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
