This paper presents the main results of an effort to integrate 2-D image analysis, geostatistics, 3-D computer reconstruction and network modeling for the purpose of creating a new tool for the evaluation of various transport and capillary properties of reservoir rock samples. The new approach utilizes statistical information, obtained from high resolution binary images of thin-sections, to create a 3-D representation of the porous microstructure using stochastic methods. The result is a 3-D model porous medium which honors the statistical properties measured in thin-sections of the real sample. Availability of such a model permits the determination of geometric and topological attributes of the 3-D microstructure which critically control the petrophysical properties of reservoir rocks. Prediction of various properties is then possible by incorporating information about the geometry (pore and throat size distributions) and connectivity (coordination number) of the pore space into network simulators. This paper outlines the main features of this new methodology and presents comparisons of model predictions of permeability, formation factor and resistivity index to experimental data for seven sandstone and carbonate samples from three different formations in Western Canada.


Rock properties of reservoir engineering interest, such as absolute and relative permeability, formation factor, resistivity index and capillary pressure curves are commonly determined by laboratory core analysis. It is common knowledge, however, that all of these properties are to a significant extent controlled by the complex geometry and topology of the pore space1. In this respect, proper interpretation of experimental results requires some knowledge of the microstructure. Most importantly, sufficiently accurate information on the connectivity and size distribution of pore space channels in reservoir rocks is required for the prediction of their properties using network models2. To date, such tools have found limited application as quantitative predictors, mainly because of the extreme difficulty in obtaining reliable geometric and topological descriptions of the porous microstructure3. Such information is, unfortunately, not accessible by 2-D measurements on sections through a porous medium4. This fact poses a severe limitation on existing methods for the prediction of petrophysical properties from 2-D pore space images5,6. Such methods cannot capture the 3-D connectivity of complex solid-void bi-continua and rely for their predictions on empirical correlations or extensive calibration with experimental data.

Recently, computer reconstruction of detailed pore structure data, obtained by serial sectioning of pore casts7,8 or magnetic resonance imaging9 (MRI) of rock samples, has been employed to determine the pore and throat size distributions and pore connectivity measures required by network simulators. To date, the best resolution achievable with MRI (ca. 10–20 µm) is not sufficiently high and serial sectioning remains an extremely laborious technique. Thus, neither method seems suitable for routine acquisition of detailed pore structure data.

Stochastic simulation of porous media in 3-D promises an attractive alternative to serial sectioning or tomographic imaging10. This technique utilizes statistical information, obtained by analyzing binary images of sections through a sample, to create a stochastic reconstruction of the porous medium in three dimensions. The main principle of stochastic simulation is that the model and real microstructures must have identical statistica

This content is only available via PDF.
You can access this article if you purchase or spend a download.