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This study presents an automated methodology for clustering gamma ray (GR) log patterns to enhance reservoir characterization and geological interpretation. Key sand pattern features, such as sand thickness and pattern variability, are extracted from each GR log and utilized in a clustering process that integrates customized Self-Organizing Maps (SOM) and hierarchical clustering. The approach was validated using real well log data, demonstrating its effectiveness in revealing distinct GR patterns across the field and organizing them into well-defined clusters. By significantly improving the speed and accuracy of GR log interpretation, the proposed methodology offers valuable insights into depositional features and strengthens the connection between sedimentary patterns and petrophysical properties.