Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods

Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implem...
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Autor*in:	Azamifard, Arash [verfasserIn] Ahmadi, Mohammad [verfasserIn] Rashidi, Fariborz [verfasserIn] Pourfard, Mohammadreza [verfasserIn] Dabir, Bahram [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Enhance oil recovery Heavy oil Multiple-point statistic Reservoir heterogeneity Reservoir simulation

Übergeordnetes Werk:	Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 13(2020), 7 vom: 31. März
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:7 ; day:31 ; month:03

Links:	Volltext

DOI / URN:	10.1007/s12517-020-05293-y

Katalog-ID:	SPR039276236

Internformat


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520			\|a Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent.
650		4	\|a Enhance oil recovery \|7 (dpeaa)DE-He213
650		4	\|a Heavy oil \|7 (dpeaa)DE-He213
650		4	\|a Multiple-point statistic \|7 (dpeaa)DE-He213
650		4	\|a Reservoir heterogeneity \|7 (dpeaa)DE-He213
650		4	\|a Reservoir simulation \|7 (dpeaa)DE-He213
700	1		\|a Ahmadi, Mohammad \|e verfasserin \|4 aut
700	1		\|a Rashidi, Fariborz \|e verfasserin \|4 aut
700	1		\|a Pourfard, Mohammadreza \|e verfasserin \|4 aut
700	1		\|a Dabir, Bahram \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Arabian journal of geosciences \|d Berlin : Springer, 2008 \|g 13(2020), 7 vom: 31. März \|w (DE-627)572421877 \|w (DE-600)2438771-X \|x 1866-7538 \|7 nnns
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912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
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912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_381
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912			\|a GBV_ILN_2044
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912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
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912			\|a GBV_ILN_2056
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912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
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author_variant	a a aa m a ma f r fr m p mp b d bd
matchkey_str	article:18667538:2020----::nihsfegnrtorsrorrpryoeigpmtosnsesnteeevipro
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publishDate	2020
allfields	10.1007/s12517-020-05293-y doi (DE-627)SPR039276236 (SPR)s12517-020-05293-y-e DE-627 ger DE-627 rakwb eng 550 ASE Azamifard, Arash verfasserin aut Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent. Enhance oil recovery (dpeaa)DE-He213 Heavy oil (dpeaa)DE-He213 Multiple-point statistic (dpeaa)DE-He213 Reservoir heterogeneity (dpeaa)DE-He213 Reservoir simulation (dpeaa)DE-He213 Ahmadi, Mohammad verfasserin aut Rashidi, Fariborz verfasserin aut Pourfard, Mohammadreza verfasserin aut Dabir, Bahram verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-020-05293-y 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_381 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 7 31 03
spelling	10.1007/s12517-020-05293-y doi (DE-627)SPR039276236 (SPR)s12517-020-05293-y-e DE-627 ger DE-627 rakwb eng 550 ASE Azamifard, Arash verfasserin aut Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent. Enhance oil recovery (dpeaa)DE-He213 Heavy oil (dpeaa)DE-He213 Multiple-point statistic (dpeaa)DE-He213 Reservoir heterogeneity (dpeaa)DE-He213 Reservoir simulation (dpeaa)DE-He213 Ahmadi, Mohammad verfasserin aut Rashidi, Fariborz verfasserin aut Pourfard, Mohammadreza verfasserin aut Dabir, Bahram verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-020-05293-y 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_381 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 7 31 03
allfields_unstemmed	10.1007/s12517-020-05293-y doi (DE-627)SPR039276236 (SPR)s12517-020-05293-y-e DE-627 ger DE-627 rakwb eng 550 ASE Azamifard, Arash verfasserin aut Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent. Enhance oil recovery (dpeaa)DE-He213 Heavy oil (dpeaa)DE-He213 Multiple-point statistic (dpeaa)DE-He213 Reservoir heterogeneity (dpeaa)DE-He213 Reservoir simulation (dpeaa)DE-He213 Ahmadi, Mohammad verfasserin aut Rashidi, Fariborz verfasserin aut Pourfard, Mohammadreza verfasserin aut Dabir, Bahram verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-020-05293-y 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_381 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 7 31 03
allfieldsGer	10.1007/s12517-020-05293-y doi (DE-627)SPR039276236 (SPR)s12517-020-05293-y-e DE-627 ger DE-627 rakwb eng 550 ASE Azamifard, Arash verfasserin aut Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent. Enhance oil recovery (dpeaa)DE-He213 Heavy oil (dpeaa)DE-He213 Multiple-point statistic (dpeaa)DE-He213 Reservoir heterogeneity (dpeaa)DE-He213 Reservoir simulation (dpeaa)DE-He213 Ahmadi, Mohammad verfasserin aut Rashidi, Fariborz verfasserin aut Pourfard, Mohammadreza verfasserin aut Dabir, Bahram verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-020-05293-y 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_381 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 7 31 03
allfieldsSound	10.1007/s12517-020-05293-y doi (DE-627)SPR039276236 (SPR)s12517-020-05293-y-e DE-627 ger DE-627 rakwb eng 550 ASE Azamifard, Arash verfasserin aut Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent. Enhance oil recovery (dpeaa)DE-He213 Heavy oil (dpeaa)DE-He213 Multiple-point statistic (dpeaa)DE-He213 Reservoir heterogeneity (dpeaa)DE-He213 Reservoir simulation (dpeaa)DE-He213 Ahmadi, Mohammad verfasserin aut Rashidi, Fariborz verfasserin aut Pourfard, Mohammadreza verfasserin aut Dabir, Bahram verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 13(2020), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:13 year:2020 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-020-05293-y 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_381 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2020 7 31 03
