Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model

Coiled tubing has been widely used in oilfield development because it can significantly improve oil well productivity and recovery efficiency. However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Applied sciences Ročník 14; číslo 20; s. 9519
Hlavní autori:	Cao, Yinping, Fang, Fengying, Wang, Guowei, Zhu, Wenyu, Hu, Yijie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.10.2024
Predmet:	Accuracy Algorithms Back propagation back-propagation neural network coiled tubing erosion rate prediction Experimental methods Flow velocity grey relational analysis Machine learning Neural networks Oil field services Oil fields Petroleum industry Petroleum mining Research methodology Simulation sparrow search algorithm Turbulence models Viscosity
ISSN:	2076-3417, 2076-3417
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Coiled tubing has been widely used in oilfield development because it can significantly improve oil well productivity and recovery efficiency. However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one of the main failure modes. To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. The results show that the main influencing factors for coiled tubing erosion rate are impact velocity, impact angle, and sand concentration. In addition, the SSA-BPNN model shows a high goodness of fit (R) and a good fit with the experimental data. The SSA-BPNN model underwent standard statistical validation tests, effectively predicting the erosion rate of coiled tubing with a high coefficient of determination and low errors, demonstrating a robust consistency between predicted and actual values. This study is of great significance to oilfield engineers, pipeline designers, and oilfield developers, and provides effective support for optimizing oilfield development and pipeline maintenance. The main users include oil companies, engineering consulting institutions and related industry personnel, and may also attract the interest of scientific research institutions and academia, providing a useful reference for the technological progress of the oil industry.
AbstractList	Coiled tubing has been widely used in oilfield development because it can significantly improve oil well productivity and recovery efficiency. However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one of the main failure modes. To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. The results show that the main influencing factors for coiled tubing erosion rate are impact velocity, impact angle, and sand concentration. In addition, the SSA-BPNN model shows a high goodness of fit (R) and a good fit with the experimental data. The SSA-BPNN model underwent standard statistical validation tests, effectively predicting the erosion rate of coiled tubing with a high coefficient of determination and low errors, demonstrating a robust consistency between predicted and actual values. This study is of great significance to oilfield engineers, pipeline designers, and oilfield developers, and provides effective support for optimizing oilfield development and pipeline maintenance. The main users include oil companies, engineering consulting institutions and related industry personnel, and may also attract the interest of scientific research institutions and academia, providing a useful reference for the technological progress of the oil industry.
Audience	Academic
Author	Fang, Fengying Hu, Yijie Cao, Yinping Zhu, Wenyu Wang, Guowei
Author_xml	– sequence: 1 givenname: Yinping orcidid: 0000-0003-3433-5397 surname: Cao fullname: Cao, Yinping – sequence: 2 givenname: Fengying surname: Fang fullname: Fang, Fengying – sequence: 3 givenname: Guowei surname: Wang fullname: Wang, Guowei – sequence: 4 givenname: Wenyu surname: Zhu fullname: Zhu, Wenyu – sequence: 5 givenname: Yijie surname: Hu fullname: Hu, Yijie
BookMark	eNptUU1v3CAQRVUqNU1z6h-w1GPlFIyx4bhdJW2ktInycUazw9hh4zUuZhX135fdraqoChwGmPfeDG_es6MxjMTYR8HPpDT8C0yTqCtulDBv2HHF26aUtWiPXpzfsdN5XvO8jJBa8GM23URyHpMPYxG6Yhn8QK6436782BfnMcy7xC0kKr7CnDP5djdBjOG5uCOI-Fgshj5Enx43GYFP5U0ME_SwF_xJ2whDDuk5xKfiR3A0fGBvOxhmOv0bT9jDxfn98nt5df3tcrm4KrHmMpXQSFStIWq10BoVampXpiNyBgVo4lyQqbSolckf64A75K6ulXAaRdtwecIuD7ouwNpO0W8g_rYBvN0_hNhbiMnjQFY4QBQrxNqJWlcSuGocKKNl2zrTrLLWp4PWFMOvLc3JrsM2jrl9K0XFVaUquat4dkD1kEX92IUUAfN2tPGYZ9Vlb-0i95ytN3vC5wMBs89zpO5fm4Lb3Ujti5FmtPgPjT7tfc5l_PAq5w-BvaTf
CitedBy_id	crossref_primary_10_1177_02670836251357334
