Microseismic Monitoring and Geomechanical Modelling of CO2 - bris

CHAPTER 6.
GENERATING ANISOTROPIC SEISMIC MODELS BASED ON GEOMECHANICAL SIMULATION
6.4 Calibration with literature data
Thus far I have provided only a limited amount of stress-velocity data with which my theory can be
calibrated. Many other works (e.g., Sayers, 2002; Hall et al., 2008; Prioul et al., 2004; Hornby, 1998)
also only provide limited numbers of samples. What is required is a compilation of data from many
samples from which trends and rules-of-thumb can be developed, as well as calibration of the typical
value ranges. This information will be very useful for model population. With this in mind Doug
Angus and I have used my model to invert for crack density and aspect ratio based on ultrasonic
velocity measurements on over 200 samples from the literature. I am grateful to Doug Angus for
compiling much of the ultrasonic velocity database upon which these calibration measurements could
be made, and also for writing the shell scripts that allowed my code to work rapidly on many individual
samples with minimal work input from the user. I am also grateful to Doug Schmitt for providing
data compiled by many of his graduate students.
Table 6.5 list the studies in which Doug Angus found usable data, in the form of stress versus
ultrasonic velocity measurements made on drained core samples. There is a large range of experimental
techniques deployed across these reports. Some of these reports have measured anisotropic velocities,
while some only make isotropic measurements. Some studies have used triaxial stresses to deform the
samples, while some use uniaxial and some hydrostatic stresses. Where anisotropic data is available, it
was inverted for, while for isotropic measurements the inversion can be collapsed into isotropic form.
Similarly, when triaxial stress have been used, this is included in the inversion, while these effects
can be ignored for hydrostatic cases. Where mineral data have been provided this has been used to
compute the background stiffness tensor C r , and where they have not we used the behaviour at high
pressures to estimate this tensor.
Figure 6.11 shows the inverted a 0 and ξ 0 values computed from 234 literature samples, coloured
by lithology. Mean values for each lithology, and standard deviations, are plotted in Table 6.6. The
overarching trend in Figure 6.11 is that, except for shales, the initial aspect ratios show remarkable
consistency, with aspect ratios of approximately 0.0002 – 0.0006. The initial crack density estimates
show greater scatter, falling between 0.0-0.5. In Table 6.6,variations between lithologies can be seen.
The clearest lithology variation is that shales generally have low values for ξ 0 , and high values of a 0 .
I also note that anhydrites have very low values for ξ 0 , implying a relative lack of stress sensitivity
for these rocks. Carbonates also appear to be less sensitive to stress than clastic rocks. The most
likely explanation is that the degree of chemical cementation found in carbonate and anhydrite rocks
makes them less sensitive to stress than sandstones and conglomerates, which appear to have generally
higher microcrack densities.
We also examined the dependence of initial crack density and aspect ratio on the depth from
which the sample was recovered and porosity (where this information has been provided) but did
not find any noticeable trends. However, most of the samples have been recovered from reservoirs.
Thus, the limited depth distribution in the data may not be sufficient to extract any trends. This
highlights the need to sample and measure all of the overburden when attempting to predict velocity
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