A New Look at Analyzing Petrographic Data: The Fuzzy Logic Approach

摘要

The common univariate analysis to evaluate the control of petrographic elements on permeability uses a quasi-quantitative approach.Relying on regression models,this analysis quantifies the behavior of permeability by isolating individual petrographic elements.Though the method does provide an overall picture of the petrographic control,it suffers one serious drawback.Its shortcoming lies in comparing the regression models with best correlation coefficients irrespective of the type of the curve fitCe.g.,quadratic versus logarithmic.This inequitable comparison forces the researcher to make qualitative judgments,based on intuition and experience,regarding the petrographic control.Various multivariate techniques have also been attempted;some as advanced as the Karhunen-LoPve transform that examines the covariance matrix and ranks the influence of each petrographic measurement on permeability.These methods tend to be mathematically complex and are not amenable to simple computer programming.In this paper,we present a simple fuzzy logic algorithm which accomplishes the ranking with relative ease.The algorithm uses non-boolean Areasoning@ to derive the simultaneous ranking of all the petrographic elements.The primary advantages of this algorithm are speed of processing and elimination of qualitative petrographic interpretations.Additionally,we demonstrate a novel thin section analysis technique which uses a minipermeameter,to increase the quantity and quality of petrographic data.The investigation volume of the minipermeameter and the proposed thin section analysis are comparable,unlike the larger measurement volume of a core plug.As a result,the measurementsCusing the new thin-section analysisCresult in more reliable correlations.The new method also conserves precious core material.The data collected with the new technique were used in our fuzzy logic analysis of two types of sandstones: the Queen and the Santa Rosa.Results from the conventional petrographic analysis and the fuzzy logic algorithm are in good agreement,while eliminating the individual bias and the tedious regressions associated with the conventional analysis.