SuperScalair sont proposées. Ces techniques tiennent compte de trois critères: performance, consommation d'énergie et surface en silicium. Les résultats obtenus par des benchmarks multimédia "MiBench" de taille significative résultent dans un sous ensemble optimal au sens de Pareto, permettant de sélectionner une ou plusieurs solutions efficaces pour les applications cibles. La seconde étape est une extension du cadre précédent par couplage de l'exploration multiobjectifs avec une implémentation matérielle sur circuits FPGA. Elle permet alors une exploration avec matériel dans la boucle. Le principe poursuivi, à l'inverse des explorations effectuées à des niveaux d'abstraction élevés (SystemC), est qu'une exploration est d'autant plus efficace que les valeurs injectées à l'algorithme d'exploration sont proches de la réalité. L'autre aspect est que l'exploration par simulation des SOC reste problématique, ceci étant dû aux temps prohibitifs de la simulation et que l'exécution directe est toujours plus rapide, donc permet des explorations larges et réalistes. Cette approche est appliquée au processeur LEON v2.0 de l' ESA sur des circuits Xilinx Virtex-II qui, de par leur reconfigurabilité, permet le chargement de nouvelles configurations lors de l'exploration. Enfin, l'importance des SOC mixtes analogiques/numériques, nous a poussés à nous intéresser à l'optimisation des circuits analogiques et ce, sur le même principe, mais en utilisant des circuits FPAA (Field Programmable Analog Array) qui permettent la conception et l'implémentation d'applications sur circuits analogiques re-programmables. Cette possibilité permet de répondre à une fonctionnalité donnée en testant et explorant de nombreuses configurations, en les implémentant physiquement dans un circuit programmable et cela à moindre coût. La thèse conclut sur les perspectives pouvant découler des contributions de ce travail sur les méthodologies de conception de SOC dans les environnements SOPC.

Simulation-based optimization methodologies are widely applied in real world optimization problems. In developing these methodologies, beside simulation models, algorithms play a critical role. One example is an evolutionary multi objective optimization algorithm known as Reference point-based Non-dominated Sorting Genetic Algorithm-II (R-NSGA-II), which has shown to have some promising results in this regard. Its successor, R-NSGA-II-adaptive diversity control (hereafter Adaptive Epsilon-R-NSGA-II (AE-R-NSGA-II) algorithm) is one of the latest proposed extensions of the R-NSGA-II algorithm and in the early stages of its development. So far, little research exists on its applicability and usefulness, especially in real world optimization problems. This thesis evaluates behavior and performance of AE-R-NSGA-II, and to the best of our knowledge is one of its kind. To this aim, we have investigated the algorithm in two experiments, using two benchmark functions, 10 performance measures, and a behavioral characteristics analysis method. The experiments are designed to (i) assess behavior and performance of AE-R-NSGA-II, (ii) and facilitate efficient use of the algorithm in real world optimization problems. This is achieved through the algorithm parameter configuration (parametric study) according to the problem characteristics. The behavior and performance of the algorithm in terms of diversity of the solutions obtained, and their convergence to the optimal Pareto front is studied in the first experiment through manipulating a parameter of the algorithm referred to as Adaptive epsilon coefficient value (C), and in the second experiment through manipulating the Reference point (R) according to the distance between the reference point and the global Pareto front. Therefore, as one contribution of this study two new diversity performance measures (called Modified spread, and Population diversity), and the behavioral characteristics analysis method called R-NSGA-II adaptive epsilon value have been introduced and applied. They can be modified and applied for the evaluation of any reference point based algorithm such as the AE-R-NSGA-II. Additionally, this project contributed to improving the Benchmark software, for instance by identifying new features that can facilitate future research in this area. Some of the findings of the study are as follows: (i) systematic changes of C and R parameters influence the diversity and convergence of the obtained solutions (to the optimal Pareto front and to the reference point), (ii) there is a tradeoff between the diversity and convergence speed, according to the systematic changes in the settings, (iii) the proposed diversity measures and the method are applicable and useful in combination with other performance measures. Moreover, we realized that because of the unexpected abnormal behaviors of the algorithm, in some cases the results are conflicting, therefore, impossible to interpret. This shows that still further research is required to verify the applicability and usefulness of AE-R-NSGA-II in practice. The knowledge gained in this study helps improving the algorithm.

