Abstract
The inaccessibility of geological reservoirs, both for oil and gas production or geothermal usage, makes detection of reservoir properties and conditions a key problem in the field of ...reservoir engineering, including for the development of geothermal power plants. Herein, an approach is presented for the development of messenger nanoparticles for the determination of reservoir conditions, with a proof of concept example of temperature detection under controlled laboratory conditions. Silica particles are synthesized with a two-layer architecture, an inner enclosed core and an outer porous shell, each doped with a different fluorescent dye to create a dual emission system. Temperature detection happens by a threshold temperature-triggered irreversible release of the outer dye, thus changing the fluorescence signal of the particles. The reported particle system consequently enables a direct, reliable and fast way to determine reservoir temperature. It also displays a sharp threshold for accurate sensing and allows detection at concentration ranges as low as few nanograms of nanoparticles per milliliter.
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.
For successful geothermal reservoir exploration, accurate temperature estimation is essential. Since reservoir temperature estimation frequently involves high uncertainties when using conventional ...solute geothermometers, a new statistical approach is proposed. The focus of this study is on the development of a new multicomponent geothermometer tool which requires a significantly reduced data set compared to existing approaches. The method is validated against reservoir temperature measurements in the Krafla and the Reykjanes geothermal systems. A site-specific basaltic mineral set was selected as the basis to compute the equilibrium temperatures. These high-enthalpy geothermal reservoirs are located in the neo-volcanic zone of Iceland where the fluid temperatures are known to reach up to 350 °C at a depth of 2000 m. During ascent, the fluid composition is prone to changes as well as possible phase segregation due to depressurization and boiling. Therefore, to reduce the uncertainty of temperature estimations, reservoir temperature conditions are numerically reconstructed with sensitivity analyses considering pH, aluminium concentration, and steam loss. The evaluation of the geochemical data and the sensitivity analyses were calculated via a numerical in-house tool called MulT_predict. In all cases, the temperature estimations match with the in situ temperatures measured at Krafla and Reykjanes. The development of this method tends to be a promising and precise tool for reservoir temperature estimation. The developed methodology is a fast and easy-to-handle exploration tool that can be applied to standard geochemical data without the need for a sophisticated gas analysis yet obtaining very accurate results.
The quantification of fluid flow in rough fractures is of high interest for reservoir engineering, especially for deep geothermal applications. Herein, rough self-affine fractures are stochastically ...generated with incremental shear displacement and geometrically described by two aperture definitions, the vertical aperture
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vert
and the effective aperture
a
eff
. In order to compare their effect on fracture flow, such as anisotropy and channelling, Local Cubic Law (LCL) model-based 2D fluid flow is simulated. The particularity of this approach is the combination of a stochastic generation of self-affine fractures with a statistical analysis (560 individual realizations) of the impact of the LCL’s aperture constraint on fracture flow. The results show that aperture definition affects the quantitative interpretation of flow anisotropy and channeling as well as the aperture distribution of the fractures with shearing. Higher values of mean aperture for a given fracture are found using
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vert
, whereas the aperture standard deviation is larger with
a
eff
. In addition, flow anisotropy is significantly sensitive to aperture definition for small shear displacements and shows a relative higher dispersion with
a
eff
. Thus, LCL prediction models based on
a
vert
are expected to lead to higher dispersion of anisotropy results with a higher uncertainty (factor ~ 2). Realizations based on
a
vert
lead to an enhanced clustering of high flow rates for higher shearing displacements. This channeling development results in higher total flow rates for these simulations. These findings support the direct calibration of pre-existing LCL anisotropy simulations based on
a
vert
towards more representative results using
a
eff
.
Solute artificial neural network geothermometers offer the possibility to overcome the complexity given by the solute-mineral composition. Herein, we present a new concept, trained from high-quality ...hydrochemical data and verified by in-situ temperature measurements with a total of 208 data pairs of geochemical input parameters (Na+, K+, Ca2+, Mg2+, Cl−, SiO2, and pH) and reservoir temperature measurements being compiled. The data comprises nine geothermal sites with a broad variety of geochemical characteristics and enthalpies. Five sites with 163 samples (Upper Rhine Graben, Pannonian Basin, German Molasse Basin, Paris Basin, and Iceland) are used to develop the ANN geothermometer, while further four sites with 45 samples (Azores, El Tatio, Miavalles, and Rotorua) are used to encounter the established artificial neural network in practice to unknown data. The setup of the application, as well as the optimisation of the network architecture and its hyperparameters, are stepwise introduced. As a result, the solute ANN geothermometer, AnnRG (Artificial neural network Regression Geothermometer), provides precise reservoir temperature predictions (RMSE of 10.442 K) with a high prediction accuracy of R2 = 0.978. In conclusion, the implementation and verification of the first adequate ANN geothermometer is an advancement in solute geothermometry. Our approach is also a basis for further broadening and refining applications in geochemistry.
