Geological Metric Space Description by SVM Classification - Turbidite Reservoir Case Study with Multiple Training Images

This paper shows the challenges related to handling multiple training images for reservoir prediction. We have identified two of the main challenges in handling multiple geological scenarios by creating a lower dimensional representation of the ensemble of model realizations: (i) how to relate geological knowledge to the metric space; and (ii) how to navigate in the metric space to facilitate in model update. In this work we demonstrate how to solve the classification problem in the metric space accounting for geological knowledge from a variety of prior geological concepts. In this paper we established geological relations in the metric space by making the links to the space of geologically interpretable parameters. These results would allow us to enhance geological realism of the new models obtained through the update process in the metric space.