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Measurements
A) Geophysical Observation
B) Assign Local Physical Properties
Fluid Substitution and Upscaling
Acknowledgements
References
We thank the Stanford University Global Climate and Energy Project for funding this work. Steve Brown was funded by DOE SBIR grant number DE-FG02-05ER84207.
Garbozi, E.J., and J.G. Berryman, 2001. Elastic moduli of a material containing composite inclusions: Effective medium theory and finite element computations, Mechanics of Materials, 33, 455-470.
Torp, T.A., and J. Gale, 2004. Demonstrating storage of CO2 in geological reservoirs: The Sleipner and SACS projects, Energy, 29, 1361-1364.
Wang, Z., M.E. Cates, and R.T. Langan, 1998, Seismic Monitoring of a CO2 flood in a carbonate reservoir: A rock physics study. Geophysics, 63, 5, 1604-1617.
In order to simplify the rest of the analysis, we used cluster analysis of the physical properties measurements to identify petrophysical facies within the Berea block. The identified facies are then treated as distinct regions within the sample which have identical physical properties. For this example we simplified the Berea sample from a continuous distribution of properties to an assemblage of 5 petrophysical units.
CO2 saturation was then simulated by building a capillary
pressure model from the porosity and permeability distributions, and progressively saturating the sample with CO
2 and water as a function of pore pressure.
Paul Hagin1 and Stephen Brown2
1Department of Geophysics, Stanford University, Stanford, CA 94305-2215, *[email protected] 2NER, Inc., 331 Olcott Drive. White River Junction, VT, 05001
At Sleipner, CO2 is injected in the
Utsira Formation, a massive, high po-rosity sandstone aquifer with shaley interbeds.4-D seismic was selected for moni-toring and verification, with the hope of being able to track CO
2 dispersion.
While 4-D seismic successfully lo-cated CO
2 as it moved through the
aquifer, the volume or saturation of CO
2 at any given location was unde-
tectable (Torp and Gale, 2004).
At McElroy, a low-porosity carbonate is overlain by a higher-porosity car-bonate. Velocity changes following CO
2 are predicted to be <1%, and un-
detectable using 4-D seismic.However, the predicted changes in AVO parameters A and B are >10%, and easily detectable (Wang et al. 1998).
Defining the Problem
Proposing a Solution
The simple example from the McElroy Field shows that for very stiif rocks such as carbonates, the changes in velocity due to CO
2 injection are too small to be de-
tected, yet the changes in AVO response are significant.
Gassmann’s equations assume that fluid substitution in-volves perfectly mixed homogeneous fluids. However, the case of undissolved gas in water or oil represents a perfectly unmixed fluid, and results in patchy saturation. Our modeling suggests that dry frame heterogeneties can also cause patchy saturation.
Interestingly, laboratory measurements on Berea sand-stone, which is assumed to be homogeneous and iso-tropic, predict a saturation response which falls between the homogeneous and patchy end-members. For the case of supercritical CO
2, this results in an AVO re-
sponse that monotonically decreases with saturation.
Geophysical Observation: The geological in-jection target site is characterized using seismic, well-logs, core samples, and other sources of data.
Assign Local Physical Properties: Physical properties such as porosity, permeability, and stiffness are defined for each stratigraphic and flow unit (facies).
Fluid Substitution: The initial pore fluid satura-tion state is updated to reflect the saturation state following a given volume of injected CO
2, and the
associated change in physical properties is mod-eled.
Upscaling: Volume averaging of the local veloci-ties within individual petrophysical facies pro-duces a description at length scales correspond-ing to field seismic data.
AVO Calculation: the response of the principal interfaces can now be calculated.
Workflow for Prediction of AVO Response to variable CO2 Saturation
Standard geophysical methods for monitoring CO2 injection have severe
limitations in many cases. In particular, while the presence of CO2 can be
identified with seismic monitoring, it is difficult to quantify changes in CO2
saturation in most reservoirs. We hypothesize that time-lapse AVO may be more sensitive to changes in CO
2 saturation than traditional 4-D seismic.
We present an example using Berea sandstone that combines laboratory and numerical experiments to show that AVO can successfully track chang-es in CO
2 saturation. C) Fluid Substitution
GCEP Research Symposium 2007, Oct. 1-3, 2007
We have developed a multi-probe physical properties scanner, which allows for millimeter-scale mapping of petrophysical properties on a slabbed sample of rock or soil.