Neste trabalho é desenvolvida uma metodologia de otimização multiobjetivo baseada no NSGA-II (Nondominated Sorting Genetic Algorithm), a qual visa a otimização do projeto de máquinas de indução pentafásicas. A escolha deste tipo de máquina se justifica pelo fato de que elas apresentam vantagens importantes quando comparadas com as trifásicas convencionais, tais como maior potência e maior torque para um mesmo volume de material ativo, além da possibilidade de operar na ocorrência de falhas (perda de uma ou duas fases). Na otimização de máquinas de indução vários objetivos podem ser definidos, sendo estes muitas vezes conflitantes. Neste contexto, este trabalho visa obter soluções que representam um compromisso entre dois objetivos: rendimento e custo do material ativo (ferro e material condutor). O algoritmo de otimização desenvolvido e implementado utiliza dois controles de diversidade da população, um baseado no fenótipo dos indivíduos, que é característico do NSGA-II, e outro adicional que é baseado no genótipo. A geometria do estator e do rotor da máquina e o seu modo de acionamento são parametrizados por 14 variáveis inteiras. O método desenvolvido foi implementado no Matlab R e aplicado a um caso prático de otimização de uma máquina de indução pentafásica considerando os dois objetivos citados. Os resultados práticos mostram que o método é capaz de obter projetos otimizados com maior rendimento e menor custo aproveitando as características particulares deste tipo de máquina. / In this work, it is developed a method of multiobjective optimization based on NSGAII (Nondominated Sorting Genetic Algorithm), which aims at optimizing the design of five-phase induction machines. The choice of this particular type of machine is justified by the fact that they have important advantages over conventional three-phase machines, such as higher power and higher torque for the same volume of material; in addition, they can operate under fault (loss of one or even two phases). When optimizing induction machines, several objectives can be defined, which are often conflicting. In this context, this work aims to obtain solutions that represent a trade-off between two objectives: efficiency and cost of active material (iron and conductor materials). The optimization algorithm that was developed and implemented uses two types of control for the diversity of the population, one based on the phenotype of the individuals, characteristic of the NSGA-II, and another one based on the genotype. The geometrical dimensions of the stator and rotor, together with the driving strategy, are parameterized by 14 integer variables. The developed method was implemented using Matlab R and applied to a practical case of a five-phase induction machine considering the aforementioned objectives. The practical results show that the method can lead to an optimized design with higher efficiency and at a lower cost, accounting for the special characteristics of this type of machine.

Neste trabalho é desenvolvida uma metodologia de otimização multiobjetivo baseada no NSGA-II (Nondominated Sorting Genetic Algorithm), a qual visa a otimização do projeto de máquinas de indução pentafásicas. A escolha deste tipo de máquina se justifica pelo fato de que elas apresentam vantagens importantes quando comparadas com as trifásicas convencionais, tais como maior potência e maior torque para um mesmo volume de material ativo, além da possibilidade de operar na ocorrência de falhas (perda de uma ou duas fases). Na otimização de máquinas de indução vários objetivos podem ser definidos, sendo estes muitas vezes conflitantes. Neste contexto, este trabalho visa obter soluções que representam um compromisso entre dois objetivos: rendimento e custo do material ativo (ferro e material condutor). O algoritmo de otimização desenvolvido e implementado utiliza dois controles de diversidade da população, um baseado no fenótipo dos indivíduos, que é característico do NSGA-II, e outro adicional que é baseado no genótipo. A geometria do estator e do rotor da máquina e o seu modo de acionamento são parametrizados por 14 variáveis inteiras. O método desenvolvido foi implementado no Matlab R e aplicado a um caso prático de otimização de uma máquina de indução pentafásica considerando os dois objetivos citados. Os resultados práticos mostram que o método é capaz de obter projetos otimizados com maior rendimento e menor custo aproveitando as características particulares deste tipo de máquina. / In this work, it is developed a method of multiobjective optimization based on NSGAII (Nondominated Sorting Genetic Algorithm), which aims at optimizing the design of five-phase induction machines. The choice of this particular type of machine is justified by the fact that they have important advantages over conventional three-phase machines, such as higher power and higher torque for the same volume of material; in addition, they can operate under fault (loss of one or even two phases). When optimizing induction machines, several objectives can be defined, which are often conflicting. In this context, this work aims to obtain solutions that represent a trade-off between two objectives: efficiency and cost of active material (iron and conductor materials). The optimization algorithm that was developed and implemented uses two types of control for the diversity of the population, one based on the phenotype of the individuals, characteristic of the NSGA-II, and another one based on the genotype. The geometrical dimensions of the stator and rotor, together with the driving strategy, are parameterized by 14 integer variables. The developed method was implemented using Matlab R and applied to a practical case of a five-phase induction machine considering the aforementioned objectives. The practical results show that the method can lead to an optimized design with higher efficiency and at a lower cost, accounting for the special characteristics of this type of machine.

