Produção Científica



Apresentação
07/11/2019

Petrophysical-hysteresis analysis related to pore structure of sedimentary rocks
Reservoir rocks characterization consists of a critical stage on petroleum reserves evaluation, in which determining the relationships between petrophysical and mechanical rock properties is a required step to guarantee the accuracy of delivered data. Mechanical properties affect both drilling programs and hydrocarbon exploitation. Rock texture also plays a significant role on reservoir characterization, because porosity and permeability depend, mainly, on geometrical parameters, such as grain size, sorting, shape and diagenetic induced changes caused by cementation and dissolution. This work consists on an evaluation of the hysteretic characteristics of American outcrops samples when submitted pore system to induced loading and unloading, analyzing the influence of chemical composition, depositional texture, pore system structure and elastic mechanical properties on its hysteretic behavior. The hysteresis areas were mathematically estimated and petrophysical properties measured, as well as mineralogical composition. Elastic properties were estimated for the dry sample and also considering only the solid rock framework. Interesting correlations were observed among the analyzed properties and the expectation is that, in the future, hysteresis phenomena can be considered on reservoir deployment monitoring.
Apresentação
07/11/2019

Using SPITS to Optimize the Cost of High-Performance Geophysics Processing on the Cloud
Public cloud providers, such as the Microsoft Azure and the Amazon Web Services (AWS), offer a wide variety of virtual machines, with different specifications, and prices, that are enabling users to run high-performance programs without buying specialized and expensive hardware. Moreover, some providers, such as AWS, allow the user to bid for lower cost virtual machines, called Spot Instances. Nonetheless, these machines may be terminated within a few minutes of warning at the provider discretion. In this work, we leveraged the SPITS programming model to implement a high-performance and fault-tolerant seismic processing application that is proper for execution on Spot instances and analyzed how different virtual machines from the AWS may affect the performance and the price of the computation. Our experimental results indicate that Spot instances have similar performance to regular instances but are roughly three times less expensive. Finally, we show that AWS groups virtual machines in Availability Zones and that selecting virtual machines from different zones may also affect the total execution cost.
Apresentação
07/11/2019

Microporosity Aspect Ratio of Carbonate Rocks Under Pressure
Carbonate rocks usually present a complex porous system, especially regarding the differences in pore geometry. When those rocks are submitted to external pressure variation, that variations in pore geometry may affect the elastic velocities. In this work, we studied the behavior of compressional and shear wave velocities in a set of carbonate rocks at different pressure stages. Our approach is based in a supposition of a dual porosity scenario and consisted in determining the mean value of the aspect ratio of the macro-meso pores through the analysis of optical microscopy images and use the Differential Effective Medium (DEM) theory to estimate the microporosity aspect ratio. The results showed that such microporosity aspect ratio decreases exponentially as pressure increases.
Apresentação
07/11/2019

Use of Bayesian Inference for Estimative of P-wave Arrival
The measurement of acoustic wave forms is present in exploration seismic, well logging and laboratory experiments. For instance, a commonly performed experiment for velocity measurements in cores is the first break picking. The measured data probably contain noise, either inherent to the measuring equipment or other factors. Develop new techniques to do this task automatically and more accurate is even more necessary over time.
Artigo em Revista
07/11/2019

Up/down acoustic wavefield decomposition using a single propagation and its application in reverse time migration
The separation of up- and downgoing wavefields is an important technique in the processing of multicomponent recorded data, propagating wavefields, and reverse time migration (RTM). Most of the previous methods for separating up/down propagating wavefields can be grouped according to their implementation strategy: a requirement to save time steps to perform Fourier transform over time or construction of the analytical wavefield through a solution of the wave equation twice (one for the source and another for the Hilbert-transformed source), in which both strategies have a high computational cost. For computing the analytical wavefield, we are proposing an alternative method based on the first-order partial equation in time and by just solving the wave equation once. Our strategy improves the computation of wavefield separation, and it can bring the causal imaging condition into practice. For time extrapolation, we are using the rapid expansion method to compute the wavefield and its first-order time derivative and then we can compute the analytical wavefield. By computing the analytical wavefield, we can, therefore, separate the wavefield into up- and downgoing components for each time step in an explicit way. Applications to synthetic models indicate that our method allows performing the wavefield decomposition similarly to the conventional method, as well as a potential application for the 3D case. For RTM applications, we can now use the causal imaging condition for several synthetic examples. Acoustic RTM up/down decomposition demonstrates that it can successfully remove the low-frequency noise, which is common in the typical crosscorrelation imaging condition, and it is usually removed by applying a Laplacian filter. Moreover, our method is efficient in terms of computational time when compared to RTM using an analytical wavefield computed by two propagations, and it is a little more costly than conventional RTM using the crosscorrelation imaging condition.
Tese de Doutorado
06/11/2019

Gustavo da Silva Costa. Análise de estabilidade e governabilidade em veículo submarino autônomo utilizando OpenFOAM
Gustavo da Silva Costa. Análise de estabilidade e governabilidade em veículo submarino autônomo utilizando OpenFOAM. 2019. SENAI/CIMATEC. Orientador: Ivan Costa da Cunha Lima.
Tese de Doutorado
06/11/2019

Florêncio Mendes Oliveira Filho. Modelagem com sinais de EEG: um nestudo com o coeficiente de correlação cruzada rho-DCCA
Florêncio Mendes Oliveira Filho. Modelagem com sinais de EEG: um nestudo com o coeficiente de correlação cruzada rho-DCCA. 2019. SENAI/CIMATEC. Orientador: Ivan Costa da Cunha Lima.
Tese de Doutorado
06/11/2019

Everaldo Freitas Guedes. Um modelo computacional para o monitoramento em tempo real da eficiênciae da interdependência do contágio entre mercados financeiros.
Everaldo Freitas Guedes. Um modelo computacional para o monitoramento em tempo real da eficiência e da interdependência do contágio entre mercados financeiros. 2019. SENAI/CIMATEC. Orientador: Ivan Costa da Cunha Lima.
Tese de Doutorado
06/11/2019

Fábio Rodrigues Santos. Análise espectral de padrões de turbulência e vorticidade em dutos gerados por simulação numéica utilizando OpenFOAM
Fábio Rodrigues Santos. Análise espectral de padrões de turbulência e vorticidade em dutos gerados por simulação numéica utilizando OpenFOAM. 2019. SENAI/CIMATEC. Oreintador: Ivan Costa da Cunha Lima.
Dissertação de Mestrado
06/11/2019

Alberto Ruiz Vieira de Melo Filho. Estudo de processo de injeção automotiva usando técnicas de simulação com OpenFOAM
Alberto Ruiz Vieira de Melo Filho. Estudo de processo de injeção automotiva usando técnicas de simulação com OpenFOAM. 2018. SENAI/CIMATEC. Orientador: Ivan Costa da Cunha Lima.
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