The scanner probe allows for the following physical properties to be measured: gas permeability, P- and S-wave velocities, and complex electrical impedance.
Using this device we have characterized the heterogeneity and anisotropy of a finely-bedded block of Berea sandstone. Berea sandstone has long been regarded as a labotory standard of homogeneous, isotropic behavior.
However, we find that permeability, resistivity, and velocity exhibit complex heterogeneity at a variety of scales, which we describe in the next section. Permeability varies by a factor of 3, velocity varies by ~10%, and resistivity varies by ~20%.
AVO Monitoring of CO2 Sequestration: A Benchtop Modeling StudyIntroduction
4-D seismic monitoring at Sleipner tracks the location of CO
2 in the
aquifer, but not changes in CO2
saturation. Saturation changes are currently modeled using a fluid flow simulator.
AutoScan II physical properties scanning table
Distribution of physical properties of Berea sandstone asmeasured in the laboratory. Using these data, we define the petrophysical faces through cluster analysis.
D) Upscaling
Gassmann fluid substitution for Berea sandstone as a function of CO2
saturation. Pore fluid properties were set a 3 different conditions, to simu-late CO
2 as both a gas and a supercritical liquid. The velocities have been
upscaled from the heterogeneous distribution described in B in two differ-ent ways. The first technique is based on simple volume averaging and is shown in the dashed lines. The second technique (solid lines) respects the anisotropy and heterogeneity in the sample, as described below.
We compute effective elastic properties of the Berea block at the scale of seismic resolution through a finite-element simulation. The advantage of this method is that the underlying spatial correlation of the heterogeneity is preserved as a general form of anisotropy in the upscaled elastic stiffness tensor.
We use the NIST elastic FEM program elas3d (Garboczi and Berryman, 2001) to compute the complete elastic stiffness tensor of the Berea block. This is done by applying strain boundary conditions and computing the cor-responding stresses. This is done in turn for a sequence of normal and shear strain boundary conditions to determine each component of the stiff-ness tensor. The results are restricted to the orthorhombic symmetry class to simplify the AVO calculations.
AVO Calculations
Sleipner Field
McElroy Field
0.05
0.055
0.06
0.065
0.07
0.075
0.08
0.085
0.09
0 5 10 15 20 25
Theta (degrees)
P-w
ave
Refl
ect
ion
Co
eff
icie
nt Pre CO_2 Flood
Post CO_2 Flood
Predicted AVO response of the McElroy Field following CO
2 injec-
tion equal to 60% saturation.
Rpp
(θ) = A + Bsin2θ + C[tan2θ + sin2θ]
Vp Vs ρ1 11
Vp Vs ρ2 2 2
θ
(A)
(C)
(E)
(D)
(B)
Geophysical Observation
Sw=0
Sw=1
Upscaling
Interpret Rock Types
Assign Local Physical Properties
Fluid Substitution
AVO Calculation
iVsiVp
ρi
While a large block sample of Berea sandstone looks relatively homogeneous, air permeability measurementsshow both vertical anisotropy and spatial heterogeneity.
Patchy Saturation End Member
Effect of Patchy Saturation
Vp/Vs
resistivity
velocity
permeability
Permeability189.032 704.706
Res
istiv
ity16
8.91
190.
407
Permeability189.032 704.706
Com
pres
sion
al V
eloc
ity21
49.2
924
47.3
8
Compressional Velocity2149.29 2447.38
Res
istiv
ity16
8.91
190.
407
Permeability189.032 704.706
Vp/V
s1.
4333
71.
4873
clusters
Conclusions
Methodology for predicting saturation from permeabilityand porosity measurements. Note that heterogeneities seen in permeability and porosity produce larger varia-bility in saturation.
Schematic representation of the strain boundary conditionsapplied to the Berea sandstone block in order to calculate the upscaled elastic stiffness tensor.
Initial State
AVO response for upscaled Berea sandstone assuming that CO
2 is a gas and the cap rock is a typical shale.
AVO response for upscaled Berea sandstone assuming that CO
2 is supercritical and the cap rock is a typical shale.