Ce projet porte, dans un souci d’efficacité énergétique, sur la récupération d’énergie des rejets thermiques à basse température. Une analyse d’optimisation des technologies dans le but d’obtenir un système de revalorisation de chaleur rentable fait objet de cette recherche. Le but sera de soutirer la chaleur des rejets thermiques et de la réappliquer à un procédé industriel. Réduire la consommation énergétique d’une usine entre habituellement en conflit avec l’investissement requis pour les équipements de revalorisation de chaleur. Ce projet de maitrise porte sur l’application d’optimisations multiobjectives par algorithme génétique (GA) pour faciliter le design en retrofit des systèmes de revalorisation de chaleur industrielle. L’originalité de cette approche consiste à l’emploi du «fast non-dominant sorting genetic algorithm» ou NSGA-II dans le but de trouver les solutions optimales entre la valeur capitale et les pertes exergétiques des réseaux d’échangeurs de chaleur et de pompes à chaleur. Identifier les solutions optimales entre le coût et l’efficacité exergétique peut ensuite aider dans le processus de sélection d’un design approprié en considérant les coûts énergétiques. Afin de tester cette approche, une étude de cas est proposée pour la récupération de chaleur dans une usine de pâte et papier. Ceci inclut l’intégration d’échangeur de chaleur Shell&tube, d’échangeur à contact direct et de pompe à chaleur au réseau thermique existant. Pour l’étude de cas, le projet en collaboration avec Cascades est constitué de deux étapes, soit de ciblage et d’optimisation de solutions de retrofit du réseau d’échangeur de chaleur de l’usine de tissus Cascades à Kinsley Falls. L’étape de ciblage, basée sur la méthode d’analyse du pincement, permet d’identifier et de sélectionner les modifications de topologie du réseau d’échangeurs existant en y ajoutant de nouveaux équipements. Les scénarios résultants passent ensuite à l’étape d’optimisation où les modèles mathématiques pour chaque nouvel équipement sont optimisés afin de produire une courbe d’échange optimal entre le critère économique et exergétique. Pourquoi doubler l’analyse économique d’un critère d’exergie? D’abord, parce que les modèles économiques sont par définition de nature imprécise. Coupler les résultats des modèles économiques avec un critère exergétique permet d’identifier des solutions de retrofit plus efficaces sans trop s’éloigner d’un optimum économique. Ensuite, le rendement exergétique permet d’identifier les designs utilisant l’énergie de haute qualité, telle que l’électricité ou la vapeur, de façon plus efficace lorsque des sources d’énergie de basse qualité, telles que les effluents thermiques, sont disponibles. Ainsi en choisissant un design qui détruit moins d’exergie, il demandera un coût énergétique moindre. Les résultats de l’étude de cas publiés dans l’article montrent une possibilité de réduction des coûts en demande de vapeur de 89% tout en réduisant la destruction d’exergie de 82%. Dans certains cas de retrofit, la solution la plus justifiable économiquement est également très proche de la solution à destruction d’exergie minimale. L’analyse du réseau d’échangeurs et l’amélioration de son rendement exergétique permettront de justifier l’intégration de ces systèmes dans l’usine. Les diverses options pourront ensuite être considérées par Cascades pour leurs faisabilités technologiques et économiques sachant qu’elles ont été optimisées.

