Data-Driven Discovery of Unconventional Shale Reservoir Dynamics

Abstract The need to deliver well-informed decisions within stringent production cycles in unconventional plays is motivating the quest for practical models that can assimilate increasing volumes of data and satisfactorily account for observed production trends. The present work introduces the 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 dataset from Vaca Muerta unconventional shale formation in Argentina. Given relatively moderate data requirements, we show that it is possible to attain a high level of predictability from hidden field state variables and well production data. 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.