[pt] Campos de petróleo com gás não associado do tipo gás condensado possuem destaque pelo maior valor econômico agregado associado a seu recurso energético: a expressiva quantidade de condensado produzida, além do próprio gás. Porém, tais reservatórios possuem um comportamento termodinâmico particular, induzindo mudanças de composição e, consequentemente, fase ao longo do processo de produção por depleção. Nas condições de reservatório, por exemplo, pode ocorrer o fenômeno chamado de condensate blockage, em que bancos de condensado se formam, geralmente em regiões próximas aos poços, dificultando assim o escoamento e afetando a produção de gás. A fim de definirmos a melhor estratégia de gerenciamento de um projeto a ser implementado ao longo da explotação desse tipo de reservatório, uma ferramenta importante utilizada pelos engenheiros é a simulação numérica. Especialmente relacionadas à representação do fenômeno físico-químico citado, nas simulações se utilizam as curvas de permeabilidade relativa. Na realidade, porém, existe uma certa limitação de representatividade do fenômeno nos ensaios laboratoriais praticados pela indústria e os melhores insumos poderiam ser fornecidos por simulações em rede de poros, com modelos que representem a sua alteração com função das mudanças na tensão interfacial e na velocidade de escoamento ao longo do reservatório. A reprodução de uma simulação de escoamento em rede de poros para a escala mais próxima possível em uma simulação de simulador comercial de diferenças finitas é validada. Da simulação em rede de poros até a escala de campo praticada nas simulações de reservatórios, uma metodologia de scale-up é proposta, utilizando um processo de otimização, procurando ser fiel à curva de permeabilidade relativa original, em escala de microporo, obtida simulando fenomenologicamente o processo de condensação no reservatório, através de um modelo que reproduza sua dependência com a velocidade desenvolvida pelas fases em meio poroso. A comparação de produtividades na escala de campo e na evolução da saturação de condensado em regiões próximas aos poços foi apresentada para as três curvas de permeabilidade relativa utilizadas. Os resultados mostram que a metodologia proposta consegue ser mais fiel à influência da condensação no reservatório sobre a produtividade dos poços quando comparada ao insumo de curva de permeabilidade relativa de ensaio laboratorial que apresenta o condensado mais móvel. / [en] Oil fields with non-associated gas like gas condensate type stand out due to the higher added economic value associated with their energy resource: the significant amount of condensate produced, in addition to the gas itself. However, such reservoirs have a particular thermodynamic behavior, inducing changes in composition and, consequently, phase throughout the depletion production process. Under reservoir conditions, for example, the phenomenon called condensate blockage may occur, in which bridges of condensate are formed, usually in regions close to the wells, thus hindering flow and affecting gas production. In order to define the best management strategy for a project to be implemented throughout the exploitation of this type of reservoir, an important tool used by engineers is numerical simulation. The relative permeability curves are used in the simulations, especially related to the representation of the mentioned physical phenomenon. In reality, however, there is a specific limitation of representativeness of the phenomenon in the laboratory tests carried out by the industry, and the best inputs could be provided by simulations in a pore network, with models that represent its alteration as a function of changes in interfacial tension and flow velocity along the reservoir. The reproduction of a pore network flow simulation to the closest possible scale in a commercial finite difference simulation is validated. From the pore network simulation to the field scale practiced in reservoir simulations, a scale-up methodology is proposed, using an optimization process, seeking to be faithful to the original relative permeability curve, on a microporous scale, obtained by simulating phenomenologically the condensation process in the reservoir, using a model that reproduces its dependence on the velocity flow developed by the phases in a porous medium. The three relative permeability curves used were presented by comparing productivities at the field scale and the evolution of condensate saturation in regions close to the wells. The results show that the proposed methodology proves to be more faithful to the influence of condensation in the reservoir on the productivity of the wells when compared to the relative permeability curve input from the laboratory test, which presents the condensate with more mobility.