Energy consumption reduction is one of the priorities of metro operators, due to financial cost and environmental impact. The new signalling system Communications-Based Train Control (CBTC) is being installed in new and upgraded metro lines to increase transportation capacity. But its continuous communication feature also permits to improve the energy performance of traffic operation, by updating the control command of the Automatic Train Operation (ATO) system at any point of the route. The present research addresses two main topics. The first is the design of efficient CBTC speed profiles for undisturbed train trajectory between two stations. The second takes into account the interaction between two consecutive trains under abnormal traffic conditions and proposes a tracking algorithm to save energy. In the first part of the research an off-line methodology to design optimal speed profiles for CBTC-ATO controlled trains is proposed. The methodology is based on a new multi-objective optimisation algorithm named NSGA-II-F, which is used to design speed profiles in such a way that can cover all the possible efficient solutions in a pseudo-Pareto front. The pseudo–Pareto front is built by using dominated solutions to make available a complete set of feasible situations in a driving scenario. The uncertainty in the passenger load is modelled as a fuzzy parameter. Each of the resulting speed profiles is obtained as a set of parameters that can be sent to the ATO equipment to perform the driving during the operation. The proposed optimisation algorithm makes use of detailed simulation of the train motion. Therefore, a simulator of the train motion has been developed, including detailed model of the specific ATO equipment, the ATP constraints, the traction equipment, the train dynamics and the track. A subsequent analysis considers the effect in the design of considering the regenerative energy flow between the train and the surrounding railway system. The second part of the research is focused on the proposal and validation of a fuzzy tracking algorithm for controlling the motion of two consecutive trains during disturbed conditions. A disturbed condition is understood as a change in the nominal driving command of a leading train and its consequences in the subsequent trains. When a train runs close enough to the preceding one, a tracking algorithm is triggered to control the distance between both trains. The following train receives the LMA (limit of movement authority) via radio, which is updated periodically as the preceding train runs. The aim of the proposed algorithm is to take actions in such a way that the following train could track the leading train meeting the safety requirements and applying an energy saving driving technique (coasting command). The uncertainty in the variations of the speed of the preceding train is modelled as a fuzzy quantity. The proposed algorithm is based on the application of coasting commands when possible, substituting traction/braking cycles by traction/coasting cycles, and hence saving energy. Both algorithms were tested and validated by using a detailed simulation program. The NSGA-II-F algorithm provided additional energy savings when compared to fixed block distance-to-go configurations, and giving a more even distribution of the solutions. The fuzzy tracking algorithm provides energy savings with a minor impact on running times while improving comfort, because of the reduction of the inefficient traction/braking cycles. / <p>QC 20170216</p>

O presente trabalho otimizou os parâmetros concentrados do sistema de suspensão de um modelo de veículo completo, representando um automóvel de passeio que trafega a uma velocidade constante por um determinado perfil de pista previsto na norma ISO 8608, 1995, através da utilização de um algoritmo meta-heurístico de otimização multiobjetivo. Duas rotinas numérico-computacionais foram desenvolvidas, visando realizar tal otimização tanto no domínio do tempo quanto no domínio da frequência. A utilização de algoritmos meta-heurísticos vem ganhando espaço na otimização de sistemas mecânicos, proporcionando rapidez e precisão na obtenção de resultados ótimos. Ao se combinar um algoritmo de otimização a um modelo que represente satisfatoriamente um sistema mecânico, obtém-se uma ferramenta indicadora dos parâmetros de máxima eficiência do sistema, que pode ser utilizada em inúmeras aplicações. Pretendeu-se, com a integração de rotinas de análise dinâmica nos domínios do tempo e da frequência ao algoritmo genético de otimização multiobjetivo NSGA-II, desenvolvido por Deb et al., 2002, a obtenção de duas fronteiras ótimas de Pareto. Estas fronteiras consistem no conjunto de soluções não dominadas que minimizam as seguintes funções objetivo: o valor