Automation of depth matching using a structured well-log database: Prototype well example in the North Sea

The Oil and Gas industry of today undergoes a digitalization process. This implies a massive effort to transform standard work procedures and workflows to be more efficient through the usage of machine learning and automation. Hence, new possibilities enabling geoscientists to explore and exploit vast amounts of data more efficiently for shorter turnaround times. For this reason, we proposed a pilot well log database in HDF5 (Hierarchical Data Format) format that can be continuously updated and provide versatility for data preparation to carry out further analysis. Similarly, through three different methods, we show automation of well-log depth matching that can be integrated and updated into the database, allowing the geoscientist to get full control of data availability, quality, and uncertainties throughout the workflow.