Multiscale Methods for Porous Media Flow Knut–Andreas Lie Vegard Kippe, Stein Krogstad, Jørg E. Aarnes SINTEF ICT, Dept. Applied Mathematics NSCM-18, November 28-29, 2005 Applied Mathematics NSCM-18 1/36
Multiscale Methods for Porous Media Flow
Knut–Andreas LieVegard Kippe, Stein Krogstad, Jørg E. Aarnes
SINTEF ICT, Dept. Applied Mathematics
NSCM-18, November 28-29, 2005
Applied Mathematics NSCM-18 1/36
Outline
1 Introduction to Reservoir SimulationFlow in Porous MediaUpscaling
2 Multiscale MethodsFrom Upscaling to Multiscale MethodsMultiscale Mixed Finite Elements
3 Numerical ExamplesMultiscale Methods versus UpscalingComputational ComplexityStrongly Heterogeneous StructuresFlexibility wrt Grids
4 Concluding Remarks
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Two-Phase Flow in Porous Media
Pressure equation:
−∇(K(x)λ(S)∇p) = q, v = −K(x)λ(S)∇p,
Fluid transport:
φ∂tS +∇ · (vf(S)) = ε∇(D(S,x)∇S
)Applied Mathematics NSCM-18 3/36
Flow in Porous Media, cont’d
Porous sandstones often have repetitive layered structures, butfaults and fractures caused by stresses in the rock disrupt flowpatterns1:
1Photo: Silje Søren Berg, CIPR, Univ. Bergen
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Scales in Porous Media Flow
The scales that impact fluid flow in oil reservoirs range from
the micrometer scale of pores and pore channels
via dm–m scale of well bores and laminae sediments
to sedimentary structures that stretch across entire reservoirs.
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Geological ModelsThe knowledge database in the oil company
Geomodels consist of geometry androck parameters (permeability K andporosity φ):
K spans many length scales andhas multiscale structure
maxK/minK ∼ 103–1010
Details on all scales impact flow
Gap between simulation models and geomodels:
High-resolution geomodels may have 107 − 109 cells
Conventional simulators are capable of about 105 − 106 cells
Traditional solution: upscaling of parameters
Applied Mathematics NSCM-18 6/36
Geological ModelsThe knowledge database in the oil company
Geomodels consist of geometry androck parameters (permeability K andporosity φ):
K spans many length scales andhas multiscale structure
maxK/minK ∼ 103–1010
Details on all scales impact flow
Gap between simulation models and geomodels:
High-resolution geomodels may have 107 − 109 cells
Conventional simulators are capable of about 105 − 106 cells
Traditional solution: upscaling of parameters
Applied Mathematics NSCM-18 6/36
Upscaling Geomodels
Upscaling a geomodel to acoarser simulation grid:
Combine cells to derivecoarse grid
Derive new efficient cellproperties
Fewer cells ⇒ fastersimulation/less storage
However:
Robust upscaling can bedifficult and work-intensive
⇑
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Upscaling the Pressure Equation
Assume that u satisfies theelliptic PDE:
−∇(a(x)∇u
)= f.
Upscaling amounts to finding anew field a∗(x) on a coarser gridsuch that
−∇(a∗(x)∇u∗
)= f ,
u∗ ∼ u, q∗ ∼ q .10 20 30 40 50 60
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Here the overbar denotes averaged quantities on a coarse grid.
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Upscaling the Pressure Equation, cont’d
How do we represent fine-scale heterogeneities on a coarse scale?
