Produção Científica
Material Didático
| PrincÃpios da GeofÃsica: Sismologia, NovaTectônica Global, Geotermia. Em primeira edição, o livro é destinado a estudantes, professores e pesquisadores das áreas das geociências interessados nas aplicações do método sismológico nas escalas local e global, e com as naturais extensões à nova tectônica global e à geotermia. Os princÃpios teóricos básicos e fundamentais da sismologia na geofÃsica são descrito através das equações da fÃsica clássica nos domÃnios tempo-espaço, e nos respectivos domÃnios espectrais. O livro é formado de 9 densos capÃtulos que abrangem desde uma introdução sobre a história da geofÃsica, conceitos básicos sobre mecânica dos sólidos, teoria do raio, método da refração, o método da reflexão, modelagem sÃsmica de meios complexos, a nova tectônica global, e naturalmente tópicos da geotermia. Ao inÃcio dos capÃtulos o autor apresenta um breve histórico sobre o método geofÃsico em destaque, e ao final um resumo geral, seguido de exercÃcios teóricos e computacionais. Adicionalmente os livros apresentam 4 apêndices que tratam detalhes envolvendo: cálculo variacional e aplicações, métodos computacionais para a solução do problema direto e inverso relacionados aos capÃtulos do livro. |
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Artigo em Revista
| A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement ste |
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Artigo em Revista
| Deterministic and Stochastic Modeling in Prediction of Petrophysical Properties of an Albian Carbonate Reservoir in the Campos Basin (Southeastern Brazil) Permeability is one of the most significant and challenging parameters to estimate when characterizing an oil reservoir. Several empirical methods with geophysical borehole logs have been employed to estimate it indirectly. They include the Timur model, which uses conventional logs, and the Timur–Coates model, which uses the nuclear magnetic resonance log. The first goal of this study was to evaluate porosity, because it directly impacts permeability estimates. Deterministic and stochastic inversions were then carried out, as the main objective of this work was to estimate the permeability in a carbonate reservoir of the Campos Basin, Southeastern Brazil. The ridge regression scheme was used to invert the Timur and Timur–Coates equations deterministically. The stochastic inversion was later solved using fuzzy logic as the forward problem, and the Monte Carlo method was utilized to assess uncertainty. The goodness of fit for the estimations was all checked with porosity and permeability laboratory data using the Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Willmott’s agreement index (d). The results for the Timur model were R = 0.41; RMSE = 333.28; MAE = 95.56; and d = 0.55. These values were worse for the Timur–Coates model, with R = 0.39; RMSE = 355.28; MAE = 79.35; and d = 0.51. The Timur model with flow zones had R = 0.55; RMSE = 210.88; MAE = 116.66; and d = 0.84, which outperformed the other two models. The deterministic inversion showed, thus, little ability to adapt to the significant variations of the permeability values along the well, as can be seen from comparing these three approaches. However, the stochastic inversion using three bins had R = 0.35; RMSE = 320.27; MAE = 190.93; and d = 0.73, looking worse than the deterministic inversion. In the meantime, the stochastic inversion with six bins successfully adjusted the set of laboratory observations, because it provides R = 0.87; RMSE = 156.81; MAE = 74.60; and d = 0.92. This way, the last approach has proven it can produce a reliable solution with consistent parameters and an accurate permeability estimation. |
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Artigo em Revista
| Impacts of a New Drilling Fluid Invasion on Electrical Resistivity and Acoustic Velocity Measurements When mud filtrate invades a geological formation, italters its physicochemical characteristics. This invasive processdivides the formation into four zones: mud cake, flushed zone, andtransitional; immediately after that, there is a virgin area. Each ofthese zones contains a mixture of fluids: drilling fluids and originalformation fluids. Thus, all well-logging measurements areinfluenced by mud filtrate invasion. The goal of this research wasto analyze the effects of a new drilling fluid system invasion, crude-glycerin-based aqueous mud (CGBAM), when estimating a rock−fluid system’s physical properties through electrical and sonicproperties (Vp and Vs). To accomplish this objective, resistivity andtransit time laboratory measurements were performed on Bereasandstone samples under dry conditions and brine- and mudfiltrate-saturated conditions. According to the results, the invasion of this aforementioned mud filtrate did not affect thecharacterization of sandstones through sonic properties. Although the crude-glycerin-based aqueous mud has rheological behaviorlike that of an oil-based mud, it presented unusual behavior from an electrical resistivity and sonic point of view. CGBAM showedsonic properties like the water-based mud and resistive behavior between oil- and water-based muds; therefore, it improves thedrilling process and geophysical interpretation regarding the clear distinction between the oil and water zones of the reservoir. |
