... optimization framework employing model order reduction techniques in a closed-loop fashion. This paper proposes the use of proper orthogonal decomposition (POD) with the discrete empirical interpolation method (DEIM), to reduce the computational effort, and to perform local optimization by means of gradient...

... combines trajectory piecewise linearization (TPWL) and proper orthogonal decomposition (POD) to provide a highly efficient surrogate model. The compositional POD-TPWL method represents new solutions in terms of linearizations around states generated (and saved) during previously simulated ‘training’ runs...

... Abstract The present paper proposes a novel non-intrusive model reduction approach based on Proper Orthogonal Decomposition (POD), the Discrete Empirical Interpolation Method (DEIM) and Radial Basis Function (RBF) networks to efficiently predict production of oil and gas reservoirs. Provided...

... Abstract We developed and implemented a new first-stage preconditioning method for large-scale reservoir simulation as an alternative to the popular Algebraic Multi Grid (AMG) method. We used Proper Orthogonal Decomposition (POD) to derive a reduced-order model for the linearized pressure...

... state space is projected into a low-dimensional subspace using proper orthogonal decomposition (POD). The geological model is represented in terms of a Karhunen-Loeve expansion of the log-transmissibility field, so both the reservoir states and geological parameters are described in a very concise way...

... the full (high-fidelity) model, during which pressure and saturation states and Jacobian matrices are saved. These states and matrices are then projected into a low-dimensional space using proper orthogonal decomposition. Simulations in this reduced space can be performed very efficiently; in this work we...