Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks

Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive cha...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Fuhao Wang [verfasserIn] Dongmei Zhang [verfasserIn] Geyong Min [verfasserIn] Jianxin Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 53317-53325
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:53317-53325

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3070343

Katalog-ID:	DOAJ050593579

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ050593579
003	DE-627
005	20230308153815.0
007	cr uuu---uuuuu
008	230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1109/ACCESS.2021.3070343 \|2 doi
035			\|a (DE-627)DOAJ050593579
035			\|a (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a TK1-9971
100	0		\|a Fuhao Wang \|e verfasserin \|4 aut
245	1	0	\|a Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks
264		1	\|c 2021
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.
650		4	\|a Fractured-vuggy carbonate reservoir
650		4	\|a production prediction
650		4	\|a variational mode decomposition
650		4	\|a gated recurrent unit
653		0	\|a Electrical engineering. Electronics. Nuclear engineering
700	0		\|a Dongmei Zhang \|e verfasserin \|4 aut
700	0		\|a Geyong Min \|e verfasserin \|4 aut
700	0		\|a Jianxin Li \|e verfasserin \|4 aut
773	0	8	\|i In \|t IEEE Access \|d IEEE, 2014 \|g 9(2021), Seite 53317-53325 \|w (DE-627)728440385 \|w (DE-600)2687964-5 \|x 21693536 \|7 nnns
773	1	8	\|g volume:9 \|g year:2021 \|g pages:53317-53325
856	4	0	\|u https://doi.org/10.1109/ACCESS.2021.3070343 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f \|z kostenfrei
856	4	0	\|u https://ieeexplore.ieee.org/document/9393382/ \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2169-3536 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 9 \|j 2021 \|h 53317-53325

