Leveraging the Value of Legacy Sidewall Core Data

This study addresses the critical industry challenge of leveraging valuable but fragmented legacy sidewall core (SWC) data. With over 100,000 pages of disparate reports, this geological resource has been historically underutilized. We present a systematic digital transformation workflow utilizing Optical Character Recognition (OCR) and Natural Language Processing (NLP) to automatically extract, standardize, and validate data from multi-format documents, including handwritten records.

The results demonstrate a high-accuracy (98.4%) and efficient process, achieving a 60% reduction in data retrieval time and a 40% reduction in processing time. This successfully converts legacy data into a structured, unified database. The key innovation is making this historical information instantly accessible and reliable, enabling its integration with other subsurface datasets and advanced applications like machine learning. This work unlocks the full potential of legacy SWC data, transforming it into a vital asset for enhanced reservoir characterization, informed operational decision-making, and future exploration activities.