[pt] Desde a década de 80, abordagens data-driven têm sido utilizadas para identificação de fluidos e caracterização de reservatórios carbonáticos e siliciclásticos principalmente em relação à análise das amplitudes sísmicas. No entanto, técnicas aplicadas com sucesso para rochas siliciclásticas, como por exemplo: Análise AVO, inversões sísmicas e IDH (Indicadores Diretos de Hidrocarbonetos) revelaram não obter o mesmo êxito para reservatórios carbonáticos heterogêneos. Em contrapartida, diversos artigos mostram que fluxos de caracterização de reservatórios com modelos de física de rochas incorporados têm alcançado grande sucesso para obtenção de propriedades petrofísicas e atributos elásticos de ambas as rochas, utilizando sísmicas e well logs, em uma abordagem model-driven, focada nas características microestruturais do reservatório. Dessa forma, levando em consideração a importância de se utilizar modelos de física de rochas no escopo da caracterização de reservatórios, dois modelos de física de rochas - Xu e Payne e T-Matrix - foram aplicados, comparados e seus parâmetros foram estocasticamente avaliados e otimizados em um arcabouço Bayesiano. Através dessa abordagem, foi possível estimar, de uma forma confiável, os atributos elásticos de um reservatório carbonático (coquinas) levando em consideração diversos tipos de incertezas. Além disso, após a calibração e validação de ambos os modelos de física de rochas para diferentes poços, análises de sensibilidade foram realizadas para compreensão de forma quantitativa do comportamento dos atributos elásticos das coquinas em relação às alterações do conteúdo mineralógico, tipos de poro e fluidos desse reservatório. / [en] Since the 80 s, data-driven approaches have been used for fluids identification and reservoir characterization of siliciclastic and carbonate rocks mainly regarding seismic amplitudes analyses. However, techniques successfully applied for siliciclastic rocks, such as: AVO analysis, seismic inversions and DHI (Direct Hydrocarbon Indicators) ranking revealed not have achieved the same outstanding and reliable results for heterogeneous carbonate rocks. On the other hand, several articles demonstrate that reservoir characterization workflows with rock physics models embedded have been reaching a robust success in order to obtain petrophysical properties and elastic attributes of both rocks, from the seismic and well logs, in a model-driven approach focused on the reservoirs microstructural information. In this way, taking into account the importance of applying rock physics models in the scope of reservoir characterization, two rock physics models - Xu and Payne and T-Matrix - were applied, compared and their parameters were stochastically evaluated and optimized in a Bayesian framework. Through this approach, it was possible to estimate, in a reliable manner, the elastic attributes of a carbonate reservoir (coquinas) taking into consideration different kinds of uncertainties. Furthermore, after the calibration in the well location and validation of both rock physics models for other wells, sensitivity analyses were conducted in order to quantitatively understand how the coquinas elastic attributes behave regarding the variations in the reservoir mineralogical content, pore shapes and fluids.

(EN) Thesis deals with an approximation of static moduli in wells from dynamic moduli determined by acoustic well logging using T-matrix model. Proposed approach makes possible to determine moduli values, which are close to values of static moduli, which would be determined by loading tests. This approach is based on an idea, that an intact rock with sufficiently high compressional strength sc and sufficiently high value of static Young's modulus Es, manifests more or less linear elastic behaviour. In such case, the values of static and dynamic moduli are identical. This fact has been experimentally verified for rocks with values of sc and Es in order of higher tens of MPa and GPa respectively. In case of a rock damage presence in such rock, it's behaviour becomes nonlinearly elastic. The amount of nonlinearity is proportional to increasing amount of rock damage. This results in the difference between values of static and dynamic moduli. T-matrix model is used to quantify this difference. This model is based on an anisotropic rock matrix with ellipsoidal inclusions. These inclusions can affect each other. The result of this model calculation is a group of values of elastic constants, which we call effective moduli. These effective moduli include the effect of porosity in the rock as well and they...