Reservatórios turbidíticos – simulação da morfologia por estatísticas multiponto e avaliação das propriedades petrofísicas

Esta dissertação apresenta uma metodologia original para simular a morfologia e as propriedades petrofísicas de reservatórios de hidrocarbonetos em sistemas de canais turbidíticos. Estes sistemas são constituídos por complexos, ou seja, conjuntos de canais de arquitetura meandriforme, e são considerados importantes alvos para a indústria petrolífera. A simulação da morfologia divide-se em duas partes, primeiramente é simulada a trajetória do complexo e depois são simuladas as trajetórias dos canais propriamente ditos condicionadas à trajetória do complexo. O algoritmo de simulação utiliza as classes de ângulos azimutais de linhas poligonais de treino como uma variável aleatória. As trajetórias são simuladas também como linhas poligonais, condicionais a estatísticas multiponto das trajetórias de treino e a pontos de controlo. As estatísticas multiponto são organizadas em árvore, que guarda sequências de classes de orientação que ocorrem na trajetória de treino e as respetivas probabilidades de ocorrência. Para avaliar as propriedades petrofísicas, o modelo morfológico das trajetórias é convertido para uma malha de blocos de alta resolução, identificando-se, em cada bloco, a fácies preponderante de acordo com um modelo conceptual de zonamento da secção dos canais. A conversão prioriza os canais mais recentes (do topo) sobre os mais antigos (da base). A cada fácies é associada uma lei de distribuição da porosidade e permeabilidade, assim são geradas imagens destas propriedades petrofísicas por Simulação Sequencial Direta com histogramas locais. Finalmente, o número de blocos da malha é reduzido por upscaling e as simulações são ordenadas para poderem ser utilizadas nos simuladores de fluxo. Para ilustrar a metodologia, utilizaram-se imagens de sísmica 3D de um reservatório turbidítico na Bacia do Baixo Congo para extrair leis de distribuição das dimensões dos canais e trajetórias de treino. Os resultados representam corretamente a arquitetura complexa destes sistemas.

Profissões : sinergias entre o video interactivo e o 3D estereoscópico

Trabalho de Projecto apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Novos Media e Práticas Web

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Development of human central nervous system 3D in vitro models for preclinical research

Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.