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The primary goal of this paper is to improve accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. We demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. Our proposed workflow aims to address those issues and improve the accuracy of the estimations by clustering the input data into zones with specific geomechanical characteristics. We hypothesis each major zones at the offset test wells might have distinct "Normal Compaction Trend" with a different empirical constants. Thus, Bowers and Tau methods should be calibrated for each cluster rather the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. Several clustering techniques namely K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical were applied and compared to find the best algorithm. We found that the self-organizing map (SOM) method provide the best results by maximizing lithology likelihood within each cluster and improve the efficiency of the Bowers and Tau methods. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.
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