Temperature logs have important applications in the geothermal industry such as the estimation of the static formation temperature (SFT) and the characterization of fluid loss from a borehole. ...However, the temperature distribution of the wellbore relies on various factors such as wellbore flow conditions, fluid losses, well layout, heat transfer mechanics within the fluid as well as between the wellbore and the surrounding rock formation, etc. In this context, the numerical approach presented in this paper is applied to investigate the influencing parameters/uncertainties in the interpretation of borehole logging data. To this end, synthetic temperature logs representing different well operation conditions were numerically generated using our newly developed wellbore simulator. Our models account for several complex operation scenarios resulting from the requirements of high-enthalpy wells where different flow conditions, such as mud injection with- and without fluid loss and shut-in, occur in the drill string and the annulus. The simulation results reveal that free convective heat transfer plays an important role in the earlier evolution of the shut-in-time temperature; high accuracy SFT estimation is only possible when long-term shut-in measurements are used. Two other simulation scenarios for a well under injection conditions show that applying simple temperature correction methods on the non-shut-in temperature data could lead to large errors for SFT estimation even at very low injection flow rates. Furthermore, the magnitude of the temperature gradient increase depends on the flow rate, the percentage of fluid loss and the lateral heat transfer between the fluid and the rock formation. As indicated by this study, under low fluid losses (< 30%) or relatively higher flow rates (> 20 L/s), the impact of flow rate and the lateral heat transfer on the temperature gradient increase can be ignored. These results provide insights on the key factors influencing the well temperature distribution, which are important for the choice of the drilling data to estimate SFT and the design of the inverse modeling scheme in future studies to determine an accurate SFT profile for the high-enthalpy geothermal environment.
The determination of reservoir temperatures represents a major task when exploring geothermal systems. Since the uncertainties of classical solute geothermometry are still preventing reliable ...reservoir temperature estimations, we assess the performance of classical geothermometers and multicomponent geothermometry by applying them to fluids composed from long-term batch-type equilibration experiments and to fluids from natural geothermal springs in the Villarrica area, Southern Chile. The experiments, weathering two reservoir rock analogues from the Villarrica area, highlight a strong impact of reservoir rock composition on the fluid chemistry and, consequently, on calculated in situ temperatures. Especially temperatures calculated from classical solute geothermometry are strongly affected. Multicomponent geothermometry is obviously more robust and independent from rock composition leading to significantly smaller temperature spreads. In a sensitivity analysis, the dilution of geothermal fluid with surficial water, the pH and the aluminum concentration are anticipated to be the factors causing underestimations of reservoir temperatures. We quantify these parameters and correct the results to obtain realistic in situ conditions. Thus, enabling the application of the method also on basis of standard fluid analysis, our approach represents an easy-to-use modification of the original multicomponent geothermometry leading to very plausible subsurface temperatures with significantly low scattering.
•Comprehensive multicomponent geothermometer with integrated optimisation process for aluminium concentration, pH value, as well as boiling and dilution.•Development of a universally valid mineral ...set for worldwide applicability of mulT_predict.•Development of an outlier removal procedure for more precise temperature estimation.•Benchmarking the optimisation process of mulT_predict by perturbation of a synthetic brine.
In this study, we introduce MulT_predict as a fully integrated solute multicomponent geothermometer, combining numerical optimisation processes for sensitive parameters to back-calculate to chemical reservoir conditions. This results in a state of the art geothermometer, providing an accurate reservoir temperature estimation validated by geothermal borehole measurements on a worldwide scale. In addition, a universally valid mineral assemblage for an unknown reservoir composition is developed, focusing on worldwide applicability. Using the evolved methodology, the limits of the optimisation processes are determined by using a synthetic brine (150 °C, pH 6, aluminium concentration 0.003 mmol/l) and successively perturbing its geochemical equilibrium state. Individual back-calculation of reservoir conditions lead to valid temperature estimations of 145 °C, 3.4% lower than the initial temperature while a simultaneous and interdependent optimisation reconstructs the sensitive parameters even more precisely with a deviation of 0.056 for the initial pH value, and 0.164 µmol/l for the aluminium concentration.
Zusammenfassung
Die hier vorgestellte Arbeit liefert Hintergrundinformationen, um das Potenzial einer künftigen Lithiumproduktion aus geothermalen Fluiden in Deutschland abzuschätzen. Aus der ...wachsenden Nachfrage und der bisherigen Abhängigkeit von schlecht diversifizierten Überseequellen lässt sich eine hohe strategische Bedeutung einer möglichen Binnenquelle ableiten. Hinzu kommen ökologische Aspekte, wie CO
2
-ärmere und flächenschonendere Lithiumgewinnungsmethoden.
Basierend auf dem Technologievergleich zur direkten Lithiumextraktion aus geothermalen Fluiden und dem heutigen Ausbauzustand der Geothermie in Deutschland und dem französischen Teil des Oberrheingrabens wurden unterschiedliche Szenarien für die extrahierbare Menge an Lithiumkarbonat berechnet. So lässt sich im optimistischsten Szenario unter Berücksichtigung aller zurzeit aktiven Bohrungen eine maximale Produktion von 7200 t/a Lithiumkarbonat-Äquivalent prognostizieren. Damit könnten 5–19 % des jährlichen Bedarfs der geplanten deutschen Batteriezellenproduktion gedeckt werden.
Schlüsselparameter für das Prozessdesign sind der nutzbare Volumenanteil des geothermalen Fluids und die Extraktionseffizienz. Die Unsicherheiten in der Ressourcenbewertung bezüglich Größe und Nachhaltigkeit ihrer Bewirtschaftung sind bislang noch beachtlich. Um die großen Potenziale dieser Technologie nutzen zu können, müssen diese zentralen Fragen geklärt werden.
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