Evaluation of artificial neural networks for the prediction of deep reservoir temperatures using the gas-phase composition of geothermal fluids

Three-layer artificial neural networks were used for the multivariate analysis of the gas-phase composition of fluids, and the prediction of geothermal reservoir temperatures. The major gas-phase composition of geothermal fluids (CO2, H2S, CH4, and H2) was defined as input variables whereas the measured bottomhole temperatures were used as output. Multivariate statistical analysis and log-ratio transformations were used for the normalization of...