RMS ponderado da aceleração vertical do assento do motorista, o valor RMS da média do fator de amplificação dinâmica das quatro rodas do modelo e o máximo deslocamento relativo entre cada roda e a carroceria. O método proposto por Shinozuka e Jan, 1972, é utilizado para a obtenção do perfil de irregularidades da pista no domínio do tempo a partir das equações de densidade espectral de potência (PSD) que representam as diferentes classes de pavimentos. O método de Newmark, 1959, é utilizado para resolver a equação diferencial de movimento no domínio do tempo e obter a resposta dinâmica do modelo a tais irregularidades. O comportamento dinâmico do modelo de veículo no domínio da frequência foi obtido através da utilização da função de resposta em frequência (FRF) do modelo de veículo analisado. Os resultados demonstraram a capacidade de ambas as rotinas de análise dinâmica desenvolvidas de produzir resultados consistentes com os encontrados na literatura, bem como a capacidade dos algoritmos de otimização implementados de fornecer fronteiras ótimas de Pareto para os problemas propostos. / The proposed work optimized the concentrated parameters of a full-vehicle model’s suspension system, being that model representative of a passenger car which travels at a constant speed on a certain road profile provided by the ISO 8608, 1995, standard, using a multi-objective meta-heuristic optimization algorithm. Two numerical-computational routines were developed, seeking to perform said optimization for both the time and frequency domains. The use of meta-heuristic algorithms has been increasing in mechanical systems optimization, providing speed and accuracy in obtaining an optimal result. Combining an optimization algorithm with a model that satisfactorily represents a mechanical system yields a tool that indicates the system’s maximum efficiency parameters, which can be used in numerous applications. It was intended, with the integration of the dynamic analysis routines to the multi-objective genetic optimization algorithm NSGA-II, developed by Deb et al., 2002, the obtainment of two Pareto-optimal fronts. These fronts consist in the set of non-dominated solutions that minimize the following objective functions: the weighted RMS value of the driver’s seat vertical acceleration, the mean RMS value of the model wheel’s dynamic amplification factor, and the maximum relative displacement between each wheel and the body of the vehicle model. The method proposed by Shinozuka and Jan, 1972, is used to obtain the road irregularity profile in the time domain from the power spectral density (PSD) equations that represent the different pavement classes. The Newmark’s method (1959) is used to solve the differential motion equation in the time domain, in order to obtain the vehicle model’s responses to these irregularities. The dynamic behavior of the vehicle model in the frequency domain was obtained through the use of the frequency response function (FRF) of the analyzed model. The results showed the capacity of both the dynamic analysis routines developed in generating results that are consistent with those found in literature, as well as the capacity of the optimization algorithms implemented in providing Pareto optimal fronts to the proposed problems.

Because blades in jet engine compressors are subject to dynamic loads based on the engine's speed, it is essential that the blades are properly "tuned" to avoid resonance at those frequencies to ensure safe operation of the engine. The tuning process can be time consuming for designers because there are many parameters controlling the geometry of the blade and, therefore, its resonance frequencies. Humans cannot easily optimize design spaces consisting of multiple variables, but optimization algorithms can effectively optimize a design space with any number of design variables. Automated blade tuning can reduce design time while increasing the fidelity and robustness of the design. Using surrogate modeling techniques and gradient-free optimization algorithms, this thesis presents a method for automating the tuning process of an airfoil. Surrogate models are generated to relate airfoil geometry to the modal frequencies of the airfoil. These surrogates enable rapid exploration of the entire design space. The optimization algorithm uses a novel objective function that accounts for the contribution of every mode's value at a specific operating speed on a Campbell diagram. When the optimization converges on a solution, the new blade parameters are output to the designer for review. This optimization guarantees a feasible solution for tuning of a blade. With 21 geometric parameters controlling the shape of the blade, the geometry for an optimally tuned blade can be determined within 20 minutes.

