| Summary: | Abstract A geochemometric study based on a multi-criteria decision analysis was applied, for the first time, for the optimal evaluation and selection of artificial neural networks, and the prediction of geothermal reservoir temperatures. Eight new gas geothermometers (GasG1 to GasG8) were derived from this study. For an effective and practical application of these geothermometers, a new computer program GaS_GeoT was developed. The prediction efficiency of the new geothermometers was compared with temperature estimates inferred from twenty-five existing geothermometers using gas-phase compositions of fluids from liquid- (LIQDR) and vapour-dominated (VAPDR) reservoirs. After applying evaluation statistical metrics (DIFF%, RMSE, MAE, MAPE, and the Theil's U test) to the temperature estimates obtained by using all the geothermometers, the following inferences were accomplished: (1) the new eight gas geothermometers (GasG1 to GasG8) provided reliable and systematic temperature estimates with performance wise occupying the first eight positions for LIQDR; (2) the GasG3 and GasG1 geothermometers exhibited consistency as the best predictor models by occupying the first two positions over all the geothermometers for VAPDR; (3) the GasG3 geothermometer exhibited a wider applicability, and a better prediction efficiency over all geothermometers in terms of a large number of samples used (up to 96% and 85% for LIQDR and VAPDR, respectively), and showed the smallest differences between predicted and measured temperatures in VAPDR and LIQDR; and lastly (4) for the VAPDR, the existing geothermometers ND84c, A98c, and ND98b sometimes showed a better prediction than some of the new gas geothermometers, except for GasG3 and GasG1. These results indicate that the new gas geothermometers may have the potential to become one of the most preferred tools for the estimation of the reservoir temperatures in geothermal systems.
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