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Artigo em Revista
| Multiparameter least-squares reverse time migration using theviscoacoustic-wave equation In viscoacoustic least-squares reverse time migration methods, the reflectivity image associated with the Q factor is negligible, inverting only the velocity (v) parameter or v-related variables such as squared slowness or bulk modulus. However, the Q factor influences the amplitude and phase of the seismic data, especially in basins containing gas reservoirs or storing . Therefore, the Q factor and its associated parameters must be considered in the context of viscoacoustic least-squares reverse time migration. Thus, we propose a multiparameter viscoacoustic least-squares reverse time migration procedure, which obtains the inverse of bulk modulus (κ) and the Q magnitude (τ) simultaneously. We derive and implement the multiparameter forward and adjoint pair Born operators and the gradient formulas concerning κ and τ parameters. Then, we apply these derivations in our proposed multiparameter approach, which can produce images with better balanced amplitudes and more resolution than conventional reverse time migration images. |
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Artigo em Revista
| Inversão gravimétrica aplicada ao estudo do relevo do embasamentodo sistema Rift Recôncavo-Tucano-Jatobá no Nordeste do Brasil Este artigo apresenta resultados da implementação de um algoritmo demodelagem inversa que considera o sinal da anomalia Bouguer,para estimativa e melhor entendimento do limite entre o pacote sedimentar e o embasamento das sub-bacias do sistema rifte Recôncavo-Tucano-Jatobá.O algoritmo para a modelagem do relevo do embasamento, considerou um meio constituÃdo por um conjunto de prismas bidimensionais discretos, com fixos contrastesde densidadesentre sedimento e embasamento,e profundidades variáveis. Este, foi aplicadoinicialmente a modelos sintéticos, resultando em curvas de variações daprofundidade do topo do embasamento concordantes com o modelo verdadeiro. Em seguida, esta metodologia foi aplicada a dados gravimétricos de satélite obtidos a partir do modelo de campo gravitacional terrestre de alta resolução denominado SGG-UGM-2. Os resultados desta aplicação, foi comparado com a profundidade da interface Moho estimada abaixo das unidades estudadas e obtida através do uso da metodologia de Parker-Oldenburg. As informações aqui produzidaspermitiram a interpretação da anomalia gravimétrica sobre o espaço deposicional das bacias estudadas e a geometria do seu embasamento. Juntamente com o conhecimento de sua estratigrafia, esta interpretação poderáser analisada para caracterizaçãodosreservatórios da região. Ao interpretar o relevo do embasamento em conjunto com a topografia Moho, este estudo retrata a evolução geotectônica do aulacógeno e sua associação com a estimativa dos depocentros das sub-bacias Recôncavo, Tucano e Jatobá. |
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Artigo em Revista
| Gamma-ray spectrometry, magnetic and gravity signatures of Archean nuclei of the Borborema Province, Northeastern Brazil |
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Artigo em Revista
| Enhancement of Bayesian Seismic Inversion Using Machine Learning and Sparse Spike Wavelet: Case Study Norne Field Dataset The concept of uncertainty is fundamental in seismic inversion modeling, as it pertains to the imprecision or lack of certainty inherent in the model’s results. In this work, we present a comprehensive study that integrates machine learning (ML), sensitivity analysis on well data, and the sparse spike wavelet to enhance to quality of Bayesian linearized inversion (BLI). Furthermore, sensitivity analysis was conducted to assess diverse combinations of attributes to facilitate the identification of the optimal model configuration and ensure robust results. Subsequently, the wavelet, obtained through the application of the sparse spike convolution on