The use of statistical communication theory to characterize porous media.

Abstract

It is known that the static and dynamic behavior of fluids in porous media depends to a large measure on porousmedia geometry. In the past,. the ability to characterize this geometry has been restricted to such average properties as porosity and permeability. However, in recent years attempts have been made to achieve a more precise charac- terization based upon the fact that porous media is statistically composed. In this thesis techniques from statistical 'communi-' cation theory are adapted as possible methods for accomplish' ing this classification process. In simplest form, a dichotamous function is defined by passing a. line through a porous medium, the function having one value when the' line is in solid matrix, and another value when the line passes through pore space. The function is then analyzed using (1) Classical Fourier series harmonic analysis, and (2) determination of the autocovariance estimate and power spectrum. Comparisons are made between seyeral functions created from the same medium, and with functions created from other media. The results indicate that the autocovariance estimate * and the power spectrum, as characterizing functions, can discriminate between different media. This success suggests many more paths of investigation, possibly leading to the complete characterization of porous media through statistical analysis.