Lithium (Li) is considered a crucial element for energy transition due to its current irreplaceability in Li-ion batteries, particularly in electric vehicles. Market analysis indicates that Germany’s future automotive sector and planned battery cell production will necessitate significant quantities of global lithium production. At the same time, only 1% of the world’s Li production is currently sourced from Europe. Recently, geothermal brines in Germany have gained attention as a potential local raw material source. These brines exhibit elevated Li concentrations and substantial flow rates in geothermal plants, suggesting the possibility of viable local production. However, a comprehensive full-scale Li extraction process from geothermal brines is yet to be established, and uncertainties persist regarding its long-term behavior. To address this, a generic model based on the geothermal settings of the Upper Rhine Graben was developed, simulating a 30-year operational period for Li extraction. The simulation revealed a 40% depletion of lithium during the observation period, while heat production remained constant. Nonetheless, the model also demonstrated a mean Li production of 231 t per year (equivalent to 1230 t per year of lithium carbonate equivalent), which could significantly enhance the economic prospects of a geothermal power plant and, if applied to multiple plants, reduce Germany’s dependence on global lithium imports. The primary factor influencing productivity is the achievable flow rate, as it directly impacts access to the raw material, hence, emphasizing the importance of detailed reservoir exploration and development in optimizing future lithium production from geothermal brines.

Regarding disadvantages of fossil fuels, renewables like geothermals can be an eco-friendly source of energy. In Iran, the availability of fossil fuels and poor policies surrounding subsidies (ranked as the first in giving subsidies) caused high energy consumption (1.75 times higher than the global average). Energy is mainly provided by fossil fuels that leads to high CO₂ emission. This study evaluates the energy consumption trend and potentials of more sustainable resources like geothermals in Iran. The formation of geothermals is tightly linked with geological prerequisites that are partly present within Iran. Adjacency of the metamorphic with volcanic zones, existence of numerous faults and seismic activity of Iran are notable geological characteristics confirming the geothermal potential. In Iran, 18 regions are being explored as the most promising geothermal prospects. To test the potentials of one of these regions, a geothermal power plant with a capacity of 5 MWe is installed in the Sabalan Field. Northwest (where Sabalan Field is located), central (like Mahalat Region) and southeast of Iran (Makran Zone) can be regarded as promising zones for hosting geothermal prospects.

Mahallat Geothermal Region, located in the central part of Iran, is known as one of the largest low-temperature geothermal fields. In this study, Mahallat geothermal resource has been evaluated based on integrated geological, geochemical and geophysical analyses. Gravity data revealed three major negative anomaly zones. Based on the geochemical analyses, quartz geothermometers are more reliable than others and confirmed that the reservoir is about 90 °C. Lithological properties of Jurassic layers and high sulphate content observed in geochemical data showed traces of the coal-rich layers on the hot fluids. Measured temperatures in 7 boreholes with the depths ranging from 50 to 100 m, have proposed that expected geothermal gradient will be about 81.5 °C/km. Among all drilled boreholes, the data coming from only one resulted in this almost reliable gradient. Other boreholes are clearly too shallow or affected by upflow or downflow of water along existing faults. Geological, geochemical, gravity and measurements of drilled boreholes suggested the existence of a shallow reservoir with an approximate temperature of 90 °C. Regarding gravity and observed faults, geothermal reservoir is elongated parallel to one of the main faults of the region with NE-SW strike.

Evaluating uncertainties of geological features on fluid temperature and pressure changes in a reservoir plays a crucial role in the safe and sustainable operation of high-temperature aquifer thermal energy storage (HT-ATES). This study introduces a new automated surface fitting function in the Python API (application programming interface) of Gmsh (v4.11) to simulate the impacts of structural barriers and variations of the reservoir geometries on thermohydraulic behaviour in heat storage applications. These structural features cannot always be detected by geophysical exploration but can be present due to geological complexities. A Python workflow is developed to implement an automated mesh generation routine for various geological scenarios. This way, complex geological models and their inherent uncertainties are transferred into reservoir simulations. The developed meshing workflow is applied to two case studies: (1) Greater Geneva Basin with the Upper Jurassic (“Malm”) limestone reservoir and (2) the 5° eastward-tilted DeepStor sandstone reservoir in the Upper Rhine Graben with a uniform thickness of 10 m. In the Greater Geneva Basin example, the top and bottom surfaces of the reservoir are randomly varied by ± 10 and ± 15 m, generating a total variation of up to 25 % from the initially assumed 100 m reservoir thickness. The injected heat plume in this limestone reservoir is independent of the reservoir geometry variation, indicating the limited propagation of the induced thermal signal. In the DeepStor reservoir, a vertical sub-seismic fault juxtaposing the permeable sandstone layers against low permeable clay-marl units is added to the base case model. The fault is located in distances varying from 4 to 118 m to the well to quantify the possible thermohydraulic response within the model. The variation in the distance between the fault and the well resulted in an insignificant change in the thermal recovery (∼ 1.5 %) but up to a ∼ 10.0 % pressure increase for the (shortest) distance of 4 m from the injection well. Modelling the pressure and temperature distribution in the 5° tilted reservoir, with a well placed in the centre of the model, reveals that heat tends to accumulate in the updip direction, while pressure increases in the downdip direction.

The growing interest in raw material extraction, particularly in trace elements, highlights the need for innovative geochemical modeling techniques to predict element concentrations accurately. This paper explores the predictive capabilities of a deep neural network (DNN) in estimating the concentrations of 20 trace elements based on 11 major elements and pH values. Using data from the BrineMine project, we applied DNNs to a challenging dataset characterized by a small sample size and imbalanced distributions. In total, 1000 independent DNN models were generated to address prediction accuracy and uncertainty instead of relying on a single model. Two preprocessing methods, including synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) statistical transformation, were applied to improve the accuracy and decrease uncertainty further. Despite issues such as low initial correlations between input features and target variables, imbalanced data distributions, and extremely low concentrations, the DNN models provided reliable and robust results, except for Cu and V. For 13 trace elements, the DNN models achieved acceptable reliability with R ² > 0.8. Analyzing the weight distribution of the DNN revealed that input features with high cross-correlation are prone to sharing the same information. While input features such as Fe, pH, and Mg are highly correlated to several target variables, accumulated local effects (ALE) scores indicate that Li has the highest influence, as it is the only input feature with a high correlation coefficient to some of the target variables.