Optimised multicomponent geothermometer MulT_predict

Geothermometry is used for reservoir temperature estimation since the 1960s. Different kind of solute, gas, and isotopic geothermometers has been evolved and further developed. We are focusing on solute geothermometery, using the multicomponent approach by Reed and Spycher (1984) and combined it with an optimisation process suggested by Nitschke et al. (2017). Therefore, IPhreeqC by Parkhurst and Appelo (2013) and Matlab were coupled. Thus, the geochemical output of IPhreeqC is numerically evaluated and optimised with Matlab. Sensitive parameters, e.g. pH-value, and aluminium concentration etc. are varied simultaneously to minimise the temperature difference between multiple mineral phases, used as geothermometer. MulT_predict with its implemented optimisation leads to more accurate temperature estimations with lesser variance of error

MulT_predict - An optimised comprehensive multicomponent geothermometer

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

MulT_predict - An optimised multicomponent geothermometer

For a successful geothermal reservoir exploration, an in-situ temperature estimation is essential. Since geothermometric reservoir temperature estimations using conventional solute geothermometers often entail high uncertainties, a new computational approach is proposed. The goal was to obtain high-accuracy multicomponent reservoir temperature estimations by only using standard geochemical data without the need of sophisticated gas analysis. Therefore, the new numerical tool MulT_predict is introduced. MulT_predict is a multicomponent geothermometer code with integrated sensitivity analyses to back calculate on in-situ conditions. The script is based on MATLAB, which interacts with IPhreeqc. The tool was calibrated and validated against in-situ reservoir temperature measurements in Iceland. Hence, reservoir conditions are numerically reconstructed by varying various sensitive parameters (e.g. pH value, steam loss, aluminum concentration etc.) to reduce the uncertainties of the reservoir temperature estimation. The new method led to statistically robust and precise reservoir temperature estimations. To apply MulT_predict on a new geological site, a set of reservoir specific minerals for the Upper Rhine Graben is developed as the base of the multicomponent geothermometer. While calculating the saturations indices of the mineral phases over a defined temperature range, sensitive parameters are subsequently varied. As pH, aluminum concentration and redox potential are prone to interferences (e.g. measurement errors, secondary processes, etc.) as well as possible phase segregation due to boiling or mixing processes during the fluid ascent, reservoir conditions are numerically reconstructed to reduce the temperature estimation uncertainties. The variation of sensitive parameters minimizes the spread between the calculated temperature estimations of each selected mineral phase. The minimal range within the temperature estimations reflects the most plausible reservoir conditions. In this case, the geochemical equilibrium between mineral phases and the reservoir rock is reconstructed. The reservoir temperature estimations mostly fit the in-situ temperature measurements. Therefore, spatial and temporal changes in the borehole can be determined and investigated

Deep learning and geochemical modelling as tools for solute geothermometry

Geothermometry is constituted as one of the most important geochemical tools for reservoir exploration and development. Solute geothermometers are used to estimate the temperature in the subsurface. Therefore, the chemical composition of a discharging geothermal fluid is used to infer the temperature of the reservoir. Conventional solute geothermometers are using element ration or single mineral phases for reservoir temperature estimation. These geothermometers have large uncertainties applying them on the same fluid sample. Therefore, a new method called multicomponent geothermometry was introduced. Assuming the reservoir rock and the geothermal fluid are in equilibrium, the mineral assemblage of the reservoir rock is used to determine the temperature within the reservoir. Thus, the temperature-dependent saturation indices of the mineral set are calculated. Utilising multiple mineral phases for temperature estimation leads to a statistically more robust and precise result than the conventional method. Nevertheless, secondary processes such as boiling, degassing, and dilution are disturbing the equilibrium reaction within the fluid. Hence, we developed a solute multicomponent geothermometer with an additional optimisation process. Geochemical modelling improves the methodology. Several interdependent, vulnerable parameters especially pH value, aluminium concentration, as well as boiling, and dilution are considered. These parameters are elaborated in the geochemical modelling process to optimise these values to fit their in-situ reservoir conditions again. Therefore, the uncertainty within the temperature estimation is furthermore reduced. However, this process is time-consuming, and geochemical as well as mineralogical knowledge would be beneficial for applying the method. On the other hand, artificial intelligence is a powerful tool to solve complex issues, even considering unknowns. Therefore, we developed a new adequate solute geothermometer based on an artificial neuronal network. Selected geochemical fluid parameters are used as input parameters. Further on, the net was trained and validated with a high-quality dataset. The best performing net was tested and compared to conventional and multicomponent geothermometers as well as in-situ temperature measurements of geothermal wells. Concluding in a neuronal solute geothermometer as precise as our optimised multicomponent geothermometer but much easier and faster in its applicability

AnnRG - An artificial neural network solute geothermometer

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

A multicomponent geothermometer for high-temperature basalt settings

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

Zeitliche und tiefenabhängige Effekte auf die Fluidchemie - Untersuchung granitischer Reservoire im Oberrheingraben mittels Multikomponentengeothermometrie

Die Anwendung von Multikomponentengeothermometrie wird seit Jahren zur geothermischen Reservoirtemperaturabschätzung genutzt. Das eigenentwickelte Tool „MulT_predict“ nutzt die Löslichkeit reservoirspezifischer Mineralphasen, sowie eine implementierte Sensitivitätsanalyse zur Temperaturabschätzung des Reservoirs. Für die Anwendung auf geothermale Tiefenwässer im Oberrheingraben wurde ein granitischer Mineralsatz ausgearbeitet und auf geothermale Standrote mit granitischen Reservoiren angewendet. Dabei stimmen die modellierten Reservoirtemperaturen mit den in-situ-Temperaturmessungen der einzelnen Geothermiefelder überein. Darüber hinaus lassen sich sowohl zeitliche als auch tiefenabhängige Effekte in den Geothermometerrechnungen für Soultz-sous-Forêts und Rittershoffen zeigen. Zeitliche Veränderungen in der Reservoirtemperatur sind zum einen in kurzzeitigen Produktions- und Tracertests sowie zum anderen über Jahre des Betriebs erkennbar. Ebenfalls können die Vertiefungen von GPK1 und GPK2 dargestellt werden. Diese Empfindlichkeit von MulT_predict gegenüber konventionellen Geothermometern könnten zukünftig zum Monitoring von Bohrungen während der Produktion genutzt werden. Unter den möglichen Anwendungsbereichen könnten einerseits die Überwachung des Reservoirs vor Auskühlen oder andererseits Veränderung der Hauptzuflüsse durch Leckagen o.ä. fallen