Papers by Guenther Schwedersky Neto
Geophysical Prospecting, Jan 8, 2021
This article has been accepted for publication and undergone full peer review but has not been th... more This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as
13th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 26–29 August 2013, 2013
This paper describes how a Bayesian framework can be modeled and applied on seismic data to estim... more This paper describes how a Bayesian framework can be modeled and applied on seismic data to estimate the wavelet. The method works on poststack and pre-stack data, in both, the convolutional forward model is considered, but it differ in how the reflectivity is calculated from the well log. We expanded the method to estimate the seismic noise correlation range jointly with the wavelet, the seismic noise level and uncertainties. The method is applied in synthetic and real post-stack seismic data. The Gaussian assumption for the likelihood models enables to obtain the analytical expressions for the conditioned distributions, which allows sampling from the posterior distribution via Gibbs Sampling Algorithm.
Proceedings, 2013
ABSTRACT One of the main challenge problems in geophysics is getting reliable seismic inverse mod... more ABSTRACT One of the main challenge problems in geophysics is getting reliable seismic inverse models while the uncertainty is assessed. Seismic inverse problems may be tackled in a probabilistic framework resulting in a set of equiprobable acoustic and elastic impedance models. Here we show a new geostatistical seismic AVO method from where density, Vp and Vs models are retrieved. With the resulting Earth models we also compute the correspondent synthetic pre-stack data and the zero-reflectivity R(0) and Gradient (G) models. We successfully applied this workflow to a 3D synthetic seismic dataset from where density, Vp and Vs models were known. The final best models achieved a global correlation between the original and the synthetic seismograms of about 0.80.
Geophysical Prospecting, Dec 26, 2017
ABSTRACTSeismic inversion plays an important role in reservoir modelling and characterisation due... more ABSTRACTSeismic inversion plays an important role in reservoir modelling and characterisation due to its potential for assessing the spatial distribution of the sub‐surface petro‐elastic properties. Seismic amplitude‐versus‐angle inversion methodologies allow to retrieve P‐wave and S‐wave velocities and density individually allowing a better characterisation of existing litho‐fluid facies. We present an iterative geostatistical seismic amplitude‐versus‐angle inversion algorithm that inverts pre‐stack seismic data, sorted by angle gather, directly for: density; P‐wave; and S‐wave velocity models. The proposed iterative geostatistical inverse procedure is based on the use of stochastic sequential simulation and co‐simulation algorithms as the perturbation technique of the model parametre space; and the use of a genetic algorithm as a global optimiser to make the simulated elastic models converge from iteration to iteration. All the elastic models simulated during the iterative procedure honour the marginal prior distributions of P‐wave velocity, S‐wave velocity and density estimated from the available well‐log data, and the corresponding joint distributions between density versus P‐wave velocity and P‐wave versus S‐wave velocity. We successfully tested and implemented the proposed inversion procedure on a pre‐stack synthetic dataset, built from a real reservoir, and on a real pre‐stack seismic dataset acquired over a deep‐water gas reservoir. In both cases the results show a good convergence between real and synthetic seismic and reliable high‐resolution elastic sub‐surface Earth models.
Geomechanics and Geoengineering, Apr 7, 2017
The present paper addresses the problem of factor of safety (FS) determination in 3D slope stabil... more The present paper addresses the problem of factor of safety (FS) determination in 3D slope stability analysis. For that purpose, use is made of two numerical methods/techniques in three benchmark problems: numerical limit analysis (NLA) and elasto-plastic analysis (EPA). Finite elements are used in both techniques. The primary objective of the study is, by comparing the two techniques, to establish the feasibility of using NLA in the evaluation of 3D slope stability problems and to establish its practical applicability and competitiveness in relation to EPA. Because of their geometry or their boundary conditions, the problems cannot be analysed as plane strain state problems or using the 2D limit equilibrium technique. In both methods, an FS is determined for the slopes by reducing the strength parameters of the geological materials using a scalar factor, known as the strength reduction factor. From the comparison of the FSs and of the computational efforts required for the two numerical techniques, it was possible to establish NLA's competiveness and viability for the analysis of real 3D slope stability problems when implemented in an efficient manner.
