Exploration Department

Due to the nature of seismic inversion problems, there are multiple possible solutions that can equally fit the observed seismic data while diverging from the real subsurface model. Consequently, it is important to assess how inverse-impedance models are converging toward the real subsurface model. For this purpose, we evaluated a new methodology to combine the multidimensional scaling (MDS) technique with an iterative geostatistical elastic seismic inversion algorithm. The geostatistical inversion algorithm inverted partial angle stacks directly for acoustic and elastic impedance (AI and EI) models. It was based on a genetic algorithm in which the model perturbation at each iteration was performed recurring to stochastic sequential simulation. To assess the reliability and convergence of the inverted models at each step, the simulated models can be projected in a metric space computed by MDS. This projection allowed distinguishing similar from variable models and assessing the convergence of inverted models toward the real impedance ones. The geostatistical inversion results of a synthetic data set, in which the real AI and EI models are known, were plotted in this metric space along with the known impedance models. We applied the same principle to a real data set using a crossvalidation technique. These examples revealed that the MDS is a valuable tool to evaluate the convergence of the inverse methodology and the impedance model variability among each iteration of the inversion process. Particularly for the geostatistical inversion algorithm we evaluated, it retrieves reliable impedance models while still producing a set of simulated models with considerable variability.

This letter presents ACOR-V , a new computationally efficient Ant Colony Optimization (ACO) based algorithm, tailored for continuous domain problems. The ACOR-V algorithm is well suited for application in seismic inversion problems owing to its intrinsic features, such as heuristics in generating the initial solution population and its facility to deal with multiobjective optimization problems. Here, we show how the ACOR-V algorithm can be applied in two methodologies to obtain 3D impedance maps from poststack seismic amplitude data. The first methodology pertains to the traditional method of forward convolution of a reflectivity model with the estimated wavelet, where ACOR-V is used to guess the appropriate wavelet as the reflectivity model. In the second methodology, we propose an even faster inversion algorithm based on inverse filter optimization, where ACOR-V optimizes the inverse filter that is deconvolved with the seismic traces and results in a reflectivity model similar to that found in well logs. This modeled inverse filter is then deconvolved with the entire 3D seismic volume. In experiments, both the methodologies are applied to a synthetic 3D seismic volume. The results validate their feasibility and the suitability of ACOR-V as an optimization algorithm. Results also show that the second methodology has the advantages of a much higher convergence speed and effectiveness as a seismic inversion tool.

Seismic impedance provides information on the relative proportion of different facies types. It is important to integrate such seismic data in the construction of detailed 3-D facies models, which are used for reservoir management. Two critical challenges faced in the integration of seismic impedance data: (1) the seismic data is at a larger scale than the well data / geological modeling cells, and (2) the seismic data provides soft (imprecise) information on the facies proportions within that large volume. A novel block cokriging approach was developed and implemented. This method was adapted for use in sequential indicator simulation to explicitly account for the large scale soft seismic data. Conventional sequential indicator simulation and a popular alternative, SIS with Bayesian updating, were considered for comparison purposes. The key challenge in applying stochastic simulation with block cokriging is the construction of a licit model of coregionalization between the "hard" well data and the "soft" seismic data. A hybrid procedure is presented that can be applied in the common case of limited well data. The Albacora field offshore Brazil consists of deep water turbidite sands, shales, and cemented sands. Good quality seismic data and significant variations in facies proportions make this an excellent example to illustrate the benefit of integrating seismic data in high resolution 3-D facies modeling.

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...

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.

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.

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.

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.

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