language	English
source	Enthalten in Arabian journal of geosciences 13(2020), 7 vom: 31. März volume:13 year:2020 number:7 day:31 month:03
sourceStr	Enthalten in Arabian journal of geosciences 13(2020), 7 vom: 31. März volume:13 year:2020 number:7 day:31 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Enhance oil recovery Heavy oil Multiple-point statistic Reservoir heterogeneity Reservoir simulation
dewey-raw	550
isfreeaccess_bool	false
container_title	Arabian journal of geosciences
authorswithroles_txt_mv	Azamifard, Arash @@aut@@ Ahmadi, Mohammad @@aut@@ Rashidi, Fariborz @@aut@@ Pourfard, Mohammadreza @@aut@@ Dabir, Bahram @@aut@@
publishDateDaySort_date	2020-03-31T00:00:00Z
hierarchy_top_id	572421877
dewey-sort	3550
id	SPR039276236
language_de	englisch
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author	Azamifard, Arash
spellingShingle	Azamifard, Arash ddc 550 misc Enhance oil recovery misc Heavy oil misc Multiple-point statistic misc Reservoir heterogeneity misc Reservoir simulation Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods
authorStr	Azamifard, Arash
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topic_title	550 ASE Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods Enhance oil recovery (dpeaa)DE-He213 Heavy oil (dpeaa)DE-He213 Multiple-point statistic (dpeaa)DE-He213 Reservoir heterogeneity (dpeaa)DE-He213 Reservoir simulation (dpeaa)DE-He213
topic	ddc 550 misc Enhance oil recovery misc Heavy oil misc Multiple-point statistic misc Reservoir heterogeneity misc Reservoir simulation
topic_unstemmed	ddc 550 misc Enhance oil recovery misc Heavy oil misc Multiple-point statistic misc Reservoir heterogeneity misc Reservoir simulation
topic_browse	ddc 550 misc Enhance oil recovery misc Heavy oil misc Multiple-point statistic misc Reservoir heterogeneity misc Reservoir simulation
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title	Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods
ctrlnum	(DE-627)SPR039276236 (SPR)s12517-020-05293-y-e
title_full	Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods
author_sort	Azamifard, Arash
journal	Arabian journal of geosciences
journalStr	Arabian journal of geosciences
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author_browse	Azamifard, Arash Ahmadi, Mohammad Rashidi, Fariborz Pourfard, Mohammadreza Dabir, Bahram
container_volume	13
class	550 ASE
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author-letter	Azamifard, Arash
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title_sort	insights of new-generation reservoir property modeling (mps methods) in assessing the reservoir performance for different recovery methods
title_auth	Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods
abstract	Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent.
abstractGer	Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent.
abstract_unstemmed	Abstract Usage of an advanced geostatistical method for reservoir characterization results in more reliable insights of reservoir performance. Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. The results show that synthesized permeability map is able to create a correct trend of oil production rate for whole reservoir life compared with the reference case. However, similar behavior at one oil viscosity value does not guarantee similar behavior of reservoir for another set of oil viscosity values. For highly viscose oil reservoir, reference permeability shows later water breakthrough than oil with normal viscosity while in synthesized permeability, water breakthrough happens earlier for oil with normal viscosity. This indicates the uncertainty of EOR scenario prediction due to imperfectness of MPS methods for characterizing the studied reservoir. Incidentally, as an important advantage, the reference general shape of water front and saturation spectrum is satisfactorily regenerated by one of MPS methods. Moreover, flanging and fingering of water front are mimicked to some acceptable extent.
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container_issue	7
title_short	Insights of new-generation reservoir property modeling (MPS methods) in assessing the reservoir performance for different recovery methods
url	https://dx.doi.org/10.1007/s12517-020-05293-y
remote_bool	true
author2	Ahmadi, Mohammad Rashidi, Fariborz Pourfard, Mohammadreza Dabir, Bahram
author2Str	Ahmadi, Mohammad Rashidi, Fariborz Pourfard, Mohammadreza Dabir, Bahram
ppnlink	572421877
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doi_str	10.1007/s12517-020-05293-y
up_date	2024-07-03T23:03:27.464Z
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Multiple-point statistic (MPS) methods which borrow ideas of texture synthesis could mimic the variation of complex reservoir permeability. This study implements this complex variation of permeability for enhanced oil recovery (EOR) purposes. Thermal EOR which reduces the viscosity of reservoir could be nominated as one of possible EOR method for any reservoir. The effect of high-order statistics of permeability is studied against the variation of oil viscosity. To this end, a modified version of SPE 10 model-1 is used to apply a thermal EOR scenario. Constant rate water injection creates a front of water which moves through the reservoir based on synthesized MPS-derived permeabilities as well as the reference case. In this study, multi-scale cross-correlation-based simulation (MSCCSIM) and image quilting (IQ) are selected for generating reservoir permeability. 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