A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures
Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intric...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Zhiming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Transport in porous media - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986, 149(2022), 1 vom: 07. Nov., Seite 345-371 |
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| Übergeordnetes Werk: |
volume:149 ; year:2022 ; number:1 ; day:07 ; month:11 ; pages:345-371 |
| Links: |
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| DOI / URN: |
10.1007/s11242-022-01877-2 |
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SPR052239128 |
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| 520 | |a Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. | ||
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10.1007/s11242-022-01877-2 doi (DE-627)SPR052239128 (SPR)s11242-022-01877-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiming verfasserin aut A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. Pressure transient behavior (dpeaa)DE-He213 Heterogeneous fracture (dpeaa)DE-He213 Bidirectional LSTM (dpeaa)DE-He213 Surrogate model (dpeaa)DE-He213 Fractured reservoirs (dpeaa)DE-He213 Li, Dexuan aut Dong, Peng aut Sepehrnoori, Kamy aut Enthalten in Transport in porous media Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 149(2022), 1 vom: 07. Nov., Seite 345-371 (DE-627)269017720 (DE-600)1473676-7 1573-1634 nnns volume:149 year:2022 number:1 day:07 month:11 pages:345-371 https://dx.doi.org/10.1007/s11242-022-01877-2 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2360 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 149 2022 1 07 11 345-371 |
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10.1007/s11242-022-01877-2 doi (DE-627)SPR052239128 (SPR)s11242-022-01877-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiming verfasserin aut A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. Pressure transient behavior (dpeaa)DE-He213 Heterogeneous fracture (dpeaa)DE-He213 Bidirectional LSTM (dpeaa)DE-He213 Surrogate model (dpeaa)DE-He213 Fractured reservoirs (dpeaa)DE-He213 Li, Dexuan aut Dong, Peng aut Sepehrnoori, Kamy aut Enthalten in Transport in porous media Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 149(2022), 1 vom: 07. Nov., Seite 345-371 (DE-627)269017720 (DE-600)1473676-7 1573-1634 nnns volume:149 year:2022 number:1 day:07 month:11 pages:345-371 https://dx.doi.org/10.1007/s11242-022-01877-2 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2360 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 149 2022 1 07 11 345-371 |
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10.1007/s11242-022-01877-2 doi (DE-627)SPR052239128 (SPR)s11242-022-01877-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiming verfasserin aut A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. Pressure transient behavior (dpeaa)DE-He213 Heterogeneous fracture (dpeaa)DE-He213 Bidirectional LSTM (dpeaa)DE-He213 Surrogate model (dpeaa)DE-He213 Fractured reservoirs (dpeaa)DE-He213 Li, Dexuan aut Dong, Peng aut Sepehrnoori, Kamy aut Enthalten in Transport in porous media Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 149(2022), 1 vom: 07. Nov., Seite 345-371 (DE-627)269017720 (DE-600)1473676-7 1573-1634 nnns volume:149 year:2022 number:1 day:07 month:11 pages:345-371 https://dx.doi.org/10.1007/s11242-022-01877-2 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2360 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 149 2022 1 07 11 345-371 |
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10.1007/s11242-022-01877-2 doi (DE-627)SPR052239128 (SPR)s11242-022-01877-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiming verfasserin aut A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. Pressure transient behavior (dpeaa)DE-He213 Heterogeneous fracture (dpeaa)DE-He213 Bidirectional LSTM (dpeaa)DE-He213 Surrogate model (dpeaa)DE-He213 Fractured reservoirs (dpeaa)DE-He213 Li, Dexuan aut Dong, Peng aut Sepehrnoori, Kamy aut Enthalten in Transport in porous media Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 149(2022), 1 vom: 07. Nov., Seite 345-371 (DE-627)269017720 (DE-600)1473676-7 1573-1634 nnns volume:149 year:2022 number:1 day:07 month:11 pages:345-371 https://dx.doi.org/10.1007/s11242-022-01877-2 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2360 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 149 2022 1 07 11 345-371 |
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10.1007/s11242-022-01877-2 doi (DE-627)SPR052239128 (SPR)s11242-022-01877-2-e DE-627 ger DE-627 rakwb eng Chen, Zhiming verfasserin aut A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. Pressure transient behavior (dpeaa)DE-He213 Heterogeneous fracture (dpeaa)DE-He213 Bidirectional LSTM (dpeaa)DE-He213 Surrogate model (dpeaa)DE-He213 Fractured reservoirs (dpeaa)DE-He213 Li, Dexuan aut Dong, Peng aut Sepehrnoori, Kamy aut Enthalten in Transport in porous media Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 149(2022), 1 vom: 07. Nov., Seite 345-371 (DE-627)269017720 (DE-600)1473676-7 1573-1634 nnns volume:149 year:2022 number:1 day:07 month:11 pages:345-371 https://dx.doi.org/10.1007/s11242-022-01877-2 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_2360 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 149 2022 1 07 11 345-371 |
