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Deep Learning methodologies are increasingly used to facilitate image analysis in various domains and raise growing interests in geology, as this discipline heavily relies on visual interpretation. However only few practical use cases on operational datasets are yet documented. Thus, in this work we appraise reference Deep Learning workflows to automate the interpretation of thin sections and core photographs from real-life geological studies. In a first appraisal, we use a dataset of carbonate thin sections from the Graus-Tremp basin (Spain) to assess object detection models. We train four Deep Learning models to automatically spot, delineate and characterize 9 different families of microfossils on these sections. The results are qualitatively assessed by human geologists, and precisions and inference times quantitatively measured. In a second appraisal, we use core samples from the Gulf of Corinth to evaluate the potential of supervised classification models in extrapolating human interpretation from a few segments to the entire wells. We carry out a Transfer Learning methodology to generate and compare multiple models with different neural architectures and training strategies. From this experience, we highlight good practices and recommendations for further use of Deep Learning technologies in similar contexts.