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.

中文翻译：

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用于高温玄武岩设置的多组分地热仪

为了成功进行地热储层勘探，准确的温度估算至关重要。由于在使用常规溶质地热仪时，储层温度估算经常涉及高度不确定性，因此提出了一种新的统计方法。这项研究的重点是开发一种新的多组分地热仪工具，与现有方法相比，该工具所需的数据集大大减少了。该方法针对Krafla和Reykjanes地热系统中的储层温度测量进行了验证。选择特定地点的玄武岩矿物集作为计算平衡温度的基础。这些高焓地热库位于冰岛的新火山区，在该处流体温度在2000 m的深度可达350°C。在上升过程中，由于减压和沸腾，流体成分容易发生变化以及可能发生相分离。因此，为了减少温度估算的不确定性，考虑了pH，铝浓度和蒸汽损失，通过敏感性分析对储层温度条件进行了数值重建。地球化学数据的评估和敏感性分析是通过称为MulT_predict的内部数字工具计算的。在所有情况下，温度估算值均与在Krafla和Reykjanes测得的现场温度相匹配。该方法的发展趋向于成为用于储层温度估算的有前途和精确的工具。