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Thursday, March 5 • 15:15 - 17:15
Poster: 'Model Order Reduction in Porous Media Flow Optimizations,' Mohammadreza Ghasemi, Texas A&M University

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The numerical simulation of high-fidelity oil and gas reservoir models is a challenging task in computationally intensive frameworks, such as history matching, optimization and uncertainty quantification. There are many methods to alleviate this issue, such as the use of high performance computing (HPC), approximations using proxy models and surrogate modeling, to name a few. Here, we take a more rigorous mathematical approximation method using concepts from system theory, where the large scale model is replaced by a reduced complexity model with a much lower dimension and reasonable accuracy. The main benefit of going through this approximation is that computational costs for simulating the complex large-scale model can be improved several orders of magnitudes.  In this poster, I will give an overview of the model order reduction (MOR) and discuss recent and ongoing efforts to develop new techniques in applying them to porous media flow simulation and optimization. These methods are based on proper orthogonal decomposition. In particular, to reduce the computational complexity of the underlying nonlinear partial differential equations, one needs to find a way to reduce the dimension of the nonlinear terms. Here I specifically consider discrete empirical interpolation methods, trajectory piece wise linearization, and bilinear quadratic formulation. These approaches allow achieving the computational cost that is independent of the fine grid dimension.  In addition, we will discuss how MOR can accelerate the production optimization workflows. We will demonstrate these ideas by applying MOR techniques to a benchmark model of an offshore reservoir with large number of producers, injectors, and control variables that need to be adjusted during the optimization process. The results will be compared with the outputs of a high fidelity model in terms of number iterations, computational time, and the improvement in NPV. Coupling MOR and HPC is also suggested for improving the results. 


Reza Ghasemi

Reservoir Engineer, Stone Ridge Technology

Thursday March 5, 2015 15:15 - 17:15 CST
BioScience Research Collaborative 6500 Main Street, Houston, Tx 77005