We propose a methodology that allows a fast, accurate, and unbiased interpretation of well data in terms of labelled discrete logs of stratigraphy at multiscale. These discrete logs can be used for: 1. advanced stratigraphy and facies analysis along the wells, 2. advanced subsurface interpretation: correlations between wells, and 3. 3D static model conditioning.

Two separate methods are proposed to provide an automatic stratigraphic zonation: 1. Automatic grain size trend interpretation from gamma ray logs, using UNet architecture, and 2. Automatic sedimentary geometry interpretation, from borehole images, using conventional neural network (CNN) and ResNet architectures. Both approaches are trained using synthetic and real data for optimal interpretation. These techniques have been combined into a workflow and validated on real data from the Wheatstone Field, Australia.


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