Modeling complex 3-D heterogeneities with the discrete smooth interpolation method

in: SEG Technical Program Expanded Abstracts, pages 166--169

Abstract

This paper presents a new method for generating and interpolating complex reservoir shapes in 3D. The method combines integration of quantitative knowledge about the shapes of the geologic bodies being modeled and generation of a large range of possible geometries corresponding to this knowledge. It is a new CAD approach based on the discrete smooth interpolation method (DSI). We apply this method to the geometric modeling of part of a complex turbiditic reservoir. In this example, channels and lobes account for most of the production, and their geometries are critical factors for oil recovery. Data include well logs, statistics about the shapes of main reservoir heterogeneities, and 3D templates representing the most likely shape. Geometric characterization of the sand bodies in this field is performed through stochastic modeling. The method used involves a Boolean approach based on indicator components and emphasizes the importance of honoring well data.

Download / Links

BibTeX Reference

@INPROCEEDINGS{wietzerbin:166,
    author = { Wietzerbin, L. and Mallet, Jean-Laurent },
     title = { Modeling complex 3-D heterogeneities with the discrete smooth interpolation method },
 booktitle = { SEG Technical Program Expanded Abstracts },
    volume = { 12 },
      year = { 1993 },
     pages = { 166--169 },
       doi = { 10.1190/1.1822427 },
  abstract = { This paper presents a new method for generating and interpolating complex reservoir shapes in 3D. The method combines integration of quantitative knowledge about the shapes of the geologic bodies being modeled and generation of a large range of possible geometries corresponding to this knowledge. It is a new CAD approach based on the discrete smooth interpolation method (DSI). We apply this method to the geometric modeling of part of a complex turbiditic reservoir. In this example, channels and lobes account for most of the production, and their geometries are critical factors for oil recovery. Data include well logs, statistics about the shapes of main reservoir heterogeneities, and 3D templates representing the most likely shape. Geometric characterization of the sand bodies in this field is performed through stochastic modeling. The method used involves a Boolean approach based on indicator components and emphasizes the importance of honoring well data. }
}