GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures

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 fr...
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Autor*in:	A. Acevedo-Anicasio [verfasserIn] E. Santoyo [verfasserIn] D. Pérez-Zárate [verfasserIn] Kailasa Pandarinath [verfasserIn] M. Guevara [verfasserIn] L. Díaz-González [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence

Übergeordnetes Werk:	In: Geothermal Energy - SpringerOpen, 2014, 9(2021), 1, Seite 41
Übergeordnetes Werk:	volume:9 ; year:2021 ; number:1 ; pages:41

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s40517-020-00182-9

Katalog-ID:	DOAJ049964968

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allfields	10.1186/s40517-020-00182-9 doi (DE-627)DOAJ049964968 (DE-599)DOAJ767f95e85c0d48de9c3f0f36a48ff44e DE-627 ger DE-627 rakwb eng TJ807-830 QE1-996.5 A. Acevedo-Anicasio verfasserin aut GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence Renewable energy sources Geology E. Santoyo verfasserin aut D. Pérez-Zárate verfasserin aut Kailasa Pandarinath verfasserin aut M. Guevara verfasserin aut L. Díaz-González verfasserin aut In Geothermal Energy SpringerOpen, 2014 9(2021), 1, Seite 41 (DE-627)749499893 (DE-600)2718871-1 21959706 nnns volume:9 year:2021 number:1 pages:41 https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/article/767f95e85c0d48de9c3f0f36a48ff44e kostenfrei https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/toc/2195-9706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 1 41
spelling	10.1186/s40517-020-00182-9 doi (DE-627)DOAJ049964968 (DE-599)DOAJ767f95e85c0d48de9c3f0f36a48ff44e DE-627 ger DE-627 rakwb eng TJ807-830 QE1-996.5 A. Acevedo-Anicasio verfasserin aut GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence Renewable energy sources Geology E. Santoyo verfasserin aut D. Pérez-Zárate verfasserin aut Kailasa Pandarinath verfasserin aut M. Guevara verfasserin aut L. Díaz-González verfasserin aut In Geothermal Energy SpringerOpen, 2014 9(2021), 1, Seite 41 (DE-627)749499893 (DE-600)2718871-1 21959706 nnns volume:9 year:2021 number:1 pages:41 https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/article/767f95e85c0d48de9c3f0f36a48ff44e kostenfrei https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/toc/2195-9706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 1 41
allfields_unstemmed	10.1186/s40517-020-00182-9 doi (DE-627)DOAJ049964968 (DE-599)DOAJ767f95e85c0d48de9c3f0f36a48ff44e DE-627 ger DE-627 rakwb eng TJ807-830 QE1-996.5 A. Acevedo-Anicasio verfasserin aut GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence Renewable energy sources Geology E. Santoyo verfasserin aut D. Pérez-Zárate verfasserin aut Kailasa Pandarinath verfasserin aut M. Guevara verfasserin aut L. Díaz-González verfasserin aut In Geothermal Energy SpringerOpen, 2014 9(2021), 1, Seite 41 (DE-627)749499893 (DE-600)2718871-1 21959706 nnns volume:9 year:2021 number:1 pages:41 https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/article/767f95e85c0d48de9c3f0f36a48ff44e kostenfrei https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/toc/2195-9706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 1 41
allfieldsGer	10.1186/s40517-020-00182-9 doi (DE-627)DOAJ049964968 (DE-599)DOAJ767f95e85c0d48de9c3f0f36a48ff44e DE-627 ger DE-627 rakwb eng TJ807-830 QE1-996.5 A. Acevedo-Anicasio verfasserin aut GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence Renewable energy sources Geology E. Santoyo verfasserin aut D. Pérez-Zárate verfasserin aut Kailasa Pandarinath verfasserin aut M. Guevara verfasserin aut L. Díaz-González verfasserin aut In Geothermal Energy SpringerOpen, 2014 9(2021), 1, Seite 41 (DE-627)749499893 (DE-600)2718871-1 21959706 nnns volume:9 year:2021 number:1 pages:41 https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/article/767f95e85c0d48de9c3f0f36a48ff44e kostenfrei https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/toc/2195-9706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 1 41
allfieldsSound	10.1186/s40517-020-00182-9 doi (DE-627)DOAJ049964968 (DE-599)DOAJ767f95e85c0d48de9c3f0f36a48ff44e DE-627 ger DE-627 rakwb eng TJ807-830 QE1-996.5 A. Acevedo-Anicasio verfasserin aut GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence Renewable energy sources Geology E. Santoyo verfasserin aut D. Pérez-Zárate verfasserin aut Kailasa Pandarinath verfasserin aut M. Guevara verfasserin aut L. Díaz-González verfasserin aut In Geothermal Energy SpringerOpen, 2014 9(2021), 1, Seite 41 (DE-627)749499893 (DE-600)2718871-1 21959706 nnns volume:9 year:2021 number:1 pages:41 https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/article/767f95e85c0d48de9c3f0f36a48ff44e kostenfrei https://doi.org/10.1186/s40517-020-00182-9 kostenfrei https://doaj.org/toc/2195-9706 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 1 41
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source	In Geothermal Energy 9(2021), 1, Seite 41 volume:9 year:2021 number:1 pages:41
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topic_title	TJ807-830 QE1-996.5 GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures Geothermal energy Renewable energy Geochemometrics Fluid geochemistry Artificial intelligence
topic	misc TJ807-830 misc QE1-996.5 misc Geothermal energy misc Renewable energy misc Geochemometrics misc Fluid geochemistry misc Artificial intelligence misc Renewable energy sources misc Geology
topic_unstemmed	misc TJ807-830 misc QE1-996.5 misc Geothermal energy misc Renewable energy misc Geochemometrics misc Fluid geochemistry misc Artificial intelligence misc Renewable energy sources misc Geology
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title	GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures
ctrlnum	(DE-627)DOAJ049964968 (DE-599)DOAJ767f95e85c0d48de9c3f0f36a48ff44e
title_full	GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures
author_sort	A. Acevedo-Anicasio
journal	Geothermal Energy
journalStr	Geothermal Energy
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author_browse	A. Acevedo-Anicasio E. Santoyo D. Pérez-Zárate Kailasa Pandarinath M. Guevara L. Díaz-González
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author-letter	A. Acevedo-Anicasio
doi_str_mv	10.1186/s40517-020-00182-9
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title_sort	gas_geot: a computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures
callnumber	TJ807-830
title_auth	GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	GaS_GeoT: A computer program for an effective use of newly improved gas geothermometers in predicting reliable geothermal reservoir temperatures
url	https://doi.org/10.1186/s40517-020-00182-9 https://doaj.org/article/767f95e85c0d48de9c3f0f36a48ff44e https://doaj.org/toc/2195-9706
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author2	E. Santoyo D. Pérez-Zárate Kailasa Pandarinath M. Guevara L. Díaz-González
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