Generative AI Applications for Subsurface Data Management

This technical report discusses the realistic applications of generative AI in managing subsurface data queries, particularly in the context of resource projects. It outlines the challenges faced due to unrealistic expectations of generative AI technologies in automatically locating critical business data. The report details the methodology employed, which includes machine learning and convolutional neural networks for metadata extraction from legacy subsurface documents. It emphasizes the importance of data quality management, standardization, and the integration of advanced data management tools. The report also presents a hybrid workflow that combines traditional machine learning with generative AI to enhance the efficiency of data retrieval processes. Observations indicate that while generative AI can significantly reduce the time required to locate essential information, it does not eliminate the need for established data management practices. The findings are supported by tests demonstrating improvements in data access and usability, particularly for projects related to energy and environmental sustainability.