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Geothermal systems, including borehole thermal energy storage (BTES) and shallow aquifer thermal energy storage systems (ATES) are becoming more popular as the world looks for ways to reduce greenhouse gas emissions. Such systems use thermal energy extracted from the ground or groundwater to heat or cool buildings, which necessitates some electrical energy input for the heat pump, while storing the excess heat or cold underground. The goal is to re-use this thermal energy during the next season in a cyclic utilization (Bayer et al., 2013;Duijff et al., 2021;Saner et al., 2010;Vanhoudt et al., 2011). The performance of BTES and ATES strongly depends on the subsurface properties. Many variables are involved in geothermal processes, including porosity, hydraulic conductivity, thermal conductivity, and heat capacity. Subsurface temperature fluctuations are strongly influenced by the spatial distribution of these parameters, the boundary conditions, and the aquifer's hydraulic gradient

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With the growing population and the adverse effects of climate change, the pressure on coastal aquifers is increasing, leading to a larger risk of saltwater intrusion (SI). SI is often complex and difficult to characterize from well data only. In this context, electrical resistivity tomography (ERT) can provide high-resolution qualitative information on the lateral and vertical distribution of salinity. However, the quantitative interpretation of ERT remains difficult because of the uncertainty of petrophysical relationships, the limitations of inversion, and the heterogeneity of aquifers. In this contribution, we propose a methodology for the semiquantitative interpretation of ERT when colocated well data are not available. We first use existing wells to identify freshwater zones and characterize the resistivity response of clayey deposits. Then, we approximate the formation factor from water samples collected in the vicinity of ERT data to derive a resistivity threshold to interpret the saline boundary. We applied the methodology in the shallow aquifers of the Luy River in the Binh Thuan province, Vietnam, where water resources are under pressure due to agricultural, aquacultural, and industrial production. Twenty-one ERT profiles were collected and revealed a much larger intrusion zone, compared to the previous study. Saltwater is present in lowland areas of the left bank over almost the whole thickness of the aquifer, while the right bank is constituted of sand dunes that are filled with freshwater. At a larger distance from the sea, a complex distribution between fresh and saltwater is observed. Our methodology could be applied to other heterogeneous aquifers in the absence of a dense monitoring network.

Temperature logs are an important tool in the geothermal industry. Temperature measurements from boreholes are used for exploration, system design, and monitoring. The number of observations, however, is not always sufficient to fully determine the temperature field or explore the entire parameter space of interest. Drilling in the best locations is still difficult and expensive. It is therefore critical to optimize the number and location of boreholes. Due to its higher spatial resolution and lower cost, four-dimensional (4D) temperature field monitoring via time-lapse Electrical Resistivity Tomography (ERT) has been investigated as a potential alternative. We use Bayesian Evidential Learning (BEL), a Monte Carlo-based training approach, to optimize the design of a 4D temperature field monitoring experiment. We demonstrate how BEL can take into account various data source combinations (temperature logs combined with geophysical data) in the experimental design (ED). To optimize the ED and determine the best data source combination, we use the Root Mean Squared Error (RMSE) of the predicted target in the low dimensional latent space where BEL is solving the prediction problem. The generated models agree well with the true models and are accurate enough to be used in optimal ED.Furthermore, the method is not limited to monitoring temperature fields and can be applied to other similar experimental problems.The method is computationally efficient and requires little training data. A training set of only 200 is sufficient for the considered optimal design problem.