To accelerate the energy transition, the exploitation of deep geothermal reservoirs is becoming a priority to supply district heating networks in areas with high potential for geothermal applications. However, the sustainable development of the resource exploitation implies minimizing the associated risks, in particular related to induced seismicity, while optimizing operational processes. Besides, the growth of this energy sector, often supported by financial aid programs, provides resources to the industry that were not available in the past to implement advanced monitoring strategies. In this context, we present a monitoring system establishing Distributed Acoustic Sensing (DAS) as an effective component of the seismic network used for the monitoring of the geothermal field of Schäftlarnstraße (Munich, Germany). We also investigate its potential for real-time seismic monitoring in an urban environment and for risk mitigation. The monitoring system is based on a data management system linking the on-site acquisition infrastructure, including the fiber optic cable deployed in an injection well and the associated DAS interrogator, to a cloud Internet-of-Things (IoT) platform. The latter is designed to deliver both a secure storage environment for the DAS recordings and optimized computing resources for their processing. The proposed solution has been tested over a six-month period under operating conditions of the geothermal field. The survey proves the feasibility of efficiently acquiring and processing the large flow of continuous DAS data. The processing outcomes, emphasized by two detected local seismic events, demonstrate the suitability of DAS, cemented behind the casing of a flowing well, for (micro-) seismic monitoring of the geothermal site. The processing applied to the data takes advantage of the high spatial density of the acquisitions for their de-noising and for the detection of events. We find that the DAS monitoring system is capable of successfully detecting an event that could not be detected by the standard surface or shallow-borehole 3C-seismometers, despite noisy conditions associated with the urban environment and the field operation. The six-month test period demonstrates the potential of DAS to be integrated as a routine seismic monitoring component of an operating geothermal field. In addition, it highlights its advantageous role as a complement to surface seismometer-based networks, particularly in urban environments.

Extreme environments on Earth host a large diversity of microbial life. Bacteria, archaea, and fungi are able to survive under one or several extreme conditions including extreme ranges of temperature, pressure, pH or salinity. Despite extensive research on extremophilic microorganisms, a relatively unexplored frontier within the study of the deep biosphere is the survey of the diversity of microorganisms inhabiting deep geothermal reservoirs used for energy production. These sites offer unique access to investigate life in the deep biosphere. The conditions in these reservoirs are often within the range of the known limits of life, which makes them a suitable habitat for various extremophilic microorganisms. Moreover, microbial-driven processes such as microbially induced scaling or corrosion can decrease the efficacy of geothermal power plant systems. The present review summarizes the current knowledge and uncertainties surrounding microbial life in deep geothermal reservoirs. As the knowledge in deep geothermal fluids is still scarce, the microbial diversity in analogous environments, such as surface geothermal springs, deep-sea hydrothermal vents or deep subsurface environments, is also summarized here. The high diversity of microorganisms inhabiting these analogous environments suggests that deep geothermal fluids may host an unsuspected microbial diversity. Moreover, the challenges associated to the study of microorganisms in geothermal fluids are reviewed. These include notably challenges linked to sampling, DNA extraction from low biomass samples, DNA amplification and sequencing of unknown communities, and biases induced by comparison of the sequences obtained to reference databases. Such biases are even stronger concerning fungi and archaea, as specific databases are less extensive than those for bacteria. A broader knowledge on microorganisms in deep geothermal fluids may not only allow to reduce the negative impact of microbial activity in geothermal power plants, but could also provide new insights into the evolution of microorganisms and their survival in extreme environments.

Geothermal energy production often involves use of corrosion inhibitors. We performed rock mechanical experiments (room temperature; confining pressure of 10/20/30 MPa) on typical reservoir rocks (Bentheim sandstone and Treuchtlinger limestone) in contact with two different inhibitor solutions or with demineralized water. The sandstone experiments show no discernible difference in rock strength between inhibitors or water, attributed to low quartz reactivity. The limestone experiments show a significant difference in rock strength (and Mohr–Coulomb envelope), dependent on inhibitor type, attributed to high carbonate reactivity. This implies that, depending on the reactivity of the rocks and local stress conditions, inhibitor leakage may lead to unpredicted reservoir failure.