O presente trabalho otimizou os parâmetros concentrados do sistema de suspensão de um modelo de veículo completo, representando um automóvel de passeio que trafega a uma velocidade constante por um determinado perfil de pista previsto na norma ISO 8608, 1995, através da utilização de um algoritmo meta-heurístico de otimização multiobjetivo. Duas rotinas numérico-computacionais foram desenvolvidas, visando realizar tal otimização tanto no domínio do tempo quanto no domínio da frequência. A utilização de algoritmos meta-heurísticos vem ganhando espaço na otimização de sistemas mecânicos, proporcionando rapidez e precisão na obtenção de resultados ótimos. Ao se combinar um algoritmo de otimização a um modelo que represente satisfatoriamente um sistema mecânico, obtém-se uma ferramenta indicadora dos parâmetros de máxima eficiência do sistema, que pode ser utilizada em inúmeras aplicações. Pretendeu-se, com a integração de rotinas de análise dinâmica nos domínios do tempo e da frequência ao algoritmo genético de otimização multiobjetivo NSGA-II, desenvolvido por Deb et al., 2002, a obtenção de duas fronteiras ótimas de Pareto. Estas fronteiras consistem no conjunto de soluções não dominadas que minimizam as seguintes funções objetivo: o valor RMS ponderado da aceleração vertical do assento do motorista, o valor RMS da média do fator de amplificação dinâmica das quatro rodas do modelo e o máximo deslocamento relativo entre cada roda e a carroceria. O método proposto por Shinozuka e Jan, 1972, é utilizado para a obtenção do perfil de irregularidades da pista no domínio do tempo a partir das equações de densidade espectral de potência (PSD) que representam as diferentes classes de pavimentos. O método de Newmark, 1959, é utilizado para resolver a equação diferencial de movimento no domínio do tempo e obter a resposta dinâmica do modelo a tais irregularidades. O comportamento dinâmico do modelo de veículo no domínio da frequência foi obtido através da utilização da função de resposta em frequência (FRF) do modelo de veículo analisado. Os resultados demonstraram a capacidade de ambas as rotinas de análise dinâmica desenvolvidas de produzir resultados consistentes com os encontrados na literatura, bem como a capacidade dos algoritmos de otimização implementados de fornecer fronteiras ótimas de Pareto para os problemas propostos. / The proposed work optimized the concentrated parameters of a full-vehicle model’s suspension system, being that model representative of a passenger car which travels at a constant speed on a certain road profile provided by the ISO 8608, 1995, standard, using a multi-objective meta-heuristic optimization algorithm. Two numerical-computational routines were developed, seeking to perform said optimization for both the time and frequency domains. The use of meta-heuristic algorithms has been increasing in mechanical systems optimization, providing speed and accuracy in obtaining an optimal result. Combining an optimization algorithm with a model that satisfactorily represents a mechanical system yields a tool that indicates the system’s maximum efficiency parameters, which can be used in numerous applications. It was intended, with the integration of the dynamic analysis routines to the multi-objective genetic optimization algorithm NSGA-II, developed by Deb et al., 2002, the obtainment of two Pareto-optimal fronts. These fronts consist in the set of non-dominated solutions that minimize the following objective functions: the weighted RMS value of the driver’s seat vertical acceleration, the mean RMS value of the model wheel’s dynamic amplification factor, and the maximum relative displacement between each wheel and the body of the vehicle model. The method proposed by Shinozuka and Jan, 1972, is used to obtain the road irregularity profile in the time domain from the power spectral density (PSD) equations that represent the different pavement classes. The Newmark’s method (1959) is used to solve the differential motion equation in the time domain, in order to obtain the vehicle model’s responses to these irregularities. The dynamic behavior of the vehicle model in the frequency domain was obtained through the use of the frequency response function (FRF) of the analyzed model. The results showed the capacity of both the dynamic analysis routines developed in generating results that are consistent with those found in literature, as well as the capacity of the optimization algorithms implemented in providing Pareto optimal fronts to the proposed problems.

