Can short-term hydrogeological experiments predict the long-term behavior of subsurface reservoirs? An example from shallow geothermy

Aquifer thermal energy storage (ATES) systems can potentially store and recover thermal energy seasonally. In practice, the increase of energy efficiency is often lower than expected from simulations due to spatial heterogeneity or non-favorable conditions. In many cases, the lack of available data leads the modeler to consider homogeneous layered conceptual models to forecast the long-term behavior of geothermal systems. Ignoring spatial heterogeneity bears the risk of misleading decisions based on the prediction of those models. The proper design of ATES systems should always consider the uncertainty about subsurface parameters. In practice, classical hydrogeological tests including geophysical surveys are performed to gain knowledge on subsurface parameters. One question remains: are these tests sufficiently informative to predict with realistic uncertainty the long-term behavior of reservoirs?
We investigate how short-term heat tracing and storage experiments can predict the long-term behavior of ATES system. We combine field experiments with a probabilistic modeling approach called Bayesian Evidential Learning (BEL) to assess the information content of our data set(s). BEL relies on a set of surrogate models of the subsurface representing prior uncertainty. It uses a global sensitivity analysis to identify sensitive parameters for long-term heat storage and short-term experimental data and can validate the use of short-term experiments to generate informative data sets. In addition, this approach allows a direct estimate of the uncertainty range of the prediction from the observed experimental data, without explicit inverse modeling. The methodology therefore allows us to tests experimental hypothesis that can be further validated with field data.
Here, we use the approach to compare the information content of different data acquisition schemes: tracing vs. storage/push-pull experiments, standard vs. multi-cycle experiments, very-short vs. long experiments, single-hole vs. multi-borehole and geophysical measurements. Finally, we illustrate and validate the proposed framework with field data.