Use of linguistic petrographical descriptions to characterise core porosity: contrasting approaches

There are many classification problems in petroleum reservoir characterisation, an example being the recognition of lithofacies from well log data. Data classification is not an easy task when the data are not of numerical origin. This paper compares three approaches to classify porosity into groups (very poor, poor, fair, good) using petrographical characteristics described in linguistic terms. The three techniques used are an expert system approach, a supervised clustering approach, and a neural network approach. From the results applied to a core data set in Australia, we found that the techniques performed best in decreasing order of their requirement for significant user effort, for a low degree of benefit achieved thereby.