Large-scale ground source heat pump (GSHP) systems are increasingly used for space heating and cooling. In comparison with smaller ones, large GSHP systems are often coupled with much more borehole heat exchangers (BHEs). Because of the intense thermal interactions between BHEs, they are more susceptible to significant ground temperature changes. Meanwhile, they possess the advantage that their operational strategies can be applied with a high degree of freedom, which presents chances to alleviate intense thermal interactions. In this study, we used a new performance indicator to access the effectiveness of GSHP operational strategies on alleviating thermal anomalies. The Daxing Airport GSHP system, contains 10,497 BHEs and is the largest in the world; therefore, it was selected as the test case for performance enhancement through operational strategies. We established a 2D model to predict ground temperature changes during the 50-year operation of the BHEs. First, it was revealed that the most severe thermal anomalies in the study area mainly occurred both within and between the BHE arrays, which should be mitigated. To alleviate the thermal anomalies caused by the thermal interactions of BHEs, operational strategies were applied by adjusting the cooling/heating starting sequence, setting time-dependent thermal loads, and reallocating thermal loads according to the position of the BHEs. Our study demonstrates that only the operation strategy that adjusts the cooling/heating starting sequence is beneficial for different BHE layouts, while the operational strategy that reallocates the thermal loads depending on BHEs position may be only effective for specific BHE layouts. In addition, our new performance indicator can be used to evaluate the effectiveness of the operational strategies and determine the spacing of adjacent BHE arrays. Therefore, it benefits the operation management of BHE array and design of BHE layout, and further guarantees the sustainable operation of the GSHP system.

Indonesia has high geothermal potential comprising 40% of the world’s potential geothermal energy, volcanic and non-volcanic systems. Volcanic systems have witnessed more exploration activities for geothermal resources compared to non-volcanic systems. A high potential non-volcanic system in Indonesia is located in the northern part of Konawe, Southeast Sulawesi. Previous research had identified surface temperature anomaly (high temperature) and some surface manifestations for this area, specifically in the northeast part of Wawolesea. However, the source of surface manifestations and permeable zones as an implication of a good reservoir are still unknown. Therefore, this research aims to investigate the permeable zones and geothermal potential in the non-volcanic geothermal system of north Wawolesea by applying lineaments analysis and the fault fracture density (FFD) method. A total of 1694 major and minor lineaments were manually delineated using ArcGIS based on Digital Elevation Model Nasional (DEMNAS). FFD map and rose diagrams displayed the orientation of all lineaments and structures with the major lineaments trending NNE–SSW, whereas the minor lineaments showed irregular distribution and orientation. Field measurements also show the same azimuth orientation for the mapped fractures. Five zones were characterized by high FFD values (2.81–4.54 km/km2). One of the extensively fractured zones (Zone C) is located between Meluhu and Lembo, covering an area of around 19.39 km2. This area is interpreted to be highly permeable and suggestive of a recharge area that contributes to surface manifestation in the Wawolesea. Therefore, the area between Meluhu and Lembo in the northern part of Konawe shows high geothermal potential due to its planar morphology and high FFD values. This study allows an improved understanding of how fracture geometry, distribution and density control the permeability in geothermal reservoirs.

More than 30% of Germany’s final energy consumption currently results from thermal energy for heating and cooling in the building sector. One possibility to achieve significant greenhouse gas emission savings in space heating and cooling is the application of aquifer thermal energy storage (ATES) systems. Hence, this study maps the spatial technical potential of shallow low-temperature ATES systems in Germany. Important criteria for efficient ATES operation considered in this assessment encompass suitable hydrogeological conditions, such as aquifer productivity and groundwater flow velocity, and balanced space heating and cooling demands. The latter is approximated by the ratio of heating and cooling degree days, which is incorporated as a time-dependent criterion to also evaluate the impact of climate change on the ATES potential. The hydrogeological and climatic criteria are combined within a spatial analysis revealing that, regarding the upcoming decades, about 54% of the investigated German area are very well or well suitable for ATES applications, largely concentrating on three regions: the North German Basin, the Upper Rhine Graben and the South German Molasse Basin. Considering time-dependent climatic conditions, the very well or well suitable areas will increase by 13% for the time period 2071–2100. This is mostly caused by a large relative area increase of the very well suitable regions due to an increasing cooling demand in the future. The sensitivity of the very well and well suitable regions to the criteria weightings is relatively low. Accounting for existing water protection zones shows a reduction of the country-wide share of very well or well suitable areas by around 11%. Nevertheless, the newly created potential map reveals a huge potential for shallow low-temperature ATES systems in Germany.

