Abstract
Uncertainty and risk normally permeate our decisions on reservoirs characterization. A key to mitigate the uncertainties and ambiguities in our analysis is to use some methods that are more quantitative in lieu of the existing conventional one. The present study focuses on identifying first three zones of Asmari Formation as well as its cap rock (Mansuri oil field, south of Iran) from Petrophysical Logs (PLs) using three data-driven methods (i.e. Bayesian classifier, KNN classifier, and Parzen classifier). In term of Lithology, Asmari Formation mainly consists of carbonates and sandstones. In order to generate suitable input data, fluid substitution was performed using Gassmann equation. According to the results, KNN classifier is the best classifier for identifying zones of Asmari Formation. Generalization of the proposed method was also examined and the results of this stage were combined with the use of optimistic Ordered Weighted Average (OWA) technique. The employed fusion technique has improved the results of this step up to 7.6%.