With the accessibility to generous spectrum and development of high gain antenna arrays, wireless communication in higher frequency bands providing multi-gigabit short range wireless access has become a reality. The directional antennas have proven to reduce losses due to interfering signals but are still exposed to blockage events. These events impede the overall user connectivity and throughput. A mobile blocker such as a moving vehicle amplifies the blockage effect. Modelling the blockage effects helps in understanding these events in depth and in maintaining the user connectivity. This thesis proposes the use of a four state channel model to describe blockage events in high-frequency communication. Two deep learning architectures are then designed and evaluated for two possible tasks, the prediction of the signal strength and the classification of the channel state. The evaluations based on simulated traces show high accuracy, and suggest that the proposed models have the potential to be extended for deployment in real systems. / Med tillgängligheten till generöst spektrum och utveckling av antennmatriser med hög förstärkning har trådlös kommunikation i högre frekvensband som ger multi-gigabit kortdistans trådlös åtkomst blivit verklighet. Riktningsantennerna har visat sig minska förluster på grund av störande signaler men är fortfarande utsatta för blockeringshändelser. Dessa händelser hindrar den övergripande användaranslutningen och genomströmningen. En mobil blockerare såsom ett fordon i rörelse förstärker blockeringseffekten. Modellering av blockeringseffekter hjälper till att förstå dessa händelser på djupet och bibehålla användaranslutningen. Denna avhandling föreslår användning av en fyrstatskanalmodell för att beskriva blockeringshändelser i högfrekvent kommunikation. Två djupinlärningsarkitekturer designas och utvärderas för två möjliga uppgifter, förutsägelsen av signalstyrkan och klassificeringen av kanalstatusen. Utvärderingarna baserade på simulerade spår visar hög noggrannhet och föreslår att de föreslagna modellerna har potential att utökas för distribution i verkliga system.

Compressed sensing has been proposed as a computationally efficient method to estimate the finite-dimensional signals. The idea is to develop an undersampling operator that can sample the large but finite-dimensional sparse signals with a rate much below the required Nyquist rate. In other words, considering the sparsity level of the signal, the compressed sensing samples the signal with a rate proportional to the amount of information hidden in the signal. In this dissertation, first, we employ compressed sensing for physical layer signal processing of directional millimeter-wave communication. Second, we go through the theoretical aspect of compressed sensing by running a comprehensive theoretical analysis of compressed sensing to address two main unsolved problems, (1) continuous-extension compressed sensing in locally convex space and (2) computing the optimum subspace and its dimension using the idea of equivalent topologies using Köthe sequence. In the first part of this thesis, we employ compressed sensing to address various problems in directional millimeter-wave communication. In particular, we are focusing on stochastic characteristics of the underlying channel to characterize, detect, estimate, and track angular parameters of doubly directional millimeter-wave communication. For this purpose, we employ compressed sensing in combination with other stochastic methods such as Correlation Matrix Distance (CMD), spectral overlap, autoregressive process, and Fuzzy entropy to (1) study the (non) stationary behavior of the channel and (2) estimate and track channel parameters. This class of applications is finite-dimensional signals. Compressed sensing demonstrates great capability in sampling finite-dimensional signals. Nevertheless, it does not show the same performance sampling the semi-infinite and infinite-dimensional signals. The second part of the thesis is more theoretical works on compressed sensing toward application. In chapter 4, we leverage the group Fourier theory and the stochastical nature of the directional communication to introduce families of the linear and quadratic family of displacement operators that track the join-distribution signals by mapping the old coordinates to the predicted new coordinates. We have shown that the continuous linear time-variant millimeter-wave channel can be represented as the product of channel Wigner distribution and doubly directional channel. We notice that the localization operators in the given model are non-associative structures. The structure of the linear and quadratic localization operator considering group and quasi-group are studied thoroughly. In the last two chapters, we propose continuous compressed sensing to address infinite-dimensional signals and apply the developed methods to a variety of applications. In chapter 5, we extend Hilbert-Schmidt integral operator to the Compressed Sensing Hilbert-Schmidt integral operator through the Kolmogorov conditional extension theorem. Two solutions for the Compressed Sensing Hilbert Schmidt integral operator have been proposed, (1) through Mercer's theorem and (2) through Green's theorem. We call the solution space the Compressed Sensing Karhunen-Loéve Expansion (CS-KLE) because of its deep relation to the conventional Karhunen-Loéve Expansion (KLE). The closed relation between CS-KLE and KLE is studied in the Hilbert space, with some additional structures inherited from the Banach space. We examine CS-KLE through a variety of finite-dimensional and infinite-dimensional compressible vector spaces. Chapter 6 proposes a theoretical framework to study the uniform convergence of a compressible vector space by formulating the compressed sensing in locally convex Hausdorff space, also known as Fréchet space. We examine the existence of an optimum subspace comprehensively and propose a method to compute the optimum subspace of both finite-dimensional and infinite-dimensional compressible topological vector spaces. To the author's best knowledge, we are the first group that proposes continuous compressed sensing that does not require any information about the local infinite-dimensional fluctuations of the signal.