Classification Lithofacies Based on Petrophysics Properties and Clustering Algorithm in X Field

Abstract

Lithofacies classification is one of the key modelling components in reservoir characterization. Log-facies classification methods aim to estimate a profile of facies at the well location based on the values of rock properties measured or computed in well log analysis (such as density, porosity, P-Wave, shale content and mineralogy). In this study, the classification of lithofacies was carried out in X field. The first step of classification lithofacies is cross-plot of each petrophysical data, the result of this step is used as a priori data to statistical facies classification (k-means algorithm). Lithofacies in this study were successfully separated into two facies namely sand and shale. The results obtained show that X Field is a gas saturated with sandstone as the main reservoir, especially in the Plover formation.