A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs

Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kuang, Yan [verfasserIn] Sima, Liqiang [verfasserIn] Zhang, Zeyu [verfasserIn] Wang, Zhenlin [verfasserIn] Chen, Meng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Tight sandy conglomerate Nuclear magnetic resonance SDR model Saturation exponent Water-wet

Übergeordnetes Werk:	Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 43(2017), 11 vom: 18. Dez., Seite 6305-6313
Übergeordnetes Werk:	volume:43 ; year:2017 ; number:11 ; day:18 ; month:12 ; pages:6305-6313

Links:	Volltext

DOI / URN:	10.1007/s13369-017-3013-1

Katalog-ID:	SPR03197046X

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520			\|a Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs.
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700	1		\|a Chen, Meng \|e verfasserin \|4 aut
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author_variant	y k yk l s ls z z zz z w zw m c mc
matchkey_str	article:21914281:2017----::mdloetmtnteauainxoetaeonrnihsny
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allfields	10.1007/s13369-017-3013-1 doi (DE-627)SPR03197046X (SPR)s13369-017-3013-1-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Kuang, Yan verfasserin aut A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs. Tight sandy conglomerate (dpeaa)DE-He213 Nuclear magnetic resonance (dpeaa)DE-He213 SDR model (dpeaa)DE-He213 Saturation exponent (dpeaa)DE-He213 Water-wet (dpeaa)DE-He213 Sima, Liqiang verfasserin aut Zhang, Zeyu verfasserin aut Wang, Zhenlin verfasserin aut Chen, Meng verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 11 vom: 18. Dez., Seite 6305-6313 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313 https://dx.doi.org/10.1007/s13369-017-3013-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 11 18 12 6305-6313
spelling	10.1007/s13369-017-3013-1 doi (DE-627)SPR03197046X (SPR)s13369-017-3013-1-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Kuang, Yan verfasserin aut A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs. Tight sandy conglomerate (dpeaa)DE-He213 Nuclear magnetic resonance (dpeaa)DE-He213 SDR model (dpeaa)DE-He213 Saturation exponent (dpeaa)DE-He213 Water-wet (dpeaa)DE-He213 Sima, Liqiang verfasserin aut Zhang, Zeyu verfasserin aut Wang, Zhenlin verfasserin aut Chen, Meng verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 11 vom: 18. Dez., Seite 6305-6313 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313 https://dx.doi.org/10.1007/s13369-017-3013-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 11 18 12 6305-6313
allfields_unstemmed	10.1007/s13369-017-3013-1 doi (DE-627)SPR03197046X (SPR)s13369-017-3013-1-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Kuang, Yan verfasserin aut A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs. Tight sandy conglomerate (dpeaa)DE-He213 Nuclear magnetic resonance (dpeaa)DE-He213 SDR model (dpeaa)DE-He213 Saturation exponent (dpeaa)DE-He213 Water-wet (dpeaa)DE-He213 Sima, Liqiang verfasserin aut Zhang, Zeyu verfasserin aut Wang, Zhenlin verfasserin aut Chen, Meng verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 11 vom: 18. Dez., Seite 6305-6313 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313 https://dx.doi.org/10.1007/s13369-017-3013-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 11 18 12 6305-6313
allfieldsGer	10.1007/s13369-017-3013-1 doi (DE-627)SPR03197046X (SPR)s13369-017-3013-1-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Kuang, Yan verfasserin aut A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs. Tight sandy conglomerate (dpeaa)DE-He213 Nuclear magnetic resonance (dpeaa)DE-He213 SDR model (dpeaa)DE-He213 Saturation exponent (dpeaa)DE-He213 Water-wet (dpeaa)DE-He213 Sima, Liqiang verfasserin aut Zhang, Zeyu verfasserin aut Wang, Zhenlin verfasserin aut Chen, Meng verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 11 vom: 18. Dez., Seite 6305-6313 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313 https://dx.doi.org/10.1007/s13369-017-3013-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 11 18 12 6305-6313
allfieldsSound	10.1007/s13369-017-3013-1 doi (DE-627)SPR03197046X (SPR)s13369-017-3013-1-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Kuang, Yan verfasserin aut A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs. Tight sandy conglomerate (dpeaa)DE-He213 Nuclear magnetic resonance (dpeaa)DE-He213 SDR model (dpeaa)DE-He213 Saturation exponent (dpeaa)DE-He213 Water-wet (dpeaa)DE-He213 Sima, Liqiang verfasserin aut Zhang, Zeyu verfasserin aut Wang, Zhenlin verfasserin aut Chen, Meng verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 43(2017), 11 vom: 18. Dez., Seite 6305-6313 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313 https://dx.doi.org/10.1007/s13369-017-3013-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 43 2017 11 18 12 6305-6313
language	English
source	Enthalten in The Arabian journal for science and engineering 43(2017), 11 vom: 18. Dez., Seite 6305-6313 volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313
