Volumetric deep learning techniques in oil & gas exploration

This work consisted in the study and application of volumetric Deep Learning (DL) approach to seismic data provided by Eni S.p.A., with an industrial utility perspective. After a series of fruitful meetings with the Upstream & Technical Services team, we clearly defined the final objective of this approach: the automatic search for geological structures such as turbidite channel-bases, as potential regions of interest for the Oil & Gas industry. Therefore, we defined a workflow based on the training of volumetric DL models over seismic horizons containing channel bases providing “windrose” input patches, i.e. a planar approximation of a three-dimensional volume. All components and sources of criticality were systematically analyzed. For this purpose we studied: the effect of preprocessing, the contribution of the dataset augmentation, the sensitivity for the channel-base manual segmentation, the effect of the spatial expansion of the input patches. Evaluating both qualitatively and quantitatively through K-fold cross-validation. This work showed: how an appropriate preprocessing of the original data substantially helps DL models, how the dataset augmentation is fundamental for good model generalization given the poor representativity of the accessible examples compared to all possible configurations, how this DL approach is susceptible to the channel-base segmentation imposing to invest sufficient effort in the generation of reliable labels, how the size of input patches must be large enough to allow models to perceive around each voxel the structure concavity and the texture of any sediment infill. We conclude that the volumetric DL approach developed in this work has proved to be very promising.