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Time-lapse imaging has been successfully applied by oil industry for reservoir monitoring along the years. Another relevant application using similar theoretical background although with different purposes, is to monitor CO 2 sequestration to detect possible leaks that may cause environmental impact. We propose to reduce the number of parameters and consequently to simplify the update surveys by two complementary key concepts: (1) keeping relevant information from previous surveys to be updated by a smaller new survey; (2) using adaptive trigonal mesh that is finer at predicted time-varying regions and coarser otherwise. To achieve our goals, we proposed an approach to integrate data from previous surveys joint with an adaptive trigonal mesh framework applied to diffraction tomography. We used these concepts to develop an inversion algorithm for CO 2 sequestration monitoring, which successfully imaged a synthetic model using VSP geometry.

Dynamic imaging provides an effective way to integrate previous surveys seismic data in order to estimate current velocity model. It is particularly useful for time-lapse imaging, which has been successfully applied to reservoir monitoring over the years by oil industry and developed for CO 2 sequestration monitoring more recently. This work aims to introduce a new dynamic imaging method for permanent acquisition systems applied to monitor CO 2 injection for detection of undesired leaks that may cause environmental impact. We propose a method called DynaSIRT that integrates previous surveys data in an efficient way, without reprocessing older data. The proposed method keeps state variables that store the temporally damped effective illumination of previous surveys and timestamps in order to track each parameter update. We successfully applied DynaSIRT to image a synthetic time-lapse diffraction tomography dataset, providing a clear detection of a CO 2 leakage even for sparse survey geometry, thus showing its relevance for CO 2 injection safety assessment.

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Redução de ordem de modelos monovariáveis, de sistemas lineares invariantes no tempo, contínuos, descritos por uma função de transferência racional, é uma técnica amplamente difundida na simplificação de modelos, como forma de reduzir complexidade envolvida em análise e projeto. Um método simples para redução de ordem baseado na minimização da norma dos coeficientes do numerador do polinômio de erro é proposto, e diversos resultados apresentados demonstram a validade e o mérito da abordagem.
Model order reduction for linear-time invariant systems, SISO, given by a rational transfer function, is a well-known technique for model simplification, as a way to reduce complexity involved in analysis and design. A simple method for model order reduction by norm minimization of the error polynomial numerator coefficients is proposed, and many presented results show the validity and quality of this approach

A method is described to approximate the 3D form and distribution of mineral dust (MD) aerosol particles based on digital in-line holographic imaging. The concept involves constructing a 3D geometrical hull of a particle defined by image-perimeter curves from a sequence of 2D images. Measuring holograms every ten milliseconds results in a video revealing the flow of the MD particles in 3D. Examples of two MD samples of different mean particle-size are presented.

Este artigo trata do desenvolvimento e utilização de um Analisador de Espectro Lock-in Digital para estimar a velocidade de um motor de indução trifásico, de maneira não-invasiva, por meio de aquisição e processamento de uma das correntes de fase, sem a necessidade de intervenção física direta no motor. Um Analisador de Espectro Lock-in Digital utiliza o princípio do Amplificador Lock-in para freqüências espaçadas uniformemente, o qual implementa detecção sensível à fase, usada para medição de sinal de freqüência específica em condições adversas de ruído.
This article discusses the development and utilization of a Digital Lock-in Spectrum Analyzer to estimate the speed of a three-phase induction motor in a less invasive manner. The estimation of speed is obtained using acquisition and processing of one phase-current, without direct physical intervention on the motor. The Digital Lock-in Spectrum Analyzer uses Lock-in Amplifier principle at equally spaced frequencies. The Lock-in Amplifier implements phase-sensitive detection applied to measurement of signals under severe noise conditions

This research aims at making optimal updates of geological models by jointly inverting flow and seismic data while honoring the geologic spatial continuity. Numerical models for reservoir characterization are increasing in complexity, due in part to the greater need to model the complex spatial heterogeneity and fluid flow in the subsurface. These models, once properly calibrated, can make better forecasts. This calibration process requires in essence the solving of an inverse problem. The inversion problem is formulated as minimizing the mismatch function between observations and the output of the numerical models. The optimal search is carried out by adjusting model parameters, typically one or more for each grid-point of the reservoir. The optimization problem is large-scale in nature, with a nonlinear and nonconvex objective function, that often involves time-expensive simulations. Additionally, this problem is generally ill-conditioned, because the number of degrees of freedom usually is larger than the number of observations. We present a robust and fairly efficient methodology to deal with these difficulties in the framework of oil reservoir characterization. The illconditioned character of the optimal search can be attenuated in two ways. By Principal Component Analysis (PCA) the search space can be projected to a subspace of much smaller dimension, while keeping consistency with prior spatial geological features already known for the reservoir. The number of optimal solutions can be reduced further by increasing the diversity of the data observed. We integrate two different types of data: time-lapse seismic (spatially distributed and of lower temporal periodicity) and production data (localized around wells and of high temporal periodicity). Production data provides an integrated response of the reservoir to fluid flow, while time-lapse seismic data yields a spatially distributed characterization of the changes in elastic velocities due to saturation and pressure variations. The reduction in the number of optimization variables by PCA allows the use of numerical derivatives of the cost function. Within a distributed computing framework these approximate derivatives can be calculated efficiently. We also consider derivative-free algorithms. We illustrate the methodology on a sector of the Stanford VI synthetic reservoir created for testing algorithms.