Maria Klepikova

Coupling heat and chemical tracer experiments for estimating heat transfer parameters in shallow alluvial aquifers

Geothermal energy systems, closed or open, are increasingly considered for heating and/or cooling buildings. The efficiency of such systems depends on the thermal properties of the subsurface. Therefore, feasibility and impact studies performed prior to their installation should include a field characterization of thermal properties and a heat transfer model using parameter values measured in situ. However, there is a lack of in situ experiments and methodology for performing such a field characterization, especially for open systems. This study presents an in situ experiment designed for estimating heat transfer parameters in shallow alluvial aquifers with focus on the specific heat capacity. This experiment consists in simultaneously injecting hot water and a chemical tracer into the aquifer and monitoring the evolution of groundwater temperature and concentration in the recovery well (and possibly in other piezometers located down gradient). Temperature and concentrations are then used for estimating the specific heat capacity. The first method for estimating this parameter is based on a modeling in series of the chemical tracer and temperature breakthrough curves at the recovery well. The second method is based on an energy balance. The values of specific heat capacity estimated for both methods (2.30 and 2.54MJ/m(3)/K) for the experimental site in the alluvial aquifer of the Meuse River (Belgium) are almost identical and consistent with values found in the literature. Temperature breakthrough curves in other piezometers are not required for estimating the specific heat capacity. However, they highlight that heat transfer in the alluvial aquifer of the Meuse River is complex and contrasted with different dominant process depending on the depth leading to significant vertical heat exchange between upper and lower part of the aquifer. Furthermore, these temperature breakthrough curves could be included in the calibration of a complex heat transfer model for estimating the entire set of heat transfer parameters and their spatial distribution by inverse modeling.

Heat as a tracer for understanding transport processes in fractured media: Theory and field assessment from multiscale thermal push‐pull tracer tests

The characterization and modeling of heat transfer in fractured media is particularly challenging as the existence of fractures at multiple scales induces highly localized flow patterns. From a theoretical and numerical analysis of heat transfer in simple conceptual models of fractured media, we show that flow channeling has a significant effect on the scaling of heat recovery in both space and time. The late time tailing of heat recovery under channeled flow is shown to diverge from the TðtÞ / t 21:5 behavior expected for the classical parallel plate model and follow the scaling TðtÞ / 1=tðlog tÞ 2 for a simple channel modeled as a tube. This scaling, which differs significantly from known scalings in mobile-immobile systems, is of purely geometrical origin: late time heat transfer from the matrix to a channel corresponds dimensionally to a radial diffusion process, while heat transfer from the matrix to a plate may be considered as a one-dimensional process. This phenomenon is also manifested on the spatial scaling of heat recovery as flow channeling affects the decay of the thermal breakthrough peak amplitude and the increase of the peak time with scale. These findings are supported by the results of a field experimental campaign performed on the fractured rock site of Ploemeur. The scaling of heat recovery in time and space, measured from thermal breakthrough curves measured through a series of push-pull tests at different scales, shows a clear signature of flow channeling. The whole data set can thus be successfully represented by a multichannel model parametrized by the mean channel density and aperture. These findings, which bring new insights on the effect of flow channeling on heat transfer in fractured rocks, show how heat recovery in geothermal tests may be controlled by fracture geometry. In addition, this highlights the interest of thermal push-pull tests as a complement to solute tracers tests to infer fracture aperture and geometry.

Uncertainty Quantification of Medium-Term Heat Storage From Short-Term Geophysical Experiments Using Bayesian Evidential Learning: PREDICTING HEAT STORAGE WITH ERT

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non-favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre- and post-field data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements. Plain Language Summary : Geothermal energy can be extracted or stored in shallow aquifers through systems called aquifer thermal energy storage (ATES). In practice, the energy efficiency of those systems is often lower than expected because of the uncertainty related to the subsurface. To assess the uncertainty, a common method in the scientific community is to generate multiple models of the subsurface fitting the available data, a process called stochastic inversion. However this process is time consuming and difficult to apply in practice for real systems. In this contribution, we develop a novel approach to avoid the inversion process called Bayesian Evidential Learning. We are still using many models of the subsurface, but we do not try to fit the available data. Instead, we use the model to learn a direct relationship between the data and the response of interest to the user. For ATES systems, this response corresponds to the energy extracted from the system. It allows to predict the amount of energy extracted with a quantification of the uncertainty. This framework makes uncertainty assessment easier and faster, a prerequisite for robust risk analysis and decision making. We demonstrate the method in a feasibility study of ATES design.