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Abstract

A methodology has been developed which is devoted to the integration of seismic information within the geological stochastic modeling workflow. Its aim is to optimize the characterization of fine-scale internal heterogeneities of the reservoirs. This workflow consists of three steps:

  • Lithoseismic interpretation and definition of a 3D seismic constraint,

  • Reconciliation of seismic data with the geological model,

  • Geostatistical geological modeling using a 3D seismic constraint

The result of the first step is a 3D volume of geological facies proportions at the seismic scale. It is described in detail in a separate companion paper.

The second step aims to improve the quantitative match of 3D seismic attributes computed directly from the detailed geological model. This implies first ensuring optimal coherency between well and seismic data. Secondly, distributions of acoustic parameters are determined for each geological facies using well log data. This information is used to obtain 3D synthetic cubes of seismic attributes, which are compared to real data. Finally, the 3D facies proportions are updated to minimize an objective function defined by the mismatch between real and synthetic seismic attributes.

In the third step, the non-stationary truncated Gaussian method is used to fill a fine grid geological model integrating a high resolution 3D seismic constraint and well information. The seismic constraint defines the main spatial trends of facies proportions, and the wells locally constrain facies in the model. This modeling process captures the geometry of complex geological objects and to reproduce the distributions of heterogeneities.

This methodology has been successfully applied to the Girassol Field. Comparisons between predicted and real impedance cubes clearly show the capability of the geological model to reproduce high-resolution seismic information. Some results of the geostatistical modeling are presented in this paper, as well as quality control tests and sensitivity analysis to critical parameters.

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