Research on geothermal reservoir temperature estimation based on silica-enthalpy models and development of application software

The silica-enthalpy model is an important method for estimating reservoir temperatures in geothermal systems; however, its traditional implementations have limitations. The equation-based method involves complex solving processes, while the graphical method relies heavily on manual interpretation and plotting. These approaches result in limited computational efficiency and reproducibility, as well as the potential introduction of subjective errors. To address these challenges, this study developed a “Silica Enthalpy Solver” based on Python. By integrating numerical algorithms such as Vandermonde matrix interpolation, the Newton‑Raphson iteration method, and the bisection method, the software achieves full automation in both calculation and visualization for the equation-based and graphical approaches. Case validation demonstrates that the software's results are in excellent agreement with literature reference values (R2 > 0.99 for reservoir temperatures). It also exhibits high efficiency in processing large datasets, with computational speeds reaching approximately 560 sets per second for the equation method and 220 sets per second for the graphical method. Moreover, the software supports uniform sampling of parameters within reasonable ranges, enabling quantitative assessment of uncertainties in reservoir temperature and cold-water mixing ratio estimates. By integrating multiple models and high‑precision numerical algorithms, the software reduces reliance on user expertise and significantly improves the reproducibility and reliability of reservoir temperature estimates. This study provides an efficient, reliable, and user‑friendly tool that overcomes the bottlenecks of traditional reservoir temperature estimation methods, thereby contributing to more refined evaluation and development of geothermal resources.