This paper, based on a novel hybrid techno-economic model for geothermal power plants with endogenized plant lifetime, investigates the economic feasibility of a sustainable exploitation of geothermal resources for electricity generation. To this end, standard terminology and classifications from the literature are reviewed, such as “sustainability”, “sustainable operation”, “renewability”, “recovery”, “recharge”, and “regeneration”. An illustrative conventional, convective high-enthalpy hydrothermal system is contrasted with an enhanced, conductive low-enthalpy petrothermal system. Furthermore, different (mostly geophysical) sustainable operation criteria for the use of geothermal energy are derived from the literature. The conditions for complying with these criteria are compared with the economic criteria of cost minimization (levelized cost of electricity, LCOE) and profit maximization (net present value, NPV), respectively, revealing differences that vary in intensity, particularly depending on the type of reservoir and their respective properties. For the two case studies, LCOE of 2.9 €-ct/kWh and 16.9 €-ct/kWh are found, which are further scrutinized by a detailed sensitivity analysis. The hydrothermal system, in contrast to the petrothermal system investigated, is found to be able to meet several of the sustainability criteria examined (extraction equals recharge, operating lifetime of 100 to 300 years), whereas economically optimal operation leads to excessive overexploitation in both cases, showing a distinct trade-off between profit maximization and sustainable operation that has not been discussed in the literature so far.

An amendment to this paper has been published and can be accessed via the original article.

Although geothermal resources are practically independent of climate factors, those factors significantly condition the potential use of the Earth’s natural heat resources. Unlike all the other factors limiting or facilitating the use of geothermal heat (like receivers’ temperature expectation, financial issues or local regulations), climate factors remain immovable. Thus, climate remains the main factor influencing the effective use of geothermal resources. Volumes of sold energy, typical capacity factors and rapid changes in heat demand may all influence the financial and technological performance of an investment. In the current paper, climate factors are translated into heat demand based on historical data (meteorological and district heating logs) by means of a dedicated artificial neural network, and analysed in terms of possible constraints and facilitators that might affect the effective use of geothermal energy. The results of ANN simulation indicate that average and typical operation is expected without any turbulences, yet about 10% of operating hours may require additional technical measures, like peak source support, smart management and buffers in order to limit pumping ramp rate. With appropriate dimensioning and exploitation, capacity factors as high as 60% are available, proving the potential for financially and environmentally effective use of geothermal resources.

A geochemometric study based on a multi-criteria decision analysis was applied, for the first time, for the optimal evaluation and selection of artificial neural networks, and the prediction of geothermal reservoir temperatures. Eight new gas geothermometers (GasG1 to GasG8) were derived from this study. For an effective and practical application of these geothermometers, a new computer program GaS_GeoT was developed. The prediction efficiency of the new geothermometers was compared with temperature estimates inferred from twenty-five existing geothermometers using gas-phase compositions of fluids from liquid- (LIQDR) and vapour-dominated (VAPDR) reservoirs. After applying evaluation statistical metrics (DIFF%, RMSE, MAE, MAPE, and the Theil's U test) to the temperature estimates obtained by using all the geothermometers, the following inferences were accomplished: (1) the new eight gas geothermometers (GasG1 to GasG8) provided reliable and systematic temperature estimates with performance wise occupying the first eight positions for LIQDR; (2) the GasG3 and GasG1 geothermometers exhibited consistency as the best predictor models by occupying the first two positions over all the geothermometers for VAPDR; (3) the GasG3 geothermometer exhibited a wider applicability, and a better prediction efficiency over all geothermometers in terms of a large number of samples used (up to 96% and 85% for LIQDR and VAPDR, respectively), and showed the smallest differences between predicted and measured temperatures in VAPDR and LIQDR; and lastly (4) for the VAPDR, the existing geothermometers ND84c, A98c, and ND98b sometimes showed a better prediction than some of the new gas geothermometers, except for GasG3 and GasG1. These results indicate that the new gas geothermometers may have the potential to become one of the most preferred tools for the estimation of the reservoir temperatures in geothermal systems.