Presentation: The potential of temperature and dissolved gas as smart tracers for process-based heterogeneity characterization

Event: AGU Fall Meeting 2019, San Francisco (USA) Presentation by Richard Hoffmann, Pascal Goderniaux, Alain Dassargues

Abstract

Informative reference data for a realistic assessment of aquifer heterogeneity is a prerequisite for robust transport simulations. Structure-based imaging using salt or a dye as tracer with a known concentration and volume to observe transfer times, is a powerful hydrogeological tool in moderate heterogenous media. Solving then the advection-dispersion equation will explain most of the point to point transport behavior. But, once the aquifer heterogeneity is more complex, e.g. in a double porosity medium like chalk, matrix porosity linked to diffusion processes must be taken into consideration to avoid a biased interpretation of the tracer information. Thus, performing additional local process-based imaging using smart tracers as dissolved gas and hot or cold water, assists to explain the late-time tailing behaviors realistically.

Smart tracers were injected in a sub-horizontal fracture connecting two adjacent wells to provide data about the complementary behaviors of each tracer and to focus on matrix diffusion processes. One reference data set is a 70 hours injection of hot water (∆T = + 40 °C) complemented by two 10 minutes uranine pulse injections within an inflatable double packer system isolating the sub-horizontal chalk fracture of interest. The temperature signal arrives at a 7.55 m distance with a delay of 12 hours compared to the first uranine injection and shows a rebound after the injection stopped. Useful reference data for further numerical modelling consists now in (a) local fracture geometry information deduced from interpretation by analytical solutions and, (b) matrix diffusion information.

Numerical modelling of those smart tracer experiments may question deterministic models for predictions and motivates for data-driven prediction tools like Monte-Carlo simulation procedures within a direct predictive framework. Distance based global sensitivity analysis (e.g. simultaneous variation of multiple input variables like diffusion coefficient, aperture and matrix storage) will be considered accounting for temperature related changes of viscosity and density. Key information about the most influencing parameters are main model outcomes, as local process understanding is very useful for possible future upscaling in regional models made of structure-based imaging.


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Publication: Heterogeneity and Prior Uncertainty Investigation Using a Joint Heat and Solute Tracer Experiment in Alluvial Sediments

in frontiers in Earth Sciences (May 2019)
by Richard Hoffmann, Alain Dassargues, Pascal Goderniaux, Thomas Hermans
https://doi.org/10.3389/feart.2019.00108

Abstract

In heterogeneous aquifers, imaging preferential flow paths, and non-Gaussian effects is critical to reduce uncertainties in transport predictions. Common deterministic approaches relying on a single model for transport prediction show limitations in capturing these processes and tend to smooth parameter distributions. Monte-Carlo simulations give one possible way to explore the uncertainty range of parameter value distributions needed for realistic predictions. Joint heat and solute tracer tests provide an innovative option for transport characterization using complementary tracer behaviors. Heat tracing adds the effect of heat advection-conduction to solute advection-dispersion.

In this contribution, a joint interpretation of heat and solute tracer data sets is proposed for the alluvial aquifer of the Meuse River at the Hermalle-sous-Argenteau test site (Belgium). First, a density-viscosity dependent flow-transport model is developed and induce, due to the water viscosity changes, up to 25% change in simulated heat tracer peak times. Second, stochastic simulations with hydraulic conductivity (K) random fields are used for a global sensitivity analysis. The latter highlights the influence of spatial parameter uncertainty on the resulting breakthrough curves, stressing the need for a more realistic uncertainty quantification.

This global sensitivity analysis in conjunction with principal component analysis assists to investigate the link between the prior distribution of parameters and the complexity of the measured data set. It allows to detect approximations done by using classical inversion approaches and the need to consider realistic K-distributions.

Furthermore, heat tracer transport is shown as significantly less sensitive to porosity compared to solute transport. Most proposed models are, nevertheless, not able to simultaneously simulate the complementary heat-solute tracers.

Therefore, constraining the model using different observed tracer behaviors necessarily comes with the requirement to use more-advanced parameterization and more realistic spatial distribution of hydrogeological parameters. The added value of data from both tracer signals is highlighted, and their complementary behavior in conjunction with advanced model/prediction approaches shows a strong uncertainty reduction potential.

Full article here

Datasets of this study are to be found on the H+ database


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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


Abstract

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


Abstract

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.


ESR11_IAH2019_MonteCarlo_Poster208_HoffmannEtAl


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