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A data curation process of large volumes of files can be a significant undertaking when adopting a manual approach. However, the integration of various AI/ML techniques, underpinned with subsurface taxonomy and state of the art data harmonization and enrichment pipeline guided by SMEs, enables a comprehensive end-to-end curation process for classifying, extracting and transforming a large volume of data in reasonable timeframes. Also, a rigorous QC workflow is designed to evaluate the extracted data and identifying data that meet predefined quality criteria. By implementing this QC process, we ensure data suitability for subsequent analysis, thereby enhancing the reliability of the results.