Geostatistical Simulation of High-Transmissivity Zones at the
Mirror Lake Site in New Hampshire: Conditioning to Hydraulic Information
By Frederick D. Day-Lewis, Paul A. Hsieh, Allen M. Shapiro, and
Steven M. Gorelick
ABSTRACT
A new approach is presented to include hydraulic information as
conditioning data in geostatistical simulation of
high-transmissivity zones. A simulated-annealing algorithm is
used to generate three-dimensional geostatistical realizations
conditioned to borehole data and inferred hydraulic connections
between packer-isolated borehole intervals. High-transmissivity
zones were identified by Hsieh and Shapiro (1996) in the bedrock
underlying the FSE well field at the U.S. Geological Survey
Fractured Rock Research Site near Mirror Lake, Grafton County,
New Hampshire. These zones are conceptualized to consist of
connected, highly transmissive fractures that are embedded
within a surrounding network of less transmissive
fractures. During multiple-well hydraulic tests, well intervals
connected by a high-transmissivity zone exhibit different
responses. To analyze the test data, Hsieh and others (1999)
constructed and calibrated a deterministic ground-water flow
model. In this study, alternative spatial patterns of hydraulic
properties are based on geostatistical realizations. The
simulated-annealing algorithm is used to generate conditional
realizations of high-transmissivity zones in the bedrock
underlying the FSE well field, using an indicator-variogram
model of spatial variability. Statistical analysis of the
generated zones yields three-dimensional images of the
probability that a high-transmissivity zone occurs and the
likely spatial extents of specific zones. For selected
realizations, ground-water flow is simulated with a
finite-element model. Hydraulic conductivity and specific
storage values of high-transmissivity zones and background rock
are calibrated. Simulation results for realizations conditioned
to borehole and hydraulic connection data compare favorably to
the test data, and realizations exhibit more complex spatial
variability than previous deterministic modeling by Hsieh and
others (1999).