Arithmetic, geometric, harmonic, or power averaging(1|V |
∫V a(x)
p dx)1/p
Equivalent permeabilities ( a∗xx = −QxLx/∆Px )
V’
p=1 p=0
u=0
u=0
V
p=1
p=0
u=0 u=0V
V’
Lx
Ly
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State-of-the-art in Industry10th SPE Comparative Solution Project
Producer A
Producer B
Producer C
Producer D
Injector
Tarbert
UpperNess
Geomodel: 60× 220× 85 ≈ 1, 1 million grid cells
Simulation: 2000 days of production
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10th SPE Comparative Solution ProjectUpscaling results reported by industry
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Developing an Alternative to Upscaling
Observation:
Variations on small scale can have large impact on large-scaleflow patterns
We therefore seek a methodology which:
gives a detailed image of the flow pattern on the fine scale,without having to solve the full fine-scale system
is robust and flexible with respect to the coarse grid
is robust and flexible with respect to the fine grid and thefine-grid solver
is accurate and conservative
is fast and easy to parallelise
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From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓
⇑
Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓
⇑
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From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓
⇑
Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓
⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓ ⇑Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓
⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓ ⇑Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓
⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓ ⇑Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓
⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓ ⇑Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓
⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓ ⇑Building blocks:
Two-scale methodGeomodel:
⇓
⇑
Building blocks:
⇓ ⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods
Standard methodUpscaled model:
⇓ ⇑Building blocks:
Two-scale methodGeomodel:
⇓ ⇑Building blocks:
⇓ ⇑
Applied Mathematics NSCM-18 13/36
From Upscaling to Multiscale Methods, cont’d
1 Global upscaling methods (Nielsen, Holden, Tveito)
global boundary conditions, minimization of error functional
2 Local-global upscaling methods (Durlofsky et al.)
global boundary conditions + iterative improvement
3 Nested gridding (Gautier, Blunt & Christie)
Upscaling + local reconstruction of fine-scale velocities
4 Multiscale finite elements
basis functions with subscale resolutionfinite elements (Hou & Wu) – pressuremixed elements (Chen & Hou; Aarnes et al.) — velocityfinite volumes (Jenny et al.) — pressure
5 Variatonal multiscale methods (Hughes et al.; Arbogast;Larson & Malqvist; Juanes)
direct decomposition of the solution, V = Vc ⊕ Vf
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Multiscale Mixed Finite ElementsFormulation
Mixed formulation:
Find (v, p) ∈ H1,div0 × L2 such that∫
(λK)−1u · v dx−∫p∇ · u dx = 0, ∀u ∈ H1,div
0 ,∫`∇ · v dx =
∫q` dx, ∀` ∈ L2.
Multiscale discretisation:
Seek solutions in low-dimensional subspaces
Ums ⊂ H1,div0 and V ∈ L2,
where local fine-scale properties are incorporated into the basisfunctions.
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Multiscale Mixed Finite ElementsGrids and Basis Functions
We assume we are given a fine grid with permeability and porosityattached to each fine-grid block.
We construct a coarse grid, and choose the discretisation spaces Vand Ums such that:
For each coarse block Ti, there is a basis function φi ∈ V .
For each coarse edge Γij , there is a basis function ψij ∈ Ums.
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Multiscale Mixed Finite ElementsGrids and Basis Functions
We assume we are given a fine grid with permeability and porosityattached to each fine-grid block.
We construct a coarse grid, and choose the discretisation spaces Vand Ums such that:
For each coarse block Ti, there is a basis function φi ∈ V .
For each coarse edge Γij , there is a basis function ψij ∈ Ums.
Applied Mathematics NSCM-18 16/36
Multiscale Mixed Finite ElementsGrids and Basis Functions
We assume we are given a fine grid with permeability and porosityattached to each fine-grid block.
Ti
We construct a coarse grid, and choose the discretisation spaces Vand Ums such that:
For each coarse block Ti, there is a basis function φi ∈ V .
For each coarse edge Γij , there is a basis function ψij ∈ Ums.
Applied Mathematics NSCM-18 16/36
Multiscale Mixed Finite ElementsGrids and Basis Functions
We assume we are given a fine grid with permeability and porosityattached to each fine-grid block.
TiTj
We construct a coarse grid, and choose the discretisation spaces Vand Ums such that:
For each coarse block Ti, there is a basis function φi ∈ V .
For each coarse edge Γij , there is a basis function ψij ∈ Ums.
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Multiscale Mixed Finite ElementsBasis for the Velocity Field
For each coarse edge Γij , define a basisfunction
ψij : Ti ∪ Tj → R2
with unit flux through Γij and no flowacross ∂(Ti ∪ Tj).
Homogeneous medium Heterogeneous medium
We use ψij = −λK∇φij with
∇ · ψij =
{wi(x), for x ∈ Ti,
−wj(x), for x ∈ Tj ,
with boundary conditions ψij · n = 0 on ∂(Ti ∪ Tj).
