Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning

Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Peng, Yudan [verfasserIn] Fu, Guangming [verfasserIn] Sun, Baojiang [verfasserIn] Chen, Jiying [verfasserIn] Zhang, Weiguo [verfasserIn] Ren, Meipeng [verfasserIn] Zhang, Heen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling

Übergeordnetes Werk:	Enthalten in: Ocean engineering - Amsterdam [u.a.] : Elsevier Science, 1970, 279
Übergeordnetes Werk:	volume:279

DOI / URN:	10.1016/j.oceaneng.2023.114400

Katalog-ID:	ELV009855041

Internformat


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024	7		\|a 10.1016/j.oceaneng.2023.114400 \|2 doi
035			\|a (DE-627)ELV009855041
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100	1		\|a Peng, Yudan \|e verfasserin \|4 aut
245	1	0	\|a Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
264		1	\|c 2023
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337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects.
650		4	\|a Screen pipe
650		4	\|a Corrosion defect
650		4	\|a Hole parameters
650		4	\|a Collapse strength
650		4	\|a Finite element
650		4	\|a Tree machine learning
650		4	\|a Data driven modelling
700	1		\|a Fu, Guangming \|e verfasserin \|4 aut
700	1		\|a Sun, Baojiang \|e verfasserin \|4 aut
700	1		\|a Chen, Jiying \|e verfasserin \|4 aut
700	1		\|a Zhang, Weiguo \|e verfasserin \|4 aut
700	1		\|a Ren, Meipeng \|e verfasserin \|4 aut
700	1		\|a Zhang, Heen \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Ocean engineering \|d Amsterdam [u.a.] : Elsevier Science, 1970 \|g 279 \|h Online-Ressource \|w (DE-627)30658977X \|w (DE-600)1498543-3 \|w (DE-576)259484164 \|x 0029-8018 \|7 nnns
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936	b	k	\|a 50.92 \|j Meerestechnik \|q VZ
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allfields	10.1016/j.oceaneng.2023.114400 doi (DE-627)ELV009855041 (ELSEVIER)S0029-8018(23)00784-9 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Peng, Yudan verfasserin aut Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects. Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling Fu, Guangming verfasserin aut Sun, Baojiang verfasserin aut Chen, Jiying verfasserin aut Zhang, Weiguo verfasserin aut Ren, Meipeng verfasserin aut Zhang, Heen verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 279 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:279 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 279
spelling	10.1016/j.oceaneng.2023.114400 doi (DE-627)ELV009855041 (ELSEVIER)S0029-8018(23)00784-9 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Peng, Yudan verfasserin aut Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects. Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling Fu, Guangming verfasserin aut Sun, Baojiang verfasserin aut Chen, Jiying verfasserin aut Zhang, Weiguo verfasserin aut Ren, Meipeng verfasserin aut Zhang, Heen verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 279 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:279 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 279
allfields_unstemmed	10.1016/j.oceaneng.2023.114400 doi (DE-627)ELV009855041 (ELSEVIER)S0029-8018(23)00784-9 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Peng, Yudan verfasserin aut Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects. Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling Fu, Guangming verfasserin aut Sun, Baojiang verfasserin aut Chen, Jiying verfasserin aut Zhang, Weiguo verfasserin aut Ren, Meipeng verfasserin aut Zhang, Heen verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 279 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:279 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 279
allfieldsGer	10.1016/j.oceaneng.2023.114400 doi (DE-627)ELV009855041 (ELSEVIER)S0029-8018(23)00784-9 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Peng, Yudan verfasserin aut Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects. Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling Fu, Guangming verfasserin aut Sun, Baojiang verfasserin aut Chen, Jiying verfasserin aut Zhang, Weiguo verfasserin aut Ren, Meipeng verfasserin aut Zhang, Heen verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 279 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:279 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 279
allfieldsSound	10.1016/j.oceaneng.2023.114400 doi (DE-627)ELV009855041 (ELSEVIER)S0029-8018(23)00784-9 DE-627 ger DE-627 rda eng 690 VZ 50.92 bkl Peng, Yudan verfasserin aut Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects. Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling Fu, Guangming verfasserin aut Sun, Baojiang verfasserin aut Chen, Jiying verfasserin aut Zhang, Weiguo verfasserin aut Ren, Meipeng verfasserin aut Zhang, Heen verfasserin aut Enthalten in Ocean engineering Amsterdam [u.a.] : Elsevier Science, 1970 279 Online-Ressource (DE-627)30658977X (DE-600)1498543-3 (DE-576)259484164 0029-8018 nnns volume:279 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.92 Meerestechnik VZ AR 279
language	English
source	Enthalten in Ocean engineering 279 volume:279
