Reservoir-scale transdimensional fracture network inversion

Abstract

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.

Comparison of Hydraulic Travel Time and Attenuation Inversions, Thermal Tracer Tomography and Geostatistical Inversion for Aquifer Characterization: A Numerical Study

“…With the technological advances in temperature monitoring, using heat as a tracer to reveal the hydraulic properties of aquifers, this is attracting more and more attention. Particularly, cross-well thermal tracer tomography, that combines multilevel heat injection tests with imaging methods, has been proposed to investigate aquifer heterogeneity over recent years [40][41][42][43][44]. It has also been used to estimate K profiles in alluvial sediments [45].…”

Geographic Information System (GIS) as a basis for the next generation of hydrogeological models to manage the geothermal area Waiwera (New Zealand)

“…Various approaches for a quantified description of the Waiwera reservoir have been implemented since the 1980s. Some are data-driven (Kühn and Schöne, 2017;Kühn and Grabow, 2021) while others are process based (Kühn and Altmannsberger, 2016;Somogyvári et al, 2019) in order to finally comprehend and assess the constraints and impacts on the system (Kühn and Schöne, 2018). However, none of the models directly delivers all the results needed for an all-encompassing water management that would e.g.…”

Hybrid Discrete Fracture Network Inversion of Hydraulic Tomography Data From a Fractured‐Porous Field Site

“…Further insights into the properties of fracture networks can be gained by implementing a DFN model for the inversion. The hydraulic properties of a predefined DFN structure (Klepikova et al, 2014(Klepikova et al, , 2020, the structural properties (Somogyvári et al, 2019), or structural and hydraulic properties of DFNs (Fischer et al, 2020;Ringel et al, 2022) can be estimated. Typically, the contribution of the matrix to the flow is neglected in the DFN model, but the approach can infer individual (discrete) fractures based on the HT data.…”