Abstract. The Waiwera aquifer hosts a structurally complex
geothermal groundwater system, where a localized thermal anomaly feeds the
thermal reservoir. The temperature anomaly is formed by the mixing of waters
from three different sources: fresh cold groundwater, cold seawater and warm
geothermal water. The stratified reservoir rock has been tilted, folded,
faulted, and fractured by tectonic movement, providing the pathways for the
groundwater. Characterization of such systems is challenging, due to the
resulting complex hydraulic and thermal conditions which cannot be
represented by a continuous porous matrix. By using discrete fracture network models (DFNs) the discrete aquifer
features can be modelled, and the main geological structures can be
identified. A major limitation of this modelling approach is that the
results are strongly dependent on the parametrization of the chosen initial
solution. Classic inversion techniques require to define the number of
fractures before any interpretation is done. In this research we apply the transdimensional DFN inversion methodology
that overcome this limitation by keeping fracture numbers flexible and gives
a good estimation on fracture locations. This stochastic inversion method
uses the reversible-jump Markov chain Monte Carlo algorithm and was
originally developed for tomographic experiments. In contrast to such
applications, this study is limited to the use of steady-state borehole
temperature profiles – with significantly less data. This is mitigated by
using a strongly simplified DFN model of the reservoir, constructed
according to available geological information. We present a synthetic example to prove the viability of the concept, then
use the algorithm on field observations for the first time. The fit of the
reconstructed temperature fields cannot compete yet with complex
three-dimensional continuum models, but indicate areas of the aquifer where
fracturing plays a big role. This could not be resolved before with
continuum modelling. It is for the first time that the transdimensional DFN
inversion was used on field data and on borehole temperature logs as input.
Porosity, permeability and 3D fracture network characterisation of dolomite reservoir rock samples
