Numerical Investigation of a Multiscale Mixed Finite Element Method

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Abstract/Contents

Abstract
Multiscale methods are currently under active investigation for reservoir simulation. Their main advantage is that they are able to represent the fine scale flow effects efficiently. A number of multiscale methods have been applied using various formulations (mixed finite element, finite volume and mimetic finite difference methods) but detailed sensitivity studies have not always been performed. In this thesis, a detailed numerical investigation of the multiscale mixed finite element (MsMFE) method is performed. We consider the effect of permeability orientation and anisotropy ratio on MsMFE accuracy for single-phase flows. The satisfaction of the discrete maximum principle by the pressure solution is also addressed in the context of single-phase flow. Local basis functions are used for these single-phase flow simulations. The performance of the MsMFE method is also assessed for two-phase flow simulations. The effect of different ways of computing and updating basis functions is addressed for realistic channelized reservoirs. Local-global basis functions are developed and tested and are shown to provide more accurate results in terms of pressure prediction compared to local basis functions. Overall, our results show that the global basis functions show somewhat higher accuracy compared to local basis functions and that updating of basis functions further increases the accuracy. Local-global basis functions appear promising though further investigation of this approach will be required.

Description

Type of resource text
Date created 2006

Creators/Contributors

Author Chaudhri, Masroor Mansoor
Primary advisor Durlofsky, Louis
Degree granting institution Stanford University, Department of Petroleum Engineering

Subjects

Subject School of Earth Energy & Environmental Sciences
Genre Thesis

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Preferred citation

Preferred Citation
Chaudhri, Masroor Mansoor. (2006). Numerical Investigation of a Multiscale Mixed Finite Element Method. Stanford Digital Repository. Available at: https://purl.stanford.edu/bk662pt1482

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Master's Theses, Doerr School of Sustainability

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