Shallow geothermal energy (SGE) is a renewable energy that could contribute to the decarbonatization of the heating and cooling sector. SGE is predominantly harnessed through ground source heat pump (GSHP) systems. The choice of which type of GSHP system depends on various factors. Understanding these factors is crucial for optimizing the efficiency of GSHP systems and fostering their implementation. In this paper, we have analysed the spatial distribution of GSHPs in Slovenia. We identified 1073 groundwater and 1122 ground-coupled heat pump systems with a total heat pump capacity of almost 30 MW. We quantitatively assessed the influence of geological, hydrogeological, and climate conditions on their spatial distribution. Using the χ² test and information value method, we identified hydrogeological conditions as the most influential factor for the GSHP systems’ spatial distribution. We also performed the spatial analysis of geological and hydrogeological data in 22 European countries, including Slovenia. We collected the reported numbers of installed GSHP units in 2020 and were able to distinguish the shares of groundwater and ground-coupled heat pump systems for 12 of these countries. The analysis showed that ground-coupled heat pumps predominate in most countries, even if the natural conditions are favourable for groundwater heat pumps.

Thermal conductivity is one of the key parameters for estimating low-temperature geothermal potential. In addition to field techniques, it can be determined based on physical parameters of the sediment measured in the laboratory. Following the methodology for cohesive and non-cohesive sample preparation, laboratory measurements were carried out on 30 samples of sediments. Density, porosity and water content of samples were measured and used in thermal conductivity estimation models (TCEM). The bulk thermal conductivity ( $${\lambda }_{b}$$λ b ) calculated with six TCEMs was compared with the measured $${\lambda }_{b}$$λ b to evaluate the predictive capacity of the analytical methods used. The results show that the empirical TCEMs are suitable to predict the $${\lambda }_{b}$$λ b of the analysed sediment types, with the standard deviation of the residuals (RMSE) ranging from 0.11 to 0.35 Wm⁻¹ K⁻¹. To improve the fit, this study provides a new modified parameterisation of two empirical TCEMs (Kersten and Côté&Konrad model) and, therefore, suggests the most suitable TCEMs for specific sample conditions. The RMSE ranges from 0.11 to 0.29 Wm⁻¹ K⁻¹. Mixing TCEM showed an RMSE of up to 2.00 Wm⁻¹ K⁻¹, meaning they are not suitable for predicting sediment $${\lambda }_{b}$$λ b . The study provides an insight into the analytical determination of thermal conductivity based on the physical properties of sediments. The results can help to estimate the low-temperature geothermal potential more quickly and easily and promote the sustainable use of this renewable energy source, which has applications in environmental and engineering science.

To achieve sustainable and efficient use of shallow geothermal resources, it is important to understand the heat transfer in the subsurface of the planned geothermal system. In the City Municipality of Murska Sobota, NE Slovenia, the use of geothermal open-loop systems has increased in recent years. Their high spatial density raises the question of possible mutual interference between the systems. By compiling geological, hydrogeological, and thermal data, obtained from the monitoring network, fieldwork, and knowledge of regional hydrogeological conditions, we have developed a transient groundwater flow and heat transfer model to evaluate the impact of the open-loop systems on the subsurface and surrounding systems. The transient simulation showed that the thermal state in the observed area is restored over the summer, when the systems are not in operation. Also, the systems do not have significant mutual interference that would affect their efficiency. However, as interest in installing new systems in the area increases, simulations of the thermal plumes of new geothermal systems are needed to ensure sustainable and efficient use of shallow geothermal energy in the future.

To achieve sustainable and efficient use of shallow geothermal resources, it is important to understand the heat transfer in the subsurface of the planned geothermal system. In the City Municipality of Murska Sobota, NE Slovenia, the use of geothermal open-loop systems has increased in recent years. Their high spatial density raises the question of potential mutual interference between the systems. By compiling geological, hydrogeological, and thermal data, obtained from the monitoring network, fieldwork, and knowledge of regional hydrogeological conditions, we have developed a transient groundwater flow and heat transfer model to evaluate the impact of the open-loop systems on the subsurface and surrounding systems. Time series data cover time span ranging from December 2019 to the end of December 2021. The sensitivity analysis showed the highest composite sensitivity values for hydraulic conductivity, porosity and dispersivity parameters, which were further calibrated with FePEST to minimize the error between groundwater level and temperature. The results of the groundwater flow model correspond well with the measured average groundwater levels. On the other hand, the thermal model shows higher deviations from the measured data, especially in the summer months when the simulated groundwater temperatures do not exceed 14.3 °C, while the measured temperatures reached even 15.4 °C. These deviations could be related to the effects of local thermal sources on the surface (e.g., sewage pipes, plumbing, buildings with more than one basement and roads), which were not considered in our model. The transient simulation showed that the thermal state in the observed area is restored over the summer, when the systems are not in operation. Also, the systems do not have significant mutual interference that would affect their efficiency. However, as interest in installing new systems in the area increases, simulations of the thermal plumes of new geothermal systems are needed to ensure sustainable and efficient use of shallow geothermal energy in the future.

Shallow geothermal energy is a renewable energy source that will play an important role in future energy management plans. Densely populated areas are often developed on alluvial plains, which consist of unconsolidated sediments. These have different thermal properties, so their accurate determination is important for planning subsurface heat utilization for heating and cooling of buildings in urban areas. Bulk thermal conductivity (λb) is one of the most important ground thermal properties for estimating shallow geothermal potential, as it controls the ability of sediments to transfer heat. The λb can be determined with empirical bulk thermal conductivity estimation models (λb EM), which define λb as a function of the measured physical parameters of the sediment (water content, bulk density) and the fluid. In this contribution, we present a preliminary study of three empirical evaluation models for determining the thermal conductivity of sediments – the Kersten (1949), the Johansen (1975) and the Cote & Conrad model (2005). Validation was carried out with laboratory-measured λb using 30 unconsolidated sediment samples classified into 2 different groups (cohesive, non-cohesive) and by water content. The modelled results were evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The modelled λb for non-cohesive sediments has the highest λb with the Johansen model. The lowest RMSE was obtained with the Kersten model. For cohesive sediments, the highest λb and lowest RMSE, and consequently the best model, are based on the saturation of the sediments. It varies between the Cote & Conrad and the Kersten model. By dividing the sediment samples based on shear strength and water content, we obtained the better agreement of individual groups with estimation models. This showed the importance of the physical parameters in better predicting the modelled results. In the future, we will need to upgrade results with the use of more estimation models, that could improve the modelled results. With such an approach the estimation models can become a useful tool for a faster determination of the shallow geothermal potential.