Granitic gneiss has been considered as one of the potential host rocks for deep
geological disposal of spent nuclear fuels in Taiwan. From 2000 to 2010, extensive
site characterization focused on a granitic rock body in a satellite island 277 km west
of Taiwan. After 2010, the efforts have been shifted to finding a new granitic rock
body in the eastern part of Taiwan. To achieve that goal, a testing site was selected
near a quarry next to the Dazhuoshui River. A vertical borehole was drilled to the
depth of 600 m. Unfortunately, all field works have been terminated indefinitely
because of political intervention. This study will introduce results of fracture mapping
and numerical modeling of discrete fracture network (DFN) in a tunnel situated in a
granitic gneiss rock body 25 km southwest to the testing site. Simulation tools
include a self-developed code DFN_OPT and the commercial software FracMan.
Fracture traces along four sub-horizontal scanlines and in two vertical scanwindows
were measured. Two high-angle and two low-angle joint sets were identified, with
76% and 13% of the data classified as the high-angle and low-angle sets, respectively.
Analyzing scanwindow data using the Miller’s method indicated that the rock body
under investigation can be considered as homogeneous. Consequently, the sample
data can be treated as a whole without the need of sub-dividing the data into
different groups. However, the simulation domain was divided into three subdomains
because tunnel orientation changes from N30E to N73E then to N65W. Prior to
DFN modeling, fracture density of each set was determined by developing a
regression equation between P32 and P21 from which a P32 value can be obtained
given the P21 calculated from field data. For scanwindow data, DFN_OPT used the
algorithm of simulated annealing (SA) to perturb the initial DFN until univariate and
two-point statistics of P21 calculated from the sampling windows best match those
of the final DFN. Similarly, scanline data was perturbed until P10 calculated from four
arbitrary scanlines in each tunnel wall best match that of the final DFN. The
advantage of SA optimization is to validate the simulated DFN against fracture traces
observed in the field. Similar optimization scheme is not included in FracMan.
Consequently, only conditioning simulation was done for FracMan modeling. Despite
the differences in DFN optimization, DFNs simulated from DFN_OPT and FracMan
can reasonably reproduce statistical characteristics of fracture parameters calculated
from field data.