Produção Científica
Artigo em Revista
A combined Markov Chain Monte Carlo and Levenbergâ€“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 Levenbergâ€“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 Karhunenâ€“LoÃ¨ve (KL) expansion of secondorder 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 

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 Timurâ€“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 Timurâ€“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 Willmottâ€™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 Timurâ€“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. 

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 welllogging measurements areinfluenced by mud filtrate invasion. The goal of this research wasto analyze the effects of a new drilling fluid system invasion, crudeglycerinbased aqueous mud (CGBAM), when estimating a rockâˆ’fluid systemâ€™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 mudfiltratesaturated conditions. According to the results, the invasion of this aforementioned mud filtrate did not affect thecharacterization of sandstones through sonic properties. Although the crudeglycerinbased aqueous mud has rheological behaviorlike that of an oilbased mud, it presented unusual behavior from an electrical resistivity and sonic point of view. CGBAM showedsonic properties like the waterbased mud and resistive behavior between oil and waterbased muds; therefore, it improves thedrilling process and geophysical interpretation regarding the clear distinction between the oil and water zones of the reservoir. 

Artigo em Revista
Multiparameter leastsquares reverse time migration using theviscoacousticwave equation In viscoacoustic leastsquares reverse time migration methods, the reflectivity image associated with the Q factor is negligible, inverting only the velocity (v) parameter or vrelated 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 leastsquares reverse time migration. Thus, we propose a multiparameter viscoacoustic leastsquares reverse time migration procedure, which obtains the inverse of bulk modulus (Îº) and the Q magnitude (Ï„) simultaneously. We derive and implement the multiparameter forward and adjoint pair Born operators and the gradient formulas concerning Îº and Ï„ 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. 

Artigo em Revista
InversÃ£o gravimÃ©trica aplicada ao estudo do relevo do embasamentodo sistema Rift RecÃ´ncavoTucanoJatobÃ¡ no Nordeste do Brasil Este artigo apresenta resultados da implementaÃ§Ã£o 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 subbacias do sistema rifte RecÃ´ncavoTucanoJatobÃ¡.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 variÃ¡veis. Este, foi aplicadoinicialmente a modelos sintÃ©ticos, resultando em curvas de variaÃ§Ãµes daprofundidade do topo do embasamento concordantes com o modelo verdadeiro. Em seguida, esta metodologia foi aplicada a dados gravimÃ©tricos de satÃ©lite obtidos a partir do modelo de campo gravitacional terrestre de alta resoluÃ§Ã£o denominado SGGUGM2. Os resultados desta aplicaÃ§Ã£o, foi comparado com a profundidade da interface Moho estimada abaixo das unidades estudadas e obtida atravÃ©s do uso da metodologia de ParkerOldenburg. As informaÃ§Ãµes aqui produzidaspermitiram a interpretaÃ§Ã£o da anomalia gravimÃ©trica sobre o espaÃ§o deposicional das bacias estudadas e a geometria do seu embasamento. Juntamente com o conhecimento de sua estratigrafia, esta interpretaÃ§Ã£o poderÃ¡ser analisada para caracterizaÃ§Ã£odosreservatÃ³rios da regiÃ£o. Ao interpretar o relevo do embasamento em conjunto com a topografia Moho, este estudo retrata a evoluÃ§Ã£o geotectÃ´nica do aulacÃ³geno e sua associaÃ§Ã£o com a estimativa dos depocentros das subbacias RecÃ´ncavo, Tucano e JatobÃ¡. 

Artigo em Revista
Gammaray spectrometry, magnetic and gravity signatures of Archean nuclei of the Borborema Province, Northeastern Brazil 

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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 modelâ€™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 wellinformed decisionmaking 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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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 CamboriÃº 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 crossplots. 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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Cycleconsistent convolutional neural network for seismic impedance inversion: An application for highresolution 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 learningbased seismic inversion has recently gained attention due to its capacity to establish nonlinear relationships between observed data and model parameters, producing robust acoustic impedance estimates. We employed a 1D cycleconsistent convolutional neural network (CNN) to perform the highresolution seismic impedance inversion in the turbidite reservoirs of the Jubarte Field, Campos Basin, Brazil. The neural network was trained using geostatisticsbased pseudowells with high pattern variability. Before applying the trained CNN, we performed the seismic data preconditioning to remove high and lowfrequency noises affecting the data amplitudes, making the dataset more suitable for seismic impedance inversion. Our results show that the deep learningbased inversion produced a highresolution 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 preconditioning 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 openloop CNN and the traditional modelbased inversion, the cycleconsistent network produced the best estimate, with good lateral continuity, vertical resolution, and correlation. We support that modern deeplearning architectures like the one presented can be efficiently integrated into reservoir characterization workflows for enhancing subsurface assessment. 

Artigo em Revista
Magnetopolariton: Strong tilted magnetic field applied on confined electron gas in a wide Ga(1x)Al(x)As quantum well The coupling of an electron gas confined by a harmonic potential and submitted to a strong magnetic field tilted in relation to the confining direction creates an excitation called here magnetopolariton. This work describes the origin of this excitation and calculates the energy eigenvalues for different tilted angles. It presents the reasons leading to the crossings and repulsions of the energy bands, the degeneracy of the states, the way the Fermi levels change with the magnetic field, the influence of an external electric field, and how the Hall resistance plateaus change with the change in the tilted angle. 
