Conventional Reservoir Modelling
2
A justified educated guessing game:
Physical properties obtained from few wells
Data incomplete and variability completely unknown at smaller scales ( say < 50 m)
Interpolated in the inter-well volume using variogram-filtered krigging
No information available for accurate well placement
No information about reservoir heterogeneity measured or used
No information about reservoir anisotropy measured or used in modelling and simulation
Introduction AFRMs Generic Models Conditioned Models The Future
… in this presentation
How to make Advanced Fractal Reservoir Model (AFRMs)
Generic fractal modelling
The effect of heterogeneity on oil production data
The effect of anisotropy on oil production data
The effect of well placement and orientation
Conditioned fractal reservoir modelling
How to use AFRMs with real reservoirs
The Future
Creating flexible software for modelling
3 Introduction AFRMs Generic Models Conditioned Models The Future
4 Introduction AFRMs Generic Models Conditioned Models The Future
Scal
es a
nd
var
iab
ility
Interpolation of wells:
Only large scale variability is taken account of
Fractal Interpolation (FSMA):
Takes account of all data from the reservoir scale to the cell scale
Interpolation of wells with seismic input: Range of scales in inter-well volume extended to seismic resolution
5 Introduction AFRMs Generic Models Conditioned Models The Future
How is it possible to make fractal reservoir models which have:
o 3D
o controlled heterogeneity
o controlled anisotropy
o can be fully validated, and
o can be used to model poroperm curves
S
aud
Al-
Zai
nal
din
[Al-Zainaldin et al., 2017, Transport in Porous Media]
7 Introduction AFRMs Generic Models Conditioned Models The Future
Porosity (D= 3.1 | cxy=cyz=1)
Porosity (D= 3.5 | cxy=cyz=1)
Porosity (D= 3.9 | cxy=cyz=1)
Porosity map (D= 3.4 | cxy= 1 | cyz=1)
Porosity map (D= 3.4 | cxy= 1 | cyz=5)
Porosity map (D= 3.4 | cxy= 1 | cyz=3)
A Typical Finished AFRM
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All the input parameters needed for full simulation.
Fully specified, unique structure, repeatable model. Not stochastic.
Introduction AFRMs Generic Models Conditioned Models The Future
Porosity map
Grain size map
Poroperm cross-plot
Cementation exponent map
Permeability map
Water saturation map
Relperm curves
Permeability map (D = 3.5 | cxy= 1 | cyz=1)
What is the effect of:
o Heterogeneity
o Anisotropy
o Well Placement
o Orientation
on reservoir production from a model reservoir?
9 Introduction AFRMs Generic Models Conditioned Models The Future
S
aud
Al-
Zai
nal
din
, G
eorg
e D
anie
l
Methodology
10 Introduction AFRMs Generic Models Conditioned Models The Future
• Finite-difference Roxar Tempest® Black-Oil simulator (ver. 7.0.4)
• Anisotropy causes striping in the x-lateral direction
11 Introduction AFRMs Generic Models Conditioned Models The Future
Fractal capillary pressures and relperms Capillary pressure map (D = 3.1 | cxy= 1 | cyz=3)
Capillary pressure map (D = 3.5 | cxy= 1 | cyz=3)
Capillary pressure map (D = 3.9 | cxy= 1 | cyz=3)
12 Introduction AFRMs Generic Models Conditioned Models The Future
Heterogeneity & Anisotropy Oil Production Rates
Homogeneous reservoirs (low D) produce at a higher rate for longer than heterogeneous reservoirs (higher D)
Greater anisotropy c reduces production rate slightly at each time point
c = 1: Isotropic
c = 5: Anisotropic
13 Introduction AFRMs Generic Models Conditioned Models The Future
Homogeneous reservoirs (low D) keep the water cut lower for longer than heterogeneous reservoirs (higher D). Greater anisotropy c leads to earlier water breakthrough
c = 1: Isotropic
c = 5: Anisotropic
Heterogeneity & Anisotropy Water cut
14 Introduction AFRMs Generic Models Conditioned Models The Future
Tests done for: • 3 configurations of random well
placement • Combinations of Injectors I and/or
producers P in low or high permeability zones
• 1 to 10 producers and 1 to 5 Injectors
• For conventional and tight reservoirs
• Fractal Dimensions 3.1, 3.5 and 3.9 • Oil production profile, Oil recovery
factor, Water cut
Well placements I and P in high permeability (k>120 mD cut-off)
54 profiles, each with 15 curves
