Presentation: Cold water injections as innovative smart tracer technique in hot fractured aquifers

Event: IAH 2019 Congress – Groundwater management and governance coping with water scarcity, Malaga (Spain)
Presentation by Richard Hoffmann, Wajid Uddin, Pascal Goderniaux, Alain Dassargues, Jean-Christophe Maréchal, Subash Chandra, Virendra Tiwari, Adrien Selles


Robust transport simulations for sustainable management of groundwater in fractured rocks, need accurate observation data about fracture and matrix processes. In aquifers with naturally hot groundwaters (i.e., 30 ºC in South India), heat injections can become difficult and cumbersome, considering strong density influences.

Injecting cold water is a much more promising and innovative tracer technique. Injecting cold water reduces the energy stored in the matrix, as heat is released to the colder circulating fluid in the fractures. Thus, cold water injections can produce very informative reference data for managing hot fractured aquifers using groundwater flow and cold plume transport numerical modeling.

Heat and cold water tracer tests have been performed for the first time in Choutuppal nearby Hyderabad in South India. Sub-horizontal fractures have been intersected by 30 wells drilled in a weathered granite aquifer. A saprolite layer of in average 14 m thickness covers the fractured granite system. The natural granite aquifer background temperature varies yearly between 30 ºCand 35 ºC During the experiments, the natural aquifer background temperature was around 30.3 ºC

The most explored well (CH03) is used as injection well for all experiments. There, an inflatable double packer system isolates one sub-horizontal fracture connecting CH03 with a pumping well (CH12) located at a 5.5 m distance. This set-up allows successive 1-hour injections of 1000 L of hot water (ΔT = +20 ºC) and cold water (ΔT = -20 ºC).

The peak arrival times measured in CH12 are 41 minutes for heat and 51 minutes for cold water. The peak temperature difference measured in CH12 for heat is ΔT = +3.3 ºC and for cold water ΔT = -2.9 ºC This is consistent with the fact that density and viscosity decrease with higher temperatures. Remarkably, cold water shows a slightly faster first arrival. It might indicate that storing energy is slightly faster initiated than releasing energy from the matrix.

First interpretations of the observed tailings show that for hot water injection, the subsequent temperature decrease (back to the background T) seems slower than the observed temperature increase after the cold water injection. It seems that cooling the matrix (i.e. reducing the energy level) is slightly more time consuming and difficult than heating the matrix (i.e. storing energy).

More experiments, e.g. repetitions of these experiments focusing stronger on the tailing for imaging matrix processes, complementing cold water tracing experiments (e.g. push-pull) and the possible parallel use of geophysical imaging tools, are ongoing. Nevertheless, the first tracer tests with cold water injections generated reference data that are very informative for further transport modeling (e.g. using Monte Carlo simulations).

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Poster: Modeling a heat tracer test in alluvial sediments using Monte Carlo: on the importance of the prior

Event: IAH Congress – Groundwater management and governance coping with water scarcity, Malaga (Spain), 2019
Poster by Richard Hoffmann, Alain Dassargues, Pascal Goderniaux, Thomas Hermans


In hydrogeology, deterministic model calibrations are useful to understand the influence of parameters on the considered variables or to image large-scale spatial parameter distribution. Oftentimes, deterministic solutions bias the problem with too smoothed parameter distributions leading to unrealistic transport predictions with underestimated uncertainties.

Instead of predictions using an optimum parameterization in conjunction with reference data confirming the model, a realistic heterogeneity consideration is crucial for robust transport simulations and managing aquifer systems sustainable. Thus, using random generated models as multiple hypotheses (e.g. with Monte Carlo), then a hypothesis may be rejected, when the model does not confirm reference data (falsification step).

For that, the reference data set in this study is a heat tracer experiment in alluvial sediments (Belgium). Between an injection well and a pumping well 20 m apart, three observation panels are located at distances of 3, 8 and 15 m downgradient from the injection well. Each panel consists of 3 wells with screened intervals in the upper and lower aquifer parts. A deterministic calibration of the experiment on temperature data, using jointly HydroGeoSphere and PEST, hardly describes the experimental observations.

The resulting spatial hydraulic conductivity distribution (K) is probably too smooth. Instead, 250 realizations using Monte Carlo in combination with sequential gaussian simulation for the K-distributions define the prior (hypotheses). For the K-distribution two scenarios are used: (1) a random K-distribution with unknown mean, variance and spatial correlation and (2) the same approach but with a downwards increasing vertical trend for the K-distribution, to mimic the observed increasing grain sizes of the sediment with depth.

With Scenario 1, the prior range (250 simulations) surrounds the reference data (i.e. heat breakthrough curves) for most of the experiment, but not for the tailing. The prior generated using Scenario 2 (with the vertical K-trend) improves the simulation of the breakthrough tailings for panel 1 and 2. In panel 3 (15 m downgradient), simulations for the lower aquifer part show significant lower peaks than measured. Scenario 1 is falsified (rejected), because the prior (250 models) do not confirm the reference data, while scenario 2 is not-falsified till panel 2 (8 m downgradient). Scenario 2 addresses the heterogeneity of the test site more realistically than all previous unsatisfying deterministic attempts.

A global sensitivity analysis at panel 1 and 2 identifies then the spatial K-distribution and its variance as the most sensitive parameters. This confirms, that future efforts needed for panel 3, should focus on identification of heterogeneous patterns in the aquifer and their subsequent introduction in the model.

As a perspective, the use of a direct predictive framework (e.g. Bayesian Evidential Learning), avoiding the commonly used calibration procedure, promises robust decisions made by more realistic quantifications of the uncertainty caused by heterogeneity.


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Presentation: “Identification of 3D fracture distribution and fracture connectivity by combined Ground Penetrating Radar Imagery and Tracer Tests at the Äspö Hard Rock Laboratory, Sweden

Event: CBH Studiedag Hydrogeofysica – Journée d’étude Hydrogéophysique, Rochefort (Belgium), May 2019
Presentation by Justine Molron, Niklas Linde, Ludovic Baron, Peter Andersson, Diane Doolaeghe, Tanguy Le Borgne, Johanna Ragvald, Jan-Olof Selroos, Caroline Darcel, Philippe Davy


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Poster: Identification of 3D fracture distribution and fracture connectivity by combined Ground Penetrating Radar Imagery and tracer Tests at the Äspö Hard Rock Laboratory, Sweden

Event: Journées Scientificques de l’ED GAAL, 2019
Poster by Justine Molron, Niklas Linde, Ludovic Baron, Peter Andersson, Diane Doolaeghe, Tanguy Le Borgne, Johanna Ragvald, Jan-Olof Selroos, Caroline Darcel


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