sourceStr	Enthalten in Ocean engineering 279 volume:279
format_phy_str_mv	Article
bklname	Meerestechnik
institution	findex.gbv.de
topic_facet	Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling
dewey-raw	690
isfreeaccess_bool	false
container_title	Ocean engineering
authorswithroles_txt_mv	Peng, Yudan @@aut@@ Fu, Guangming @@aut@@ Sun, Baojiang @@aut@@ Chen, Jiying @@aut@@ Zhang, Weiguo @@aut@@ Ren, Meipeng @@aut@@ Zhang, Heen @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	30658977X
dewey-sort	3690
id	ELV009855041
language_de	englisch
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Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. 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author	Peng, Yudan
spellingShingle	Peng, Yudan ddc 690 bkl 50.92 misc Screen pipe misc Corrosion defect misc Hole parameters misc Collapse strength misc Finite element misc Tree machine learning misc Data driven modelling Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
authorStr	Peng, Yudan
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topic_title	690 VZ 50.92 bkl Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning Screen pipe Corrosion defect Hole parameters Collapse strength Finite element Tree machine learning Data driven modelling
topic	ddc 690 bkl 50.92 misc Screen pipe misc Corrosion defect misc Hole parameters misc Collapse strength misc Finite element misc Tree machine learning misc Data driven modelling
topic_unstemmed	ddc 690 bkl 50.92 misc Screen pipe misc Corrosion defect misc Hole parameters misc Collapse strength misc Finite element misc Tree machine learning misc Data driven modelling
topic_browse	ddc 690 bkl 50.92 misc Screen pipe misc Corrosion defect misc Hole parameters misc Collapse strength misc Finite element misc Tree machine learning misc Data driven modelling
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title	Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
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title_full	Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
author_sort	Peng, Yudan
journal	Ocean engineering
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author_browse	Peng, Yudan Fu, Guangming Sun, Baojiang Chen, Jiying Zhang, Weiguo Ren, Meipeng Zhang, Heen
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author-letter	Peng, Yudan
doi_str_mv	10.1016/j.oceaneng.2023.114400
dewey-full	690
author2-role	verfasserin
title_sort	data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
title_auth	Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
abstract	Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects.
abstractGer	Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects.
abstract_unstemmed	Aiming to address the difficulties in predicting the collapse strength of sand control screen pipes with internal corrosion defects under the external pressure in offshore unconsolidated sandstone oil and gas reservoirs development, a parametric analysis model of a screen pipe with corrosion defects was established based on secondary development of ABAQUS by Python script, and was verified by the published experimental data. Then, the effect of corrosion defect and hole parameters on the collapse strength of the screen pipes was analyzed. Furthermore, a novel data-driven model with the gradient boosting regression tree (GBRT) method was used to explore and characterize the laws contained in the numerical simulation data based on a large volume of numerical simulation data. The results demonstrated that the proposed data-driven prediction model for the collapse strength of screen pipes exhibited a stable prediction average difference of 3.97% in the test set. And a large number of generalization practices based on data-driven prediction model indicated that corresponding corrosion parameter warning interval existed when the screen pipe design structure was determined, and when the corrosion defect size was greater than the warning interval, the collapse capacity of the screen pipe was considerably weakened. Moreover, the collapse strength was the lowest when the corrosion defect was located at the center of the screen pipe. And the collapse strength of the screen pipe gradually decreased with an increase of corrosion defects parameter and hole arrangement parameter. This study can provide a reference for evaluating the strength and establishing a safety-related warning mechanism of sand control screen pipes with corrosion defects.
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title_short	Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning
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author2	Fu, Guangming Sun, Baojiang Chen, Jiying Zhang, Weiguo Ren, Meipeng Zhang, Heen
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doi_str	10.1016/j.oceaneng.2023.114400
up_date	2024-07-07T00:34:58.519Z
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Data-driven collapse strength modelling for the screen pipes with internal corrosion defect based on finite element analysis and tree-based machine learning

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