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.
Highlights
Geothermal energy has emerged as a clean alternative source of renewable energy for electric power generation and other direct uses (Wu and Li 2020)
By considering the gas-phase compositions and the Bottom-hole temperatures (BHTm) measurements compiled in the new Worldwide Geochemical Database (NWGDB), thirteen geothermal fields of the world (Berlin, Zunil, Krafla, Kamojang, Sibayak, Amiata, Larderello, Olkaria, Cerro Prieto, Las Tres Virgenes, Los Azufres, Los Humeros, and Palinpinon) were used for the geochemometric evaluation
The geothermal reservoir temperatures estimated by applying the new gas geothermometers (GaS_GeoT) along with twenty-five existing geothermometers are reported in Additional file 1: Tables S6 and S7, respectively
Summary
Geothermal energy has emerged as a clean alternative source of renewable energy for electric power generation and other direct uses (Wu and Li 2020). The chemical composition of liquid and steam (gas) phases of geothermal fluids provides useful information on hydrogeological processes, thermal and recharge conditions of reservoirs, and underground flow patterns (Nicholson 1993). Within these applications, the reliable estimation of reservoir temperatures is a crucial task to evaluate the energy potential of geothermal resources (Gutiérrez-Negrín 2019). The reliable estimation of reservoir temperatures is a crucial task to evaluate the energy potential of geothermal resources (Gutiérrez-Negrín 2019) To carry out this task, several chemical geothermometers have been proposed for the prediction of deep equilibrium temperatures in geothermal systems (Guo et al 2017). Solute geothermometers are mostly recommended for the prediction of reservoir temperatures in liquid-dominated reservoirs, LIQDR (Verma et al 2008), whereas gas geothermometers are predominantly suggested for the calculation of reservoir temperatures in vapour-dominated (VAPDR) reservoirs (García-López et al 2014)
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