Geostatistical classification and prediction of lithology is an important step in reservoir characterization. By predicting rock facies with geostatistical analysis of well logs, especially in intervals that have not been cored, overall data acquisition costs, project cycle times, project costs, and exploration costs could be reduced. Additionally, lithology identification adds a control factor for the relationship between permeability and porosity. In this case study, partitioning algorithms Partitioning Around Mediods (PAM) and K-means Cluster Analysis were utilized to analyze well log data in R-statistical computing language, for one well in Forest Hill oil field in the East Texas Basin. The output from K-means Cluster Analysis indicated that three clusters were the ideal groupings of the input data, which aligned with the known stratigraphy of the area of interest for this case study. Each cluster was then assigned a lithological classification based on geologic literature of the East Texas Basin. These classifications were compared to the original well log data and well log interpretations from Interactive Petrophysics software to verify the accuracy of the clustering analysis and the vertical sequence of lithology in the well.


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