Indexfelder

author_variant	f w fw d z dz g m gm j l jl
matchkey_str	article:21693536:2021----::eevipoutopeitobsdnaitoamddcmoiinng
hierarchy_sort_str	2021
callnumber-subject-code	TK
publishDate	2021
allfields	10.1109/ACCESS.2021.3070343 doi (DE-627)DOAJ050593579 (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f DE-627 ger DE-627 rakwb eng TK1-9971 Fuhao Wang verfasserin aut Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs. Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit Electrical engineering. Electronics. Nuclear engineering Dongmei Zhang verfasserin aut Geyong Min verfasserin aut Jianxin Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 53317-53325 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:53317-53325 https://doi.org/10.1109/ACCESS.2021.3070343 kostenfrei https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f kostenfrei https://ieeexplore.ieee.org/document/9393382/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 53317-53325
spelling	10.1109/ACCESS.2021.3070343 doi (DE-627)DOAJ050593579 (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f DE-627 ger DE-627 rakwb eng TK1-9971 Fuhao Wang verfasserin aut Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs. Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit Electrical engineering. Electronics. Nuclear engineering Dongmei Zhang verfasserin aut Geyong Min verfasserin aut Jianxin Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 53317-53325 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:53317-53325 https://doi.org/10.1109/ACCESS.2021.3070343 kostenfrei https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f kostenfrei https://ieeexplore.ieee.org/document/9393382/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 53317-53325
allfields_unstemmed	10.1109/ACCESS.2021.3070343 doi (DE-627)DOAJ050593579 (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f DE-627 ger DE-627 rakwb eng TK1-9971 Fuhao Wang verfasserin aut Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs. Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit Electrical engineering. Electronics. Nuclear engineering Dongmei Zhang verfasserin aut Geyong Min verfasserin aut Jianxin Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 53317-53325 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:53317-53325 https://doi.org/10.1109/ACCESS.2021.3070343 kostenfrei https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f kostenfrei https://ieeexplore.ieee.org/document/9393382/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 53317-53325
allfieldsGer	10.1109/ACCESS.2021.3070343 doi (DE-627)DOAJ050593579 (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f DE-627 ger DE-627 rakwb eng TK1-9971 Fuhao Wang verfasserin aut Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs. Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit Electrical engineering. Electronics. Nuclear engineering Dongmei Zhang verfasserin aut Geyong Min verfasserin aut Jianxin Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 53317-53325 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:53317-53325 https://doi.org/10.1109/ACCESS.2021.3070343 kostenfrei https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f kostenfrei https://ieeexplore.ieee.org/document/9393382/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 53317-53325
allfieldsSound	10.1109/ACCESS.2021.3070343 doi (DE-627)DOAJ050593579 (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f DE-627 ger DE-627 rakwb eng TK1-9971 Fuhao Wang verfasserin aut Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs. Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit Electrical engineering. Electronics. Nuclear engineering Dongmei Zhang verfasserin aut Geyong Min verfasserin aut Jianxin Li verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 53317-53325 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:53317-53325 https://doi.org/10.1109/ACCESS.2021.3070343 kostenfrei https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f kostenfrei https://ieeexplore.ieee.org/document/9393382/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 53317-53325
language	English
source	In IEEE Access 9(2021), Seite 53317-53325 volume:9 year:2021 pages:53317-53325
sourceStr	In IEEE Access 9(2021), Seite 53317-53325 volume:9 year:2021 pages:53317-53325
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit Electrical engineering. Electronics. Nuclear engineering
isfreeaccess_bool	true
container_title	IEEE Access
authorswithroles_txt_mv	Fuhao Wang @@aut@@ Dongmei Zhang @@aut@@ Geyong Min @@aut@@ Jianxin Li @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	728440385
id	DOAJ050593579
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ050593579</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308153815.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2021.3070343</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ050593579</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Fuhao Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractured-vuggy carbonate reservoir</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">production prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">variational mode decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gated recurrent unit</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dongmei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Geyong Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jianxin Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">9(2021), Seite 53317-53325</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:53317-53325</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2021.3070343</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9393382/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2021</subfield><subfield code="h">53317-53325</subfield></datafield></record></collection>
callnumber-first	T - Technology
author	Fuhao Wang
spellingShingle	Fuhao Wang misc TK1-9971 misc Fractured-vuggy carbonate reservoir misc production prediction misc variational mode decomposition misc gated recurrent unit misc Electrical engineering. Electronics. Nuclear engineering Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks
authorStr	Fuhao Wang
ppnlink_with_tag_str_mv	@@773@@(DE-627)728440385
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	TK1-9971
illustrated	Not Illustrated
issn	21693536
topic_title	TK1-9971 Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks Fractured-vuggy carbonate reservoir production prediction variational mode decomposition gated recurrent unit
topic	misc TK1-9971 misc Fractured-vuggy carbonate reservoir misc production prediction misc variational mode decomposition misc gated recurrent unit misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Fractured-vuggy carbonate reservoir misc production prediction misc variational mode decomposition misc gated recurrent unit misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Fractured-vuggy carbonate reservoir misc production prediction misc variational mode decomposition misc gated recurrent unit misc Electrical engineering. Electronics. Nuclear engineering
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	IEEE Access
hierarchy_parent_id	728440385
hierarchy_top_title	IEEE Access
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)728440385 (DE-600)2687964-5
title	Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks
ctrlnum	(DE-627)DOAJ050593579 (DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f
title_full	Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks
author_sort	Fuhao Wang
journal	IEEE Access
journalStr	IEEE Access
callnumber-first-code	T
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2021
contenttype_str_mv	txt
container_start_page	53317
author_browse	Fuhao Wang Dongmei Zhang Geyong Min Jianxin Li
container_volume	9
class	TK1-9971
format_se	Elektronische Aufsätze
author-letter	Fuhao Wang
doi_str_mv	10.1109/ACCESS.2021.3070343
author2-role	verfasserin
title_sort	reservoir production prediction based on variational mode decomposition and gated recurrent unit networks
callnumber	TK1-9971
title_auth	Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks
abstract	Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.
abstractGer	Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.
abstract_unstemmed	Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks
url	https://doi.org/10.1109/ACCESS.2021.3070343 https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f https://ieeexplore.ieee.org/document/9393382/ https://doaj.org/toc/2169-3536
remote_bool	true
author2	Dongmei Zhang Geyong Min Jianxin Li
author2Str	Dongmei Zhang Geyong Min Jianxin Li
ppnlink	728440385
callnumber-subject	TK - Electrical and Nuclear Engineering
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1109/ACCESS.2021.3070343
callnumber-a	TK1-9971
up_date	2024-07-03T15:42:37.990Z
_version_	1803573115478867968
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">DOAJ050593579</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308153815.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/ACCESS.2021.3070343</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ050593579</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJfe87bb6ea407420093d2313f2aeb6b8f</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="050" ind1=" " ind2="0"><subfield code="a">TK1-9971</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Fuhao Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</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">Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fractured-vuggy carbonate reservoir</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">production prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">variational mode decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gated recurrent unit</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. Nuclear engineering</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Dongmei Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Geyong Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jianxin Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">IEEE Access</subfield><subfield code="d">IEEE, 2014</subfield><subfield code="g">9(2021), Seite 53317-53325</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:53317-53325</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2021.3070343</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/fe87bb6ea407420093d2313f2aeb6b8f</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9393382/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2169-3536</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2021</subfield><subfield code="h">53317-53325</subfield></datafield></record></collection>
score	7.4002047

Nicht das Richtige dabei?

Schreiben Sie uns!

Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?