GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 3.1 GNGTS 2024 Saltelli, A., Rato, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitvity Analysis: The Primer . John Wiley & Sons. Shi, Y., Song, X., & Song, G. (2021). Productvity predicton of a multlateral-well geothermal system based on a long short-term memory and mult-layer perceptron combinatonal neural network. Applied Energy , 282 , 116046. htps://doi.org/10.1016/j.apenergy.2020.116046 Trumpy, E., & Manzella, A. (2017). Geothopica and the interactve analysis and visualizaton of the updated Italian Natonal Geothermal Database. Internatonal Journal of Applied Earth Observaton and Geoinformaton , 54 , 28–37. htps://doi.org/10.1016/j.jag.2016.09.004 ViDEPI . (s.d.). Recuperato 28 novembre 2023, da htps://www.videpi.com/videpi/pozzi/pozzi.asp Wang, N., Chang, H., Kong, X., Saar, M. O., & Zhang, D. (2022). Deep learning based closed-loop optmizaton of geothermal reservoir producton (arXiv:2204.08987). arXiv. htps://doi.org/ 10.48550/arXiv.2204.08987 Witer, J. B., Trainor-Guiton, W. J., & Siler, D. L. (2019). Uncertainty and risk evaluaton during the exploraton stage of geothermal development: A review. Geothermics , 78 , 233–242. htps:// doi.org/10.1016/j.geothermics.2018.12.011 Yuan, W., Chen, Z., Grasby, S. E., & Litle, E. (2021). Closed-loop geothermal energy recovery from deep high enthalpy systems. Renewable Energy , 177 , 976–991. htps://doi.org/10.1016/ j.renene.2021.06.028 Corresponding author: atlio.molossi@phd.units.it