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No. of bitstreams: 1 FranciscaDeFatimaDoNascimentoSilva_TESE.pdf: 4413362 bytes, checksum: e0033cfcbd51c0cdcb5f93d10f64d5d3 (MD5) Previous issue date: 2017-03-06 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / O desenvolvimento de um campo petrol?fero pode ser entendido como o conjunto de a??es necess?rias para colocar o campo em produ??o: perfura??es, sistemas de inje??o, plataformas, etc. A forma como ser? feito este desenvolvimento define uma ou mais alternativas. Assim, definir alternativas de desenvolvimento de um campo petrol?fero ? uma das tarefas mais importantes na ?rea de reservat?rios, dado que estas defini??es afetam o comportamento do reservat?rio, decis?es futuras, an?lises econ?micas e, consequentemente, a atratividade resultante dos projetos definidos. Este trabalho apresenta a implementa??o de um sistema otimizador multiobjetivo baseado no algoritmo gen?tico NSGA-II (Non-Dominated Sorting Genetic Algorithm), que oferece uma ferramenta de suporte ? decis?o e automatiza a busca de alternativas para o desenvolvimento de campos petrol?feros submetidos ao processo de inje??o de ?gua. Cada alternativa refere-se ? forma como um campo petrol?fero, conhecido e delimitado, ? colocado em produ??o, isto ?, diz respeito ? determina??o do n?mero e a disposi??o dos po?os produtores e injetores no campo. A aplica??o do algoritmo consiste em encontrar as configura??es de produ??o que, em longo prazo, forne?am o maior Valor Presente L?quido (VPL), obtido a partir do custo de investimento inicial, do pre?o do petr?leo, da produ??o de ?leo e dos custos de opera??o pagos durante o tempo de produ??o, ou seja, a condi??o operacional mais vi?vel economicamente, reduzindo o tempo do processo de tomada de decis?o. Com os resultados apresentados foi poss?vel observar que em v?rios casos as aplica??es das linhas de a??o possibilitaram aumentos significativos no VPL e no Fator de Recupera??o ao final do projeto. Considerando o Caso_36 de dimens?o de malha de 300m, o Fator de Recupera??o aumentou de 45,66% para 50,24%, um aumento de quase 5 pontos percentuais no volume de ?leo recuperado. Diante do exposto, observa-se que as interven??es operacionais de alterar (aumentar ou diminuir) a vaz?o de inje??o de ?gua inicial ou mudar o layout de malha no campo melhoram a rentabilidade, reduzindo os custos com a inje??o de ?gua, tratamento e descarte da ?gua produzida, aumentando o tempo de viabilidade do projeto. Por outro lado, ? importante destacar tamb?m que, em alguns casos, ao aplicar as linhas de a??o, o Fator de recupera??o final ? menor, mas ainda sim as redu??es dos custos operacionais viabilizam a opera??o. / The development of an oil field can be understood as the set of actions necessary to put the field into production: drilling, injection systems, platforms, etc. This development the way will be made defines an alternative. Set a development of an oil field alternative is one of the most important tasks in the reservoir area, given that this definition affects the reservoir behavior, future decisions, economic analysis and consequently the resulting attractiveness of the defined project. This paper presents the implementation of a system based on genetic algorithm multiobjective optimizer NSGA-II (Non-Dominated Sorting Genetic Algorithm), which offers a decision support tool and automates the search for alternatives to the development of the oilfield submitted to water injection process. Each alternative refers to how an oil field, known and defined, is put into production, that is, with respect to the determination of number and the disposition of producers wells and injectors in the field. The implementation of the algorithm is to find the production settings, in the long run, which provide the highest net present value (NPV), obtained from the initial investment cost, the price of oil, oil production and operation costs paid during the production time, considering the operational conditions economically viable, reducing operating costs and the time in the decision-making process. With the obtained results it was possible to observe that in many cases the application of the lines of action enabled relevant rise on the net present value (NPV) and also in the Recovery Factor, both seen in the end of the project. Considering the Case_36 of the mesh that has 300m, the Recovery Factor increased from 45,66% to 50,24%, a rise of almost 5 percentage points on the volume of oil recovered. In the light of what was presented, it may be perceived that the operations that alter (ascending or descending) the flow of water injection or that change the mesh?s layout on the field improve the profitability, reducing costs from the water injection, treatment and disposal of the produced water, increasing the duration of viability of the project. However, it is important to highlight that, in some cases, applying the lines of action, the final recovery factor is lower, but still the reductions of the operational costs will make the operation viable.