Microseismic Monitoring and Geomechanical Modelling of CO2 - bris

CHAPTER 6.
GENERATING ANISOTROPIC SEISMIC MODELS BASED ON GEOMECHANICAL SIMULATION
mica content, and show a 0 greater than the global trend. Aspect ratio also appears to increase with
increasing clay and mica content from 1784 to 1788. This would appear to confirm that increasing
clay and mica content correlates with larger values of a 0 .
In order to understand this relationship, it is helpful to consider the micro-structure for clean
and clay-rich samples. Figure 6.13 shows back-scatter electron micrograph (BSEM) pictures for the
clean sandstones 1909 and 1950, and for the clay-rich sample 1784. The clean samples show random
orientation of quartz and feldspar, with a random orientation of the diagenetic calcite (Valcke et al.,
2006). The clay-rich sample shows a preferred orientation of the mica and clay grains with vertically
aligned rotational symmetry due to compaction. Figure 6.13 highlights the dominance of the mica and
clay, which is also noted in the ultrasonic data, where a strong VTI symmetry is observed. It is not
clear whether this strong lithological anisotropy has a significant influence on aspect ratio estimates.
For instance, does this initial VTI skew the inversion estimates, or does the presence of significant
amounts of mica and clay lead to an inherent micro-structural bias of larger aspect ratios It is
difficult with such limited data to conclude with any certainty that it is the presence of clay particles
alone that causes aspect ratios to increase. More velocity-stress data for shales and shaley (clay rich)
sandstones, with accompanying petrophysical analyses, are necessary.
6.5 Comparison of rock physics models
So far I have outlined two rock physics models, the third-order elasticity approach of Prioul et al.
(2004), and the approach developed in this thesis and Verdon et al. (2008). Other models available but
only touched on briefly in this chapter are the R-factor (Hatchell and Bourne, 2005), and models based
entirely on empirical calibration (e.g., Minkoff et al., 2004). Each model has particular advantages and
issues, and each requires its own assumptions. Therefore each has its role, or niche, in the geophysicists
toolbox.
The simplest method for dealing with stress sensitive velocities is to use an empirically defined
relationship. Cores samples are taken from the reservoir, and velocities measured at the stresses of
interest. The advantage of such an approach is that no assumptions need to be made about the
physics controlling stress-sensitive seismic velocities. However, such an approach will be limited in its
validity to the parameter space tested in experiments. In a real scenario, the triaxial stress tensor
will vary continuously across a reservoir and overburden, and will vary through time as a result of
production. Therefore it is unfeasible to conduct experiments for every stress state. Models are needed
to extrapolate from experiments to the stress condition at each point in the reservoir.
One approach is to use a linear interpolation between velocities measured at hydrostatic stress
conditions (e.g., Minkoff et al., 2004). Such an approach requires no assumptions about the response
of seismic velocities to stress. However, such an approach is extremely limited, because in reality rocks
around the reservoir are not at hydrostatic stress. An approach is needed that can map triaxial stress
changes to anisotropic variations in seismic properties. Furthermore, experimental observations show
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