Classification of Geochemical and Petrophysical Data by Using Fuzzy Clustering

Abstract

In this study, the fuzzy c-mean clustering method was used in an unsupervised manner to automatically classify the different lithologies present at the Hillside prospect (Yorke Penninsula, SA). The algorithm was applied to various combinations of petrophysical and geochemical data to identify the combination that returned the most accurate result and the smallest combination that provides a nearly identical success as the best. We show that by using a combination of geochemical and petrophysical data the likelihood of a correct classification increases by 5% compared to analysing only geochemical data, and by over 20% compared to analysing only petrophysical data. However, using a few common elements and a few petrophysical values we can achieve almost the same success rate as the best result. Improvements in pre-treatment and conditioning of the data should allow the fuzzy cluster algorithm yield even better results. In addition to showing that combining petrophysical and elemental analysis is more robust, we demonstrate that if we could add some targeted elemental analysis to logging while drilling (LWD) then robust automated lithological logging becomes feasible.