sourceStr	Enthalten in The Arabian journal for science and engineering 43(2017), 11 vom: 18. Dez., Seite 6305-6313 volume:43 year:2017 number:11 day:18 month:12 pages:6305-6313
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Tight sandy conglomerate Nuclear magnetic resonance SDR model Saturation exponent Water-wet
dewey-raw	600
isfreeaccess_bool	false
container_title	The Arabian journal for science and engineering
authorswithroles_txt_mv	Kuang, Yan @@aut@@ Sima, Liqiang @@aut@@ Zhang, Zeyu @@aut@@ Wang, Zhenlin @@aut@@ Chen, Meng @@aut@@
publishDateDaySort_date	2017-12-18T00:00:00Z
hierarchy_top_id	588780731
dewey-sort	3600
id	SPR03197046X
language_de	englisch
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author	Kuang, Yan
spellingShingle	Kuang, Yan ddc 600 bkl 31.00 misc Tight sandy conglomerate misc Nuclear magnetic resonance misc SDR model misc Saturation exponent misc Water-wet A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs
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topic_title	600 500 ASE 31.00 bkl A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs Tight sandy conglomerate (dpeaa)DE-He213 Nuclear magnetic resonance (dpeaa)DE-He213 SDR model (dpeaa)DE-He213 Saturation exponent (dpeaa)DE-He213 Water-wet (dpeaa)DE-He213
topic	ddc 600 bkl 31.00 misc Tight sandy conglomerate misc Nuclear magnetic resonance misc SDR model misc Saturation exponent misc Water-wet
topic_unstemmed	ddc 600 bkl 31.00 misc Tight sandy conglomerate misc Nuclear magnetic resonance misc SDR model misc Saturation exponent misc Water-wet
topic_browse	ddc 600 bkl 31.00 misc Tight sandy conglomerate misc Nuclear magnetic resonance misc SDR model misc Saturation exponent misc Water-wet
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title	A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs
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title_full	A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs
author_sort	Kuang, Yan
journal	The Arabian journal for science and engineering
journalStr	The Arabian journal for science and engineering
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author_browse	Kuang, Yan Sima, Liqiang Zhang, Zeyu Wang, Zhenlin Chen, Meng
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title_sort	model for estimating the saturation exponent based on nmr in tight sandy conglomerate reservoirs
title_auth	A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs
abstract	Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs.
abstractGer	Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs.
abstract_unstemmed	Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. These results indicate that the proposed model could be applied to accurately predict the water saturation in tight sandy conglomerate reservoirs.
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title_short	A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs
url	https://dx.doi.org/10.1007/s13369-017-3013-1
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author2	Sima, Liqiang Zhang, Zeyu Wang, Zhenlin Chen, Meng
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up_date	2024-07-04T02:01:14.317Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR03197046X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111193143.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13369-017-3013-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR03197046X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13369-017-3013-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="a">500</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kuang, Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The electrical resistivity of a porous medium, which is used for water or oil saturation evaluation through combination with Archie’s law, strongly depends on the wettability and geometry of the pore system. In tight sandy conglomerate reservoirs, the pore-throat structure is so complicated that Archie’s equation is non-applicable. Analysis shows that the saturation exponent n is a function of the water saturation %$(S_\mathrm{w})%$, microstructure, and wettability. Here, a novel method based on nuclear magnetic resonance is proposed to determine the saturation exponent (n) under water- or oil-wet conditions. The Schlumberger Doll Research model is also introduced to estimate and analyze the fluid distribution in an irregular pore space. To validate the model, 21 water-wet core samples from the tight conglomerate reservoir in the Junggar Basin, Northwestern China, were selected to measure the electrical resistivity at varying water saturation levels. The absolute errors between the predicted and measured saturation exponents range from −0.153 to 0.131, and the absolute errors for the water saturation between the predicted and measured values vary from −0.032 to 0.023. 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A Model for Estimating the Saturation Exponent Based on NMR in Tight Sandy Conglomerate Reservoirs

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