Deep learning to de-risk reserve estimation

Abstract

Oil and gas companies evaluate the possibility of finding oil and gas fields carefully more than ever because it has been difficult to find gigantic discoveries which directly leads to their capital. Since a conventional evaluation contains human interpretation, luck and uncertainties, a variety of ranges of the reserves are often inferred from different interpreters given even identical dataset and conditions. As a consequence, there are differences between actual reserves and evaluated reserves. In this paper, using certain cases of how much actual reserves are deviated from interpreted reserves, deep learning is applied to mitigate such differences for unknown data which do not have actual reserves information. We find that our approach stably predicts the actual model by decreasing the misfit between the human and actual in comparison with the validation data on our workflow. The approach could be used to de-risk reserves estimation without changing traditional way of interpretations.