Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure
Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $...
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| Autor*in: |
Shokir, Eissa M. El-M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Pleiades Publishing, Ltd. 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Petroleum chemistry - Nauka/Interperiodica, 1962, 47(2007), 5 vom: Sept., Seite 368-376 |
|---|---|
| Übergeordnetes Werk: |
volume:47 ; year:2007 ; number:5 ; month:09 ; pages:368-376 |
| Links: |
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| DOI / URN: |
10.1134/S0965544107050106 |
|---|
| Katalog-ID: |
OLC2042193216 |
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| 520 | |a Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). | ||
| 650 | 4 | |a Critical Temperature | |
| 650 | 4 | |a Petroleum Chemistry | |
| 650 | 4 | |a Average Relative Error | |
| 650 | 4 | |a Reservoir Temperature | |
| 650 | 4 | |a Optimal Transformation | |
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10.1134/S0965544107050106 doi (DE-627)OLC2042193216 (DE-He213)S0965544107050106-p DE-627 ger DE-627 rakwb eng 690 VZ Shokir, Eissa M. El-M. verfasserin aut Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2007 Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). Critical Temperature Petroleum Chemistry Average Relative Error Reservoir Temperature Optimal Transformation Enthalten in Petroleum chemistry Nauka/Interperiodica, 1962 47(2007), 5 vom: Sept., Seite 368-376 (DE-627)129598399 (DE-600)241025-4 (DE-576)015091651 0965-5441 nnns volume:47 year:2007 number:5 month:09 pages:368-376 https://doi.org/10.1134/S0965544107050106 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 47 2007 5 09 368-376 |
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10.1134/S0965544107050106 doi (DE-627)OLC2042193216 (DE-He213)S0965544107050106-p DE-627 ger DE-627 rakwb eng 690 VZ Shokir, Eissa M. El-M. verfasserin aut Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2007 Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). Critical Temperature Petroleum Chemistry Average Relative Error Reservoir Temperature Optimal Transformation Enthalten in Petroleum chemistry Nauka/Interperiodica, 1962 47(2007), 5 vom: Sept., Seite 368-376 (DE-627)129598399 (DE-600)241025-4 (DE-576)015091651 0965-5441 nnns volume:47 year:2007 number:5 month:09 pages:368-376 https://doi.org/10.1134/S0965544107050106 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 47 2007 5 09 368-376 |
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10.1134/S0965544107050106 doi (DE-627)OLC2042193216 (DE-He213)S0965544107050106-p DE-627 ger DE-627 rakwb eng 690 VZ Shokir, Eissa M. El-M. verfasserin aut Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2007 Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). Critical Temperature Petroleum Chemistry Average Relative Error Reservoir Temperature Optimal Transformation Enthalten in Petroleum chemistry Nauka/Interperiodica, 1962 47(2007), 5 vom: Sept., Seite 368-376 (DE-627)129598399 (DE-600)241025-4 (DE-576)015091651 0965-5441 nnns volume:47 year:2007 number:5 month:09 pages:368-376 https://doi.org/10.1134/S0965544107050106 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 47 2007 5 09 368-376 |
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10.1134/S0965544107050106 doi (DE-627)OLC2042193216 (DE-He213)S0965544107050106-p DE-627 ger DE-627 rakwb eng 690 VZ Shokir, Eissa M. El-M. verfasserin aut Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2007 Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). Critical Temperature Petroleum Chemistry Average Relative Error Reservoir Temperature Optimal Transformation Enthalten in Petroleum chemistry Nauka/Interperiodica, 1962 47(2007), 5 vom: Sept., Seite 368-376 (DE-627)129598399 (DE-600)241025-4 (DE-576)015091651 0965-5441 nnns volume:47 year:2007 number:5 month:09 pages:368-376 https://doi.org/10.1134/S0965544107050106 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 47 2007 5 09 368-376 |
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10.1134/S0965544107050106 doi (DE-627)OLC2042193216 (DE-He213)S0965544107050106-p DE-627 ger DE-627 rakwb eng 690 VZ Shokir, Eissa M. El-M. verfasserin aut Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Pleiades Publishing, Ltd. 2007 Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). Critical Temperature Petroleum Chemistry Average Relative Error Reservoir Temperature Optimal Transformation Enthalten in Petroleum chemistry Nauka/Interperiodica, 1962 47(2007), 5 vom: Sept., Seite 368-376 (DE-627)129598399 (DE-600)241025-4 (DE-576)015091651 0965-5441 nnns volume:47 year:2007 number:5 month:09 pages:368-376 https://doi.org/10.1134/S0965544107050106 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 AR 47 2007 5 09 368-376 |
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Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure |
| abstract |
Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). © Pleiades Publishing, Ltd. 2007 |
| abstractGer |
Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). © Pleiades Publishing, Ltd. 2007 |
| abstract_unstemmed |
Abstract The $ CO_{2} $—oil minimum miscibility pressure (MMP) is an important parameter for screening and selecting reservoirs for $ CO_{2} $ injection projects. For the highest recovery, a candidate reservoir must be capable of withstanding an average reservoir pressure greater than the $ CO_{2} $—oil MMP. Knowledge of the $ CO_{2} $—oil MMP is also important when selecting a model to predict or simulate reservoir performance as a result of $ CO_{2} $ injection. This paper, presents a new alternating conditional expectation “ACE”-based model for estimating $ CO_{2} $—oil MMP. The ACE algorithm estimates the optimal transformation that maximizes the correlation between the transformed dependent variable “$ CO_{2} $—oil MMP” and the sum of the transformed independent variables that represent reservoir temperature and different components of oil composition. Predicted values of the $ CO_{2} $—oil MMP from the developed ACE-based model were compared with the experimental and calculated values from the most common correlations reported in the literature for $ CO_{2} $—oil MMP prediction. The results showed that the ACE-based model is superior to other commonly used correlations. Regarding other correlations, the ACE-based model yielded the highest correlation coefficient (0.9878), the lowest average relative error (0.7428%), and the lowest standard deviation of error (1.2265). © Pleiades Publishing, Ltd. 2007 |
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| container_issue |
5 |
| title_short |
Precise model for estimating $ CO_{2} $—oil minimum miscibility pressure |
| url |
https://doi.org/10.1134/S0965544107050106 |
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| doi_str |
10.1134/S0965544107050106 |
| up_date |
2024-07-03T14:10:33.359Z |
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