Data-Driven Prediction of Unconventional Shale-Reservoir Dynamics

Abstract

Summary The present work introduces extended dynamic mode decomposition (EDMD) as a suitable data-driven framework for learning the reservoir dynamics entailed by flow/fracture interactions in unconventional shales. The proposed EDMD approach builds on the approximation of infinite-dimensional linear operators combined with the power of deep learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single- and two-phase coupled flow/geomechanics simulations and a real production data set from the Vaca Muerta unconventional shale formation in Argentina. We demonstrated that we could attain a high level of predictability from unseen field-state variables and well-production data given relatively moderate input requirements. As the main conclusion of this work, EDMD stands as a promising data-driven choice for efficiently reconstructing flow/fracture dynamics that are either partially or entirely unknown, or that are too complex to formulate using known simulation tools on unconventional plays.