The application of geothermometry has been used for the last six decades for geothermal reservoir temperature estimation. A steady evolution of conventional geothermometers to multicomponent tools as well as application of artificial intelligence are nowadays available.
The development of high-performing computers offers the possibility to use deep learning algorithm for reservoir temperature estimation. Serving a selection of geochemical input parameters to artificial neural networks, they can be used to predict temperatures in the subsurface. Therefore, the chemical composition of the geothermal fluids are required. Main cations and anions as well as the SiO2 concentration and the pH value serve as these input parameters. Using the data of well-studied geothermal systems, the neurons within the layers of the neural network are linked and weighted. Thus, the newly developed artificial intelligence is trained and validated. As a result, the modelled reservoir temperatures match with the in-situ temperature measurements of the analysed geothermal fields. Contrary to the usage of conventional geothermometers, the application of artificial neural networks are a useful novelty. While dealing with large amounts of data, artificial neural networks are faster, more easy-to-handle, as well as higher in accuracy.