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
Autor*in: |
Fuhao Wang [verfasserIn] Dongmei Zhang [verfasserIn] Geyong Min [verfasserIn] Jianxin Li [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Schlagwörter: |
Fractured-vuggy carbonate reservoir |
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Übergeordnetes Werk: |
In: IEEE Access - IEEE, 2014, 9(2021), Seite 53317-53325 |
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Übergeordnetes Werk: |
volume:9 ; year:2021 ; pages:53317-53325 |
Links: |
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DOI / URN: |
10.1109/ACCESS.2021.3070343 |
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Katalog-ID: |
DOAJ050593579 |
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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. | ||
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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 |
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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 |
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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 |
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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 |
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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 |
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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. |
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Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks |
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