77th EAGE Conference and Exhibition 2015, 2015
We present a new iterative geostatistical seismic inversion algorithm that allows retrieving: den... more We present a new iterative geostatistical seismic inversion algorithm that allows retrieving: density; P-wave velocity; S-wave velocity and facies models. This novel procedure is based on two key main ideas: stochastic sequential simulation and co-simulation as the perturbation technique of the model parameter space; and a genetic algorithm that act as a global optimizer to converge the iterative procedure towards an objective function, the mismatch between recorded and synthetic pre-stack seismic data. At the end of each iteration, the triplet of elastic traces that jointly produce synthetic gathers with the highest correlation coefficient are the basis for generating the new elastic models of the next iteration. The iterative procedure finishes when the global correlation between recorded and inverted seismic data is above a certain threshold. All the elastic models simulated during the iterative procedure honor the marginal and joint distributions of P-wave velocity, S-wave velocity and density as estimated from the available well-log data. We successfully tested this new algorithm on a real pre-stack seismic dataset acquired over a deep-water turbidite oil reservoir. The results show a good convergence between real and synthetic seismic and the retrieved high resolution petro-elastic models agree with previous studies performed with commercial inversion algorithms.
Anuário do Instituto de Geociências, Jan 15, 2016
A maneira mais efetiva de se integrar o dado sísmico no processo de caracterização de reservatóri... more A maneira mais efetiva de se integrar o dado sísmico no processo de caracterização de reservatórios é por meio da geração de modelos de impedância derivados do processo de inversão sísmica. Neste trabalho foram comparados os resultados das inversões sísmicas determinística e geoestatística de um campo de petróleo, no intuito de melhorar a caracterização do campo e de gerar um modelo mais preciso, onde as previsões do comportamento do campo possam ser feitas de maneira mais efetiva. A inversão acústica determinística é uma técnica bastante utilizada, que gera um único resultado, invertendo o dado sísmico disponível para parâmetros acústicos (impedância P). Já a inversão acústica geoestatística gera múltiplos modelos de propriedades de reservatório (parâmetros acústicos, petrofísicos e litologia), todos equiprováveis, o que leva à possibilidade de se quantificar a incerteza em torno do modelo de reservatório que está sendo criado.
IEEE Transactions on Geoscience and Remote Sensing, Aug 1, 2017
Seismic inversion is an important technique for reservoir modeling and characterization due to it... more Seismic inversion is an important technique for reservoir modeling and characterization due to its potential in inferring the spatial distribution of the subsurface elastic properties of interest. Two of the most common seismic inversion methodologies within the oil and gas industry are iterative geostatistical seismic inversion and Bayesian linearized seismic inversion. Although the first technique is able to explore the uncertainty space related with the inverse solution in a more comprehensive way, it is also very computationally expensive compared with the Bayesian linearized approach. In this paper, we introduce a novel hybrid seismic inversion procedure that takes advantage of both the frameworks: an iterative geostatistical seismic inversion methodology is started from an initial guess model provided by a Bayesian inversion solution. Also, we propose a new approach to model the uncertainty of the retrieved inverse solution by means of kernel density estimation. The proposed approach is implemented in two different real data sets with different signal-to-noise ratios. The results show the robustness of the hybrid inverse methodology and the usefulness of modeling the uncertainty of the retrieved inverse solution. On board unit Iris recognition Data transfer Sea ice Ferrofluid Switched capacitor networks. Road side unit Cavity perturbation methods Fuel cells Sensitivity and specificity Breast biopsy ISDN Wireless access points Neuromuscular Atomic batteries. TEM cells Government policies Magnetic semiconductors Intracranial pressure sensors Materials reliability Optical planar waveguides Antibacterial activity Stability criteria Photonic band gap Iridium Facial muscles Adhesives Neuromuscular.
