Abstract
Real-time reservoir management, optimization and uncertainty assessment requires an effective assimilation of all available data into multiple reservoir models to be able to generate accurate forecasts and in turn, fast and reliable decisions. This process entails thousands of complex simulations that generally require several days and weeks to be completed even with the most powerful supercomputer facilities available today. Consequently, there has been an increasing attempt to develop physics-based surrogate models that could eventually replace the job of high-fidelity simulations in an accurate and consistent way within optimization and uncertainty quantification workflows. The present work proposes a novel combination of physics-sound analytical growth models with data driven techniques to generate surrogate models that can replace or mitigate the computational burden entailed by intensive numerical simulations and thus, unlock fast and reliable forecasts to decrease the turn-around time within the field development decision-making process. The analytical model relies on the association of cumulative production curves with growth functions and diffusion phenomena that have been extensively used in the description of population and cell growth models in several branches of Life, Social and Economic Sciences. The use of growth function models enables description of production profiles from the complex interaction of main flow drivers across a connectivity network or continuous heterogeneous system. The data-driven component allows for discovering secondary physical flow or production trends that reside hidden in the data that in turn, may aid at complementing and extending the predictability of the whole surrogate model. The data-driven component relies on machine learning techniques to construct universal interpolators via radial basis function and/or artificial neural networks. Both analytical and data-driven approaches are conceived in a non-intrusive fashion; that is, neither an explicit knowledge of the equations governing the physics nor modification of flow simulation code is required. Hence, the proposed surrogate workflow can be realized from either field data or data generated from black-box commercial simulation software. The proposed class of non-intrusive physics-based surrogate models is promising for generation of moderate to long range forecasts for SAGD operations in oil sands as well as improving the reliability of forecasting and reserves estimation in unconventional resources. Demonstration of the proposed methodology is illustrated with a variety of forecasting scenarios and for predicting inflow performance relationships.