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Multiscale Mixed Finite ElementsBasis for Velocity Field, cont’d
Homogeneous coefficients and rectangular support domain:basis function = lowest order Raviart-Thomas basis
MsMFEM = extension to cases with subscale variation incoefficients and non-rectangular support domain
Homogeneous medium Heterogeneous medium
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Multiscale Mixed Finite ElementsSummary
Velocity basis functions ψij
⇑
Geomodel
=⇒ Coarse-grid approximation space
⇓
Coarse-scale velocity
⇓
Fine-scale velocity
For the MsMFEM the fine-scale velocity field is a linearsuperposition of basis functions: v =
∑ij v
∗ijψij .
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Properties of the MsMFEM
Multiscale:Incorporates small-scale effects into coarse-scale solution
Conservative:Mass conservative on coarse grid and on the subgrid scale
Scalable:Well suited for parallel implementation since basis functions areprocessed independently
Flexible:No restrictions on subgrids and subgrid numerical method. Fewrestrictions on the shape of the coarse blocks
Fast:The method is fast when avoiding regeneration of (most of) thebasis functions at every time step
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Examples: AccuracySPE10 Revisited (5× 11× 17 Coarse Grid)
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ReferenceMsMFEM Nested Gridding
ReferenceMsMFEM Nested Gridding
ReferenceMsMFEM Nested Gridding
ReferenceMsMFEM Nested Gridding
Nested gridding: upscaling + downscaling
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Multiscale vs. UpscalingSPE10, Layer 85 (15× 55 Grid)
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reference (240× 880) MsMFEM MsFVM
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ALGU-NG pressure method harmonic-arithmetic
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Multiscale vs. UpscalingSaturation Errors on the Upscaled Grid
4 x 4 6 x 10 10 x 22 15 x 55 30 x 1100
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Multiscale vs. Upscaling/Downscaling
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reference (240× 880) MsMFEM
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MsFVM ALGU-NG
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Multiscale vs. UpscalingSaturation Errors on the Fine Grid
4 x 4 6 x 10 10 x 22 15 x 55 30 x 1100
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Computational ComplexityOrder of Magnitude Argument
Example: 3D (128x128x128), α = 1.2 and m = 3
8^3 16^3 32^3 64^30
0.5
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1.5
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4.5 x 108
Fine scale sol. ↓
MsFVMMsMFEMLGNGALGNG
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Computational ComplexityComments
Direct solution more efficient, so why bother with multiscale?
Full simulation: O(102) steps.
Basis functions need not be recomputed
2^3 4^3 8^3 16^3 32^3 64^3 128^30
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10 x 109
LocalGlobal
Also:
Possible to solve very large problems
Easy parallelization
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Strongly Heterogeneous Structures
Logarithm of kx
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−7
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kred = 104
kyellow = 1kblue = 10−8
Coarse grid = 8× 8.
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Reference
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MsMFEM
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Problem: Traversing Barriers
Problems occur when a basis function forces flow through a barrier:
Potential problem No problem
Problem-cases can be detected automatically through the indicator
υij = ψij · (λK)−1ψij .
If υij(x) > C for some x ∈ Ti, then split Ti, and generate basisfunctions for the new faces.
Applied Mathematics NSCM-18 30/36
Problem: Traversing Barriers
Problems occur when a basis function forces flow through a barrier:
Potential problem No problem
Problem-cases can be detected automatically through the indicator
υij = ψij · (λK)−1ψij .
If υij(x) > C for some x ∈ Ti, then split Ti, and generate basisfunctions for the new faces.
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Barrier Case, revisited
Reference Uniform coarse grid Non-uniform grid Barrier grid
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Flexibility wrt. GridsFracture Networks
2
2Courtesy of M. Karimi-Fard, Stanford
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Concluding Remarks
Upscaling is and will be an important part of the reservoirmodelling workflow
Multiscale methods may replace upscaling/downscaling forsimulation purposes, because they:
give better resolution
are more flexible
may be faster
However, a lot of (exciting) research needs to be done..
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