Well placement Near-homogeneous D=3.1
Heterogeneous D=3.9
Ra
te (
Ms
tb/d
ay)
Number of production wells
Number of production wells
AO
PR
(M
stb
/day
) A
OP
R (
Mst
b/d
ay)
Ra
te (
Ms
tb/d
ay)
AO
PR
(M
stb
/da
y)
AO
PR
(M
stb
/da
y)
Near-homogeneous D=3.1
Heterogeneous D=3.9
No. of production wells
No. of production wells
15 Introduction AFRMs Generic Models Conditioned Models The Future
1. AFRMs created with different heterogeneities and anisotropies
2. Define simple injector-producer well pattern
3. Rotate well pattern with respect to AFRM
Well orientation & anisotropy
4. Simulate major reservoir production parameters at each orientation
16 Introduction AFRMs Generic Models Conditioned Models The Future
Permeability anisotropy
Well Orientation and Anisotropy
Production rate becomes
isotropic by late production
17 Introduction AFRMs Generic Models Conditioned Models The Future
Well Orientation and Anisotropy
Increasing horizontal anisotropy is significant but not necessarily intuitive
Increasing vertical anisotropy is less significant
18 Introduction AFRMs Generic Models Conditioned Models The Future
Permeability anisotropy
Well Orientation and Anisotropy
Water cut, anisotropic in main production, becomes isotropic in late production
How can AFRMs be used to represent real reservoirs?
19 Introduction AFRMs Generic Models Conditioned Models The Future
H
assa
n A
l-R
amad
han
Methodology
20 Introduction AFRMs Generic Models Conditioned Models The Future
The fractal interpolation method allows data to be retained at all scales.
Fundamental differences
21 Introduction AFRMs Generic Models Conditioned Models The Future
Krigged interpolation Fractal interpolation
The fractal interpolation is NOT identical to the gold standard reference, BUT it contains information over the whole range of frequencies, WHEREAS the
conventional interpolation contains only long wavelength information.
22 Introduction AFRMs Generic Models Conditioned Models The Future
Conventional interpolation Fractal interpolation
D = 2.6
23 Introduction AFRMs Generic Models Conditioned Models The Future
Production Tests Simulated production using: • The 10K Gold standard reference • The conventional variogram
krigged interpolation • The new fractal interpolation
Results • Homogeneous D = 3.0: The new
fractal interpretation marginally better than the conventional interpolation
• Heterogeneous D = 3.5, 3.9: The
new fractal interpolation much better than the failing conventional method
24 Introduction AFRMs Generic Models Conditioned Models The Future
Interpretation The fractal interpolation mimics the reference model very well
It contains information over all wavelength scales
The conventional interpolation contains only long wavelength information
Consequently, production data is simulated from a fractal model of the reservoir
Putting it all together.
Thanks to the AFRM TEAM (Piroska Lorinczi, Saud Al-Zainaldin, George Daniel, Hassan Al-Ramadhan, Saddam Sinan, Mehdi Yaghoobpour)
The Future
26 Introduction AFRMs Generic Models Conditioned Models The Future
To do:
Create methods for deriving fractal dimensions and anisotropies from seismic attribute data
Synthesise all methodologies and code into a professional UOI
Run a number of case study scenarios in clastic, carbonate and unconventional reservoirs
Fractal Reservoir Modelling
27 Introduction AFRMs Generic Models Conditioned Models The Future
AFRMs can depict and control heterogeneity
AFRMs can depict and control xy, yz and zx anisotropy
AFRM accuracy can be verified
AFRM allows generic sensitivity tests for heterogeneity, anisotropy, well placement and orientation
AFRM is more accurate than conventional models in simulations
AFRM contains information at all scales larger than cell size
AFRMs can be fully reservoir-conditioned
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AFRMs are a powerful new approach to creating realistic 3D geological models of reservoirs for
simulation…
…giving insight into how heterogeneity and anisotropy affect reservoir production at all scales and capable of being conditioned to represent real
reservoirs.
Introduction AFRMs Generic Models Conditioned Models The Future
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