Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
2003-03-06
2004-11-23
Thomson, William (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C703S010000, C703S009000, C703S006000, C702S013000, C702S012000, C702S006000, C367S069000, C367S072000
Reexamination Certificate
active
06823297
ABSTRACT:
TECHNICAL FIELD
The present invention relates generally to subsurface reservoir simulators, and more particularly, to those simulators which use multi-scale physics to simulate flow in an underground reservoir.
BACKGROUND OF THE INVENTION
The level of detail available in reservoir description often exceeds the computational capability of existing reservoir simulators. This resolution gap is usually tackled by upscaling the fine-scale description to sizes that can be treated by a full-featured simulator. In upscaling, the original model is coarsened using a computationally inexpensive process. In flow-based methods, the process is based on single-phase flow. A simulation study is then performed using the coarsened model. Upscaling methods such as these have proven to be quite successful. However, it is not possible to have a priori estimates of the errors that are present when complex flow processes are investigated using coarse models constructed via these simplified settings.
Various fundamentally different multi-scale approaches for flow in porous media have been proposed to accommodate the fine-scale description directly. As opposed to upscaling, the multi-scale approach targets the full problem with the original resolution. The upscaling methodology is typically based on resolving the length and time-scales of interest by maximizing local operations. Arbogast et al. (T. Arbogast,
Numerical subgrid upscaling of two phase flow in porous media
, Technical report, Texas Institute for Computational and Applied Mathematics, The University of Texas at Austin, 1999, and T. Arbogast and S. L. Bryant,
Numerical subgrid upscaling for waterflood simulations,
SPE 66375, 2001) presented a mixed finite-element method where fine-scale effects are localized by a boundary condition assumption at the coarse element boundaries. Then the small-scale influence is coupled with the coarsescale effects by numerical Greens functions. Hou and Wu (T. Hou and X. H. Wu,
A multiscale finite element method for elliptic problems in composite materials and porous media,
J. Comp. Phys., 134:169-189, 1997) employed a finite-element approach and constructed specific basis functions which capture the small scales. Again, localization is achieved by boundary condition assumptions for the coarse elements. To reduce the effects of these boundary conditions, an oversampling technique can be applied. Chen and Hou (Z. Chen and T. Y. Hou,
A mixed finite element method for elliptic problems with rapidly oscillating coefficients,
Math. Comput., June 2002) utilized these ideas in combination with a mixed finite-element approach. Another approach by Beckie et al. (R. Beckie, A. A. Aldama, and E. F. Wood,
Modeling the large-scale dynamics of saturated groundwater flow using spatial filtering,
Water Resources Research, 32:1269-1280, 1996) is based on large eddy simulation (LES) techniques which are commonly used for turbulence modeling.
Lee et al. (S. H. Lee, L. J. Durlofsky, M. F. Lough, and W. H. Chen,
Finite difference simulation of geologically complex reservoirs with tensor permeabilities,
SPERE&E, pages 567-574, 1998) developed a flux-continuous finite-difference (FCFD) scheme for 2D models. Lee et al. further developed a method to address 3D models (S. H. Lee, H. Tchelepi, P. Jenny and L. Dechant,
Implementation of a flux continuous finite-difference method for stratigraphic, hexahedron grids,
SPE Journal, September, pages 269-277, 2002). Jenny et al. (P. Jenny, C. Wolfsteiner, S. H. Lee and L. J. Durlofsky,
Modeling flow in geometrically complex reservoirs using hexahedral multi-block grids,
SPE Journal, June, pages 149-157, 2002) later implemented this scheme in a multi-block simulator.
In light of the above modeling efforts, there is a need for a simulation method which more efficiently captures the effects of small scales on a coarse grid. Ideally, the method would be conservative and also treat tensor permeabilities correctly. Further, preferably the reconstructed fine-scale solution would satisfy the proper mass balance on the fine-scale. The present invention provides such a simulation method.
SUMMARY OF THE INVENTION
A multi-scale finite-volume (MSFV) approach is taught for solving elliptic or parabolic problems such as those found in subsurface flow simulators. Advantages of the present MSFV method are that it fits nicely into a finite-volume framework, it allows for computing effective coarse-scale transmissibilities, treats tensor permeabilities properly, and is conservative at both the coarse and fine scales. The present method is computationally efficient relative to reservoir simulation now in use and is well suited for massive parallel computation. The present invention can be applied to 3D unstructured grids and also to multi-phase flow. Further, the reconstructed fine-scale solution satisfies the proper mass balance on the fine-scale.
A multi-scale approach is described which results in effective transmissibilities for the coarse-scale problem. Once the transmissibilities are constructed, the MSFV method uses a finite-volume scheme employing multi-point stencils for flux discretization. The approach is conservative and treats tensor permeabilities correctly. This method is easily applied using existing finite-volume codes, and once the transmissibilities are computed, the method is computationally very efficient. In computing the effective transmissibilities, closure assumptions are employed.
A significant characteristic of the present multi-scale method is that two sets of basis functions are employed. A first set of dual basis functions is computed to construct transmissibilities between coarse cells. A second set of locally computed fine scale basis functions is utilized to reconstruct a fine-scale velocity field from a coarse scale solution. This second set of fine-scale basis functions is designed such that the reconstructed fine-scale velocity solution is fully consistent with the transmissibilities. Further, the solution satisfies the proper mass balance on the small scale.
The MSFV method may be used in modeling a subsurface reservoir. A fine grid is first created defining a plurality of fine cells. A permeability field and other fine scale properties are associated with the fine cells. Next, a coarse grid is created which defines a plurality of coarse cells having interfaces between the coarse cells. The coarse cells are ideally aggregates of the fine cells. A dual coarse grid is constructed defining a plurality of dual coarse control volumes. The dual coarse control volumes are ideally also aggregates of the fine cells. Boundaries surround the dual coarse control volumes.
Dual basis functions are then calculated on the dual coarse control volumes by solving local elliptic or parabolic problems, preferably using boundary conditions obtained from solving reduced problems along the interfaces of the course cells. Fluxes, preferably integral fluxes, are then extracted across the interfaces of the coarse cells from the dual basis functions. These fluxes are assembled to obtain effective transmissibilities between coarse cells of the coarse cell grid. The transmissibilities can be used for coarse scale finite volume calculations.
A fine scale velocity field may be established. A finite volume method is used to calculate pressures in the coarse cells utilizing the transmissibilities between cells. Fine scale basis functions are computed by solving local elliptic or parabolic flow problems on the coarse cells and by utilizing fine scale fluxes across the interfaces of the coarse cells which are extracted from the dual basis functions. Finally, the fine-scale basis functions and the corresponding coarse cell pressures are combined to extract the small scale velocity field.
A transport problem may be solved on the fine grid by using the small scale velocity field. Ideally, the transport problem is solved iteratively in two stages. In the first stage, a fine scale velocity field is obtained from solving a pressure equation. In the second stage, the transport problem is solved on the f
Jenny Patrick
Lee Seong
Tchelepi Hamdi A.
Chevron U.S.A. Inc.
Schulte Richard J.
Thomson William
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