seismic data, was used to further augment the quality of results in the BLI process inversion. This wavelet yields a more precise and efficient representation of seismic events, enabling an enhanced quality in the interpretation of data in the Bayesian context of seismic information. By integrating the sparse spike wavelet with the well and seismic data, a substantial improvement in inversion quality is achieved compared to the initial inversion performed using the original well data and the Ricker wavelet. The combination of ML, sensitivity analysis, and sparse spike wavelet in the BLI inversion process generates results that are more precise and reliable, facilitating well-informed decision-making in the exploration of underground resources. In our analysis, we applied the BLI inversion process in real data from the Norne Field in the North Sea, Norway. |
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Artigo em Revista
| Characterization of subaerial lava flows of the presalt strata in the Santos Basin based on basic well logs It is hard to identify volcanic morphologies using well logs to support technical decisions during well drilling and the acquisition of geological information. Therefore, this study was carried out on the volcanic sequence present in the Presalt interval of the Santos Basin, Brazil, which is referred to as the Camboriú Formation. This section was described in a previous study using image well logs as a succession of subaerial lava flows formed mainly by compound pahoehoes, sheet pahoehoes and rubbly pahoehoes. Based on sidewall core samples and petrophysical, geochemical and mineralogical reports, we identified five electrofacies from gamma ray, resistivity, sonic, density and neutron logs, representing patterns for identifying lava flows in subaerial sequences. In addition, we applied a porosity quantification method that resulted in a method for obtaining information from the matrix and presented coherent values in facies that did not present amygdalae or breccias and inferring altered zones from variations in resistivity and density logs or from cross-plots. Patterns of well log responses were defined for each type of lava flow present in the studied well, as was the inference about the most favorable facies for reservoirs. This methodology, which uses basic well logs from basalts, is unprecedented in the Presalt sequence and can be easily used to evaluate this exploratory frontier because of its close association with subaerial volcanism. |
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Artigo em Revista
| Cycle-consistent convolutional neural network for seismic impedance inversion: An application for high-resolution characterization of turbidites reservoirs. Acoustic impedance is a subsurface layer parameter crucial for reservoir characterization due to its relationship with petrophysical properties. Seismic impedance inversion is the routine conventionally used to calculate the acoustic impedance in 3D seismic datasets. Deep learning-based seismic inversion has recently gained attention due to its capacity to establish non-linear relationships between observed data and model parameters, producing robust acoustic impedance estimates. We employed a 1D cycle-consistent convolutional neural network (CNN) to perform the high-resolution seismic impedance inversion in the turbidite reservoirs of the Jubarte Field, Campos Basin, Brazil. The neural network was trained using geostatistics-based pseudo-wells with high pattern variability. Before applying the trained CNN, we performed the seismic data pre-conditioning to remove high and low-frequency noises affecting the data amplitudes, making the dataset more suitable for seismic impedance inversion. Our results show that the deep learning-based inversion produced a high-resolution estimate, allowing an accurate internal characterization of turbidite lobes. Quantitatively, the estimated average correlation coefficient in the eight wells evaluated in this study was 0.78. We observed that the pre-conditioning step was important for this application since the 1D architecture utilized could not deal properly with the noise as it disregards lateral connections. 2D and 3D networks may address this issue. Compared to the open-loop CNN and the traditional model-based inversion, the cycle-consistent network produced the best estimate, with good lateral continuity, vertical resolution, and correlation. We support that modern deep-learning architectures like the one presented can be efficiently integrated into reservoir characterization workflows for enhancing subsurface assessment. |
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