Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation
Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathema...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sadi, Maryam [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Iterated Gilbert mosaics - Baccelli, Francois ELSEVIER, 2019, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:171 ; year:2018 ; pages:1211-1222 ; extent:12 |
| Links: |
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| DOI / URN: |
10.1016/j.petrol.2018.08.041 |
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ELV044422105 |
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| 245 | 1 | 0 | |a Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation |
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| 520 | |a Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. | ||
| 520 | |a Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. | ||
| 650 | 7 | |a SARA fractions |2 Elsevier | |
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| 650 | 7 | |a Group method of data handling |2 Elsevier | |
| 650 | 7 | |a Asphaltene precipitation |2 Elsevier | |
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10.1016/j.petrol.2018.08.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001028.pica (DE-627)ELV044422105 (ELSEVIER)S0920-4105(18)30712-5 DE-627 ger DE-627 rakwb eng 510 VZ 31.70 bkl Sadi, Maryam verfasserin aut Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation 2018transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. SARA fractions Elsevier Leverage approach Elsevier Genetic algorithm Elsevier Group method of data handling Elsevier Asphaltene precipitation Elsevier Shahrabadi, Abbas oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:171 year:2018 pages:1211-1222 extent:12 https://doi.org/10.1016/j.petrol.2018.08.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 171 2018 1211-1222 12 |
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10.1016/j.petrol.2018.08.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001028.pica (DE-627)ELV044422105 (ELSEVIER)S0920-4105(18)30712-5 DE-627 ger DE-627 rakwb eng 510 VZ 31.70 bkl Sadi, Maryam verfasserin aut Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation 2018transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. SARA fractions Elsevier Leverage approach Elsevier Genetic algorithm Elsevier Group method of data handling Elsevier Asphaltene precipitation Elsevier Shahrabadi, Abbas oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:171 year:2018 pages:1211-1222 extent:12 https://doi.org/10.1016/j.petrol.2018.08.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 171 2018 1211-1222 12 |
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10.1016/j.petrol.2018.08.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001028.pica (DE-627)ELV044422105 (ELSEVIER)S0920-4105(18)30712-5 DE-627 ger DE-627 rakwb eng 510 VZ 31.70 bkl Sadi, Maryam verfasserin aut Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation 2018transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. SARA fractions Elsevier Leverage approach Elsevier Genetic algorithm Elsevier Group method of data handling Elsevier Asphaltene precipitation Elsevier Shahrabadi, Abbas oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:171 year:2018 pages:1211-1222 extent:12 https://doi.org/10.1016/j.petrol.2018.08.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 171 2018 1211-1222 12 |
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10.1016/j.petrol.2018.08.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001028.pica (DE-627)ELV044422105 (ELSEVIER)S0920-4105(18)30712-5 DE-627 ger DE-627 rakwb eng 510 VZ 31.70 bkl Sadi, Maryam verfasserin aut Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation 2018transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. SARA fractions Elsevier Leverage approach Elsevier Genetic algorithm Elsevier Group method of data handling Elsevier Asphaltene precipitation Elsevier Shahrabadi, Abbas oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:171 year:2018 pages:1211-1222 extent:12 https://doi.org/10.1016/j.petrol.2018.08.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 171 2018 1211-1222 12 |
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10.1016/j.petrol.2018.08.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001028.pica (DE-627)ELV044422105 (ELSEVIER)S0920-4105(18)30712-5 DE-627 ger DE-627 rakwb eng 510 VZ 31.70 bkl Sadi, Maryam verfasserin aut Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation 2018transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. SARA fractions Elsevier Leverage approach Elsevier Genetic algorithm Elsevier Group method of data handling Elsevier Asphaltene precipitation Elsevier Shahrabadi, Abbas oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:171 year:2018 pages:1211-1222 extent:12 https://doi.org/10.1016/j.petrol.2018.08.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 171 2018 1211-1222 12 |
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evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation |
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Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation |
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Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. |
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Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. |
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Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively. |
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