Modeling uncertainty in seismic inversion problems is a topic of interest for both the oil and ga... more Modeling uncertainty in seismic inversion problems is a topic of interest for both the oil and gas industry and the academia. Although recent advances in methodologies for sampling the posterior space of the petro-elastic properties of interest, integrating the a priori knowledge, they still have high computational cost. Global Stochastic Inversion, an iterative geostatistical seismic inversion methodology, stands out due to its spatial constraining capacity and a priori knowledge integration. However, it is very computationally expensive in searching the model parameter space. On the other hand, Bayesian Linearized Inversion procedures are fast if done trace-by-trace but it inefficient at spatial modeling, specifically when sampling the posterior distribution. This paper proposes a hybrid methodology to tackle the disadvantages of both inversion procedures. Experimental results using a real dataset suggests faster convergence and a better uncertainty modelling when applying the proposed methodology contrary to conventional Global Stochastic Inversion.
The optimization of the quantitative seismic data interpretation process is a key factor for rese... more The optimization of the quantitative seismic data interpretation process is a key factor for reservoir characterization and monitoring. In this paper, we show the results of a methodological investigation where Neural Network and Discriminant Analysis, two possible tools to incorporate the 4D seismic attributes in the quantification of saturation changes during the production of a reservoir, where analyzed. A special focus was applied to the evaluation of the impact of the quality and the amount of the calibration data to the final result, showing the pseudo-well technology as a good alternative to improve the process.
Rio Oil and Gas Expo and Conference
Rio Oil and Gas Expo and Conference, 2020
6th International Congress of the Brazilian Geophysical Society, 1999
6th International Congress of the Brazilian Geophysical Society, 1999
8th International Congress of the Brazilian Geophysical Society, 2003
5th International Congress of the Brazilian Geophysical Society, 1997
7th International Congress of the Brazilian Geophysical Society, 2001
Journal of Computational Physics, 2017
We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and... more We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
An important feature present in neural network models is their ability to learn from data, even w... more An important feature present in neural network models is their ability to learn from data, even when the user does not have much information about the particular dataset. However, the most popular models do not perform well in spatial interpolation problems due to their difficulty in accurately modeling spatial correlation between samples. On the other hand, one of the most important geostatistical methods for spatial interpolation, Kriging, performs very well but requires some expert knowledge to fit the correlation model (semivariogram). In this work, we adapt the Incremental Gaussian Mixture Network (IGMN) neural network model for spatial interpolation and geostatistical sequential simulation applications. Results show that our approach outperforms Multilayer Perceptron (MLP) and the original IGMN, especially in anisotropic and sparse datasets. Also, we propose an algorithm for Sequential Gaussian Simulation that uses IGMN instead of Kriging and can successfully generate equally probable realizations of the defined grid. To the best of our knowledge, this is the first time a Neural Network model is specialized for spatial interpolation applications and has the ability to perform a geostatistical simulation.
GEOPHYSICS, 2015
We have developed a new iterative geostatistical seismic amplitude variation with angle (AVA) inv... more We have developed a new iterative geostatistical seismic amplitude variation with angle (AVA) inversion algorithm that inverts prestack seismic data, sorted by angle gathers, directly for high-resolution density, P-wave velocity, S-wave velocity, and facies models. This novel iterative geostatistical inverse procedure is based on two key main principles: the use of stochastic sequential simulation and cosimulation as the perturbation technique of the model parameter space and a global optimizer based on a crossover genetic algorithm to converge the simulated earth models toward an objective function, in this case, the mismatch between the recorded and synthetic prestack seismic data. As a geostatistical approach, all the elastic models simulated during the iterative procedure honors the well-log data at its own locations, the marginal prior distributions of P-wave velocity and S-wave velocity, and density estimated from the available well-log data, and the corresponding joint distri...
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Papers by Guenther Schwedersky Neto