Evaldo Cesário Mundim

Comparing deterministic and stochastic seismic inversion for thin-bed reservoir characterization in a turbidite synthetic reference model of Campos Basin, Brazil

The inversion of seismic data for acoustic impedance is a frequently used technique because it offers several advantages:

Characterization of Thin Beds Through Joint Time-frequency Analysis Applied to a Turbidite Reservoir In Campos Basin, Brazil

A new spectral decomposition based method is presented here. We propose to use the ridges of the joint timefrequency analysis, as a new way to detect, in each trace, the maximum instantaneous frequencies and their associated amplitudes, which were applied as a tool to detect seismic geomorphologic bodies. The technique was applied to a synthesized wedge model and also to a thin bed offshore turbidite reservoir in Campos Basin, Brazil.

Factorial kriging analysis: Geostatistical filtering applied to reservoir characterization

This paper discusses factorial kriging analysis (FKA) for filtering seismic attributes. The great advantage of this technique is that the filter parameterization uses geologic knowledge about the field.

Application of Stochastic Inversion to Reservoir Characterization Process

Abstract To properly accomplish the reservoir characterization process in an effective way, it is necessary to integrate all the available information about the field in a consistent model. It is not an easy task to perform this integration in practice: it is necessary to use some specific methods such as seismic inversion. The seismic inversion is an efficient method to integrate both well-log and seismic data, obtaining a model that could be used in a forecasting process by numeric flow simulation. The seismic inversion can be achieved by several methods, which can be divided into two main groups: the deterministic methods (represented by the recursive inversion and constrained sparse-spike inversion) and the stochastic methods (represented by the geostatistical inversion). In this study, we show how stochastic inversion can improve the reservoir characterization process, by comparing its results with those obtained by deterministic inversion. The stochastic inversion can use a high sample rate that is close to the cell size of the reservoir model, implying that a more reliable model can be generated. The stochastic inversion method can also generate some basic statistics measurements that improve interpretations. Due to the great number of realizations generated during the process, we could also perform an uncertainty study on the reservoir model.

WTdecon, a colored deconvolution implemented by wavelet transform

This paper presents a new monochannel deconvolution technique with great potential to improve seismic resolution.

Application of seismic attribute filtering with Factorial Kriging to estimate porous volume: a case study on a Brazilian east coast offshore turbidite reservoir

The estimation of reservoir properties is a risky issue. Uncertainties come around; the number of wells (hard data) is often low and poorly distributed. Densely sampled, 3D seismic information (soft information) has been seen as a key to reducing interwell uncertainties. As it is well known, however, even after seismic processing, noises and sign distortions remain if not introduced by the processing itself. Moreover, inconsistency in horizons picking, during interpretation, worsens the quality of the obtained attributes with corresponding impact for its use as a predictive variable for reservoir mean properties estimation. In this paper, we discuss incorporation of seismic attributes filtered by Factorial Kriging via conditional simulation on reservoir porous volume estimation. The seismic attribute mean acoustic impedance obtained from a 3D seismic program with seismic stratigraphic inversion is considered. It shows itself to be well correlated to the porous thickness of the reservoir interval under consideration. A nested corregionalized model, which presented two structures, was fitted to the attribute. The small range associated to data noises was filtered by Factorial Kriging, which improved the correlation between the acoustic impedance and the porous thickness. The dispersion of the conditional simulation constrained by the filtered seismic attribute is lower than the dispersion of the conditional simulation constrained by the pre-filtering attribute, reducing the uncertainty of the reservoir porous volume estimation.

Seismically constrained reservoir quality modeling - an example in Eocenic turbidites of Campos Basin, Brazil.

Summary The integration of seismic data into the reservoir characterization process is very important in cases where the amount of well information is sparse. This is the case for most of the Campos Basin oil fields as well as for the field object of this study. Due to the large well spacing in this field, the use of seismic derived attributes to constrain the reservoir modeling is of paramount importance. For this case, the seismic amplitude, the most common seismic attribute used for reservoir characterization in Campos Basin, shows very weak correlation with the reservoir properties mainly due to strong impedance variations of the overlain beds. The alternative attribute chosen was the acoustic impedance derived from a post-stack seismic inversion. Such attribute showed better correlation with the reservoir properties of interest and it was then incorporated to the reservoir characterization process via geostatistical procedures. Stochastic simulation algorithms were used to generate facies and porosity models: the acoustic impedance was used as a soft constraint for the facies modeling and the facies distribution conditioned the porosity realizations. Based on the geological knowledge of the reservoir, some porosity models were selected to be the basis of the flow simulation model.