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Enthalten in Transport in porous media 149(2022), 1 vom: 07. Nov., Seite 345-371 volume:149 year:2022 number:1 day:07 month:11 pages:345-371 |
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Enthalten in Transport in porous media 149(2022), 1 vom: 07. Nov., Seite 345-371 volume:149 year:2022 number:1 day:07 month:11 pages:345-371 |
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Pressure transient behavior Heterogeneous fracture Bidirectional LSTM Surrogate model Fractured reservoirs |
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Chen, Zhiming @@aut@@ Li, Dexuan @@aut@@ Dong, Peng @@aut@@ Sepehrnoori, Kamy @@aut@@ |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pressure transient behavior</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heterogeneous fracture</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bidirectional LSTM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Surrogate model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractured reservoirs</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Dexuan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dong, Peng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sepehrnoori, Kamy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Transport in porous media</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986</subfield><subfield code="g">149(2022), 1 vom: 07. 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|
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Chen, Zhiming |
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Chen, Zhiming misc Pressure transient behavior misc Heterogeneous fracture misc Bidirectional LSTM misc Surrogate model misc Fractured reservoirs A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures |
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A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures Pressure transient behavior (dpeaa)DE-He213 Heterogeneous fracture (dpeaa)DE-He213 Bidirectional LSTM (dpeaa)DE-He213 Surrogate model (dpeaa)DE-He213 Fractured reservoirs (dpeaa)DE-He213 |
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misc Pressure transient behavior misc Heterogeneous fracture misc Bidirectional LSTM misc Surrogate model misc Fractured reservoirs |
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A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures |
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A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures |
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Transport in porous media |
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deep learning-based surrogate model for pressure transient behaviors in shale wells with heterogeneous fractures |
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A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures |
| abstract |
Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract With hydraulic fracturing technology, manmade fractures can be generated around the shale-gas wells. After hydraulic fracturing at each fracture stage, many wells in shale reservoirs have the “shut-in” process, which provides many precious data for parameter estimation. But, owing to intricate geological and engineering factors, the fractures in reservoirs are asymmetric and heterogeneous, which brings a great challenge for fracture estimation. To improve this situation, coupling with deep learning (DL) approach and experimental practices, we established a surrogate model for non-uniform fractures at one fracture stage, based on deep Bi-directional LSTM model. First, a well testing model containing three distinct flow regions is developed, namely (1) heterogeneous hydraulic fractures, (2) the inner region affected by hydraulic fracturing, and (3) the outer region without stimulation. Laplace transformation methods are used for model solutions. Then, with the model solutions, a surrogate model based on deep bidirectional LSTM is built for improving computational efficiency. Compared with the proxy model based on LSTM, RNN and ANN, Bi-LSTM model can effectively reduce the early prediction error of pressure derivative, and the average relative prediction error is 1.16%. The pressure transient behavior of the surrogate model can be divided into four flow regimes, which represent bilinear flow, interporosity flow, linear flow and boundary dominated flow, respectively. Finally, model verification was shown by comparing with the results from traditional well testing model. The results show that the calculation speed of the surrogate model is three orders of magnitude higher than that of the well test model. The findings of this study can help to efficiently evaluate the fracture parameter in complex fracture system generated by large-scale fracturing treatments in shale reservoirs. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A Deep Learning-based Surrogate Model for Pressure Transient Behaviors in Shale Wells with Heterogeneous Fractures |
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https://dx.doi.org/10.1007/s11242-022-01877-2 |
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Li, Dexuan Dong, Peng Sepehrnoori, Kamy |
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Li, Dexuan Dong, Peng Sepehrnoori, Kamy |
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10.1007/s11242-022-01877-2 |
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| score |
7.4013214 |