With the ratification of the Paris Agreement in 2015 and the accelerating global climate crisis, reducing our carbon footprint has become crucial, particularly in the energy sector. Consequently, developing geothermal energy has emerged as a priority in the energy policies of many countries, including Austria and Canada. Traditional geothermal exploration for deep geothermal projects relies on conventional active seismic surveys, which are both logistically challenging and expensive. Recently, noise-based passive seismic methods combined with large and dense seismic nodal arrays have shown to be reliable and cost-effective alternatives for geothermal exploration. Although these nodes are typically designed with high corner-frequencies (>5 Hz), signals within the microseism bandwidths (0.15–1 Hz) can be accurately retrieved by enhancing the signal-to-noise ratio through seismic interferometric methods such as waveform correlation and stacking. This makes them suitable for imaging purposes. Here, we briefly introduce three case studies, where noise-based passive seismic methods are applied for geothermal exploration: one in southern Yukon, Canada, and two in the Vienna Basin, Austria. We discuss features observed in our models relevant to geothermal exploration.

Land surface temperature (LST) is commonly retrieved from thermal infrared remote sensing data and has been used in various applications within the field of geothermal exploration. In geothermal studies, the measured LST is assumed to arise from the combined effect of surface and subsurface processes, with the latter being of fundamental importance to characterize. However, due to diurnal solar heating and spatial heterogeneity in the heating rates of surface materials, the subsurface heat component is recognized only when it presents a high contrast against the background temperature. In this work, we introduce a single-source energy balance model named SkinTES (Surface KINetic TEmperature Simulator), developed in the Interactive Data Language (IDL) environment, for modeling and correcting high-resolution (<100 m) surface temperature data for diurnal and topographic effects. This approach combines atmospheric parameters with a bulk-layer soil model and remote-sensing-based parameterization schemes to simulate surface temperature over bare surfaces. By solving the energy balance, heat, and water flow equations for each pixel and integrating the surface and subsurface energy fluxes over time, SkinTES generates a model-simulated temperature map. This map is then contrasted with concurrent remote sensing LST data to uncover the subtle temperature anomalies arising from subsurface geothermic processes. We present the theoretical basis of the model, its parameterization schemes, and the results obtained by applying it to point-scale and ASTER thermal datasets acquired over a geologically complex sedimentary basin in Iran. The potential application of the model in geologic studies and its capability in detecting blind geothermal systems are highlighted.

The area of Abano Terme is a prominent part of the Hydrothermal Basin of the Euganean Hills. The thermal waters are extracted from wells reaching depths of over 1000 meters. The water from the deep reservoir (upper Trias “Dolomia Principale” and Giurassic limestones) has averaged temperatures of 85°C, chlorinated characteristics and is rich in dissolved silica.

The vulnerability of the Euganean hydrothermal system has led, as is typically the case in similar contexts, to the prohibition of geothermal resource exploitation using classical open-loop systems, reserving the hot water solely for sanitary-thermal use.

Abandoned hydrothermal wells offers the potential for cost-effective energy recovery through conventional DBHE arrays. FEFLOW modelling has been conducted to verify the efficiency of the exchangers by the dynamic flow condition in the reservoir, facilitated by pumping for the thermal establishments, compared to a purely conductive scheme. Detailed simulations have verified scenarios with coaxial DBHE exchangers with vacuum insulated inner tubing, capable of minimizing thermal short-circuiting between the supply and return pipes.

For newly constructed wells, exploitation scenarios with systems conceptually similar to DCL® (Dynamic Closed Loop) technology have also been verified. This technology is currently being tested particularly for exchangers in shallow geothermal systems. The DCL type application, for proper evaluation and sizing, requires the availability of an accurate reservoir geological model (as normally available in hydrothermal exploitation areas). However, as confirmed by numerical modeling, it can find excellent application in deep geothermal systems, ensuring significantly higher energy extraction than classic DBHE.

The Upper Jurassic Aquifer (UJA) in the Molasse Basin, South Germany, presents favorable conditions for geothermal energy utilization due to its high temperature and promising hydraulic properties. In the city of Munich, the reservoir offers optimal conditions for geothermal energy production, driving further research in the area to meet the growing demand for renewable heat sources. Our study focuses on the Schaeftlarnstrasse (SLS) geothermal site in Munich, the largest inner-city geothermal plant in Europe. The site consists of three SLS geothermal doublets (each consisting of an extraction well and an injection well) that were developed to facilitate the reuse of hydrothermal fluids and enhance the reservoir's natural fractures. These doublets operate within a 500 m thick UJA, intersecting its numerous features, including three matrix blocks, two normal faults separating the matrices, two damage zones around the faults, a karst zone, and a debris facies. Due to this high heterogeneity in a relatively small localization, an accurate characterization of the reservoir’s hydraulic properties is needed to understand and improve its performance. This was done in this research using a detailed numerical model that could emulate the hydraulic processes in the reservoir upon which sensitivity and parametric studies are applied. These studies were able to determine the controlling parameters and the parameter combinations responsible for altering the reservoir conditions. They also account for better decision-making in designing and operating the wells, hence facilitating a more sustainable exploitation of the UJA geothermal resources.

The demand for sustainable and renewable energy sources has intensified the exploration of geothermal reservoirs globally and in the European region. This research aims to identify potential opportunities and geothermal applications across the UK and delves into the application of play fairway analysis for geothermal reservoirs.

Play fairway analysis has been conventionally used in the oil and gas industry at the pre-exploration stage for mitigating geological risks, ranking promising sites and identifying areas with the highest potential. The comprehensive analysis integrates multidisciplinary datasets to map the elements of a prospect and create a holistic understanding of subsurface conditions.

An approach to locating geothermal resources follows the play fairway analysis workflow that identifies key components of a prospect. Based on gathered geological data key parameters controlling the distribution of geothermal systems have been identified and mapped by applying weighted cut-offs. A scoring system has been applied to rank potential prospects and eliminate high-risk areas. As a result of the work, for different geothermal types composite common risk segment maps have been created integrating reservoir-level uncertainties.

Thus, applying play fairway analysis to geothermal reservoirs allows for mitigating risks associated with critical components controlling the resource quality. The described method could be applied to identify areas with optimal conditions thereby enhancing exploration efficiency and contributing to the sustainable development of geothermal energy. The results could be utilized as a basis for subsequent resource evaluation and economic feasibility assessment.