Applying the Automated Depth-Shifting Workflow of Well Logging Data and Whole Core Images for Carbonate vs Clastic Rocks

This paper describes a novel approach for automatic depth shifting geophysical well logs data and whole core images. The proposed approach combines the use of machine learning algorithms for automatic lithotype description via whole core images and an amplitude modal inversion algorithm, enabling the integration of stratigraphic information during the shifting process. The automatic shifting with whole core images is performed without using the natural gamma radiation curve of drill core or core routine data. As part of this study, the proposed workflow was validated on clastic and carbonate datasets (including non-clayey carbonates rocks) for neutron and density logs. The automatic shifting results have high accuracy, with precision equal to that of the smoothing filter (approximately 60 cm for neutron logs and 80 cm for density logs.), according to the analysis of the processed data and comparison with core routine data. The proposed technique significantly reduces the time required for processing well logging data compared to traditional approaches, minimizes errors related to human factors, and can serve as a foundation for further research in this area.