Cites_doi	10.1061/(ASCE)CF.1943-5509.0000886 10.1016/S0893-6080(97)00134-2 10.1007/s11668-020-01006-5 10.3390/en15103750 10.1016/j.wear.2021.203646 10.1115/1.4043580 10.1016/j.neunet.2013.02.002 10.1016/j.molliq.2022.121194 10.1007/s11668-022-01400-1 10.1016/j.powtec.2011.12.016 10.1038/323533a0 10.1016/j.jclepro.2023.137153 10.1016/j.powtec.2015.06.073 10.1016/j.wear.2017.02.028 10.1016/j.wear.2013.02.014 10.1016/j.matdes.2021.110376 10.3390/en15051901 10.1016/j.engfailanal.2022.106688 10.3390/app14125234 10.3390/ma12030358 10.1016/j.engfailanal.2020.104909 10.1016/0308-0161(94)00055-N 10.1080/21642583.2019.1708830 10.3390/en5103892 10.1515/ntrev-2022-0493 10.1016/j.petsci.2023.01.010 10.1016/j.wear.2019.01.015 10.1007/s00521-021-06116-1 10.1016/j.wear.2007.03.016 10.3390/en14206609 10.1016/j.engfailanal.2017.07.011 10.3390/pr8060661 10.1007/s11668-018-0456-y 10.1007/s12206-023-0325-9 10.1016/j.heliyon.2023.e21275 10.1016/j.jngse.2022.104423
ContentType	Journal Article
Copyright	COPYRIGHT 2024 MDPI AG 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2024 MDPI AG – notice: 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS DOA
DOI	10.3390/app14209519
DatabaseName	CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Sciences (General)
EISSN	2076-3417
ExternalDocumentID	oai_doaj_org_article_1dacc1bcc4d14823a056da598377d96b A814381930 10_3390_app14209519
GroupedDBID	.4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c403t-a63c579ee78188c5c8e7b9feed9c1a8e001e9281459341fa0dc0d4451d8c17603
IEDL.DBID	DOA
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001341677100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2076-3417
IngestDate	Tue Oct 14 19:04:14 EDT 2025 Mon Jun 30 15:08:42 EDT 2025 Tue Nov 04 18:15:37 EST 2025 Sat Nov 29 07:12:31 EST 2025 Tue Nov 18 22:15:34 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	20
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c403t-a63c579ee78188c5c8e7b9feed9c1a8e001e9281459341fa0dc0d4451d8c17603
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-3433-5397
OpenAccessLink	https://doaj.org/article/1dacc1bcc4d14823a056da598377d96b
PQID	3120525230
PQPubID	2032433
ParticipantIDs	doaj_primary_oai_doaj_org_article_1dacc1bcc4d14823a056da598377d96b proquest_journals_3120525230 gale_infotracacademiconefile_A814381930 crossref_primary_10_3390_app14209519 crossref_citationtrail_10_3390_app14209519
PublicationCentury	2000
PublicationDate	2024-10-01
PublicationDateYYYYMMDD	2024-10-01
PublicationDate_xml	– month: 10 year: 2024 text: 2024-10-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Applied sciences
PublicationYear	2024
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Sedrez (ref_11) 2019; 426–427 Zangenehmadar (ref_27) 2016; 30 Bilal (ref_14) 2021; 476 Shah (ref_18) 2008; 264 Jantunen (ref_42) 2023; 407 Lai (ref_17) 2019; 141 Cui (ref_41) 2024; 10 Han (ref_34) 1995; 63 Xu (ref_3) 2022; 22 Wang (ref_2) 2023; 373 Shaik (ref_29) 2021; 33 Liao (ref_32) 2012; 5 Rumelhart (ref_36) 1986; 323 Xie (ref_8) 2023; 9 Wang (ref_12) 2023; 37 Deng (ref_25) 2021; 119 Beccati (ref_9) 2019; 7 Zhao (ref_19) 2022; 99 Zhou (ref_7) 2022; 214 Zhang (ref_33) 2022; 11 Macchini (ref_30) 2013; 1–2 Nasiri (ref_35) 2017; 81 ref_22 ref_21 ref_20 Jiang (ref_1) 2023; 20 Zhu (ref_13) 2022; 142 Huang (ref_31) 2021; 33 Pandya (ref_23) 2017; 378–379 ref_28 Xue (ref_37) 2020; 8 Cao (ref_16) 2023; 51 ref_26 Cui (ref_5) 2018; 18 Marinack (ref_10) 2012; 221 Nitta (ref_39) 2013; 43 Cheng (ref_40) 2013; 34 Liu (ref_15) 2016; 22 Hamey (ref_38) 1998; 11 Jia (ref_4) 2020; 20 ref_6 Shamshirband (ref_24) 2015; 284
References_xml	– volume: 51 start-page: 143 year: 2023 ident: ref_16 article-title: Erosion Factor Analysis of Coiled Tubing with Helical Buckling publication-title: China Pet. Mach. – volume: 30 start-page: 04016032 year: 2016 ident: ref_27 article-title: Assessment of Remaining Useful Life of Pipelines Using Different Artificial Neural Networks Models publication-title: J. Perform. Constr. Facil. doi: 10.1061/(ASCE)CF.1943-5509.0000886 – volume: 11 start-page: 669 year: 1998 ident: ref_38 article-title: XOR has No Local Minima: A Case Study in Neural Network Error Surface Analysis publication-title: Neural. Netw. doi: 10.1016/S0893-6080(97)00134-2 – volume: 20 start-page: 1928 year: 2020 ident: ref_4 article-title: Numerical Simulation on Coiled Tubing Erosion During Hydraulic Fracturing publication-title: J. Fail. Anal. Prev. doi: 10.1007/s11668-020-01006-5 – ident: ref_26 doi: 10.3390/en15103750 – volume: 476 start-page: 203646 year: 2021 ident: ref_14 article-title: Experimental and CFD Investigations of 45 and 90 Degrees Bends and Various Elbow Curvature Radii Effects on Solid Particle Erosion publication-title: Wear doi: 10.1016/j.wear.2021.203646 – volume: 141 start-page: 121302 year: 2019 ident: ref_17 article-title: Numerical Study of Solid Particle Erosion in a Centrifugal Pump for Liquid–Solid Flow publication-title: J. Fluid. Eng. doi: 10.1115/1.4043580 – volume: 43 start-page: 1 year: 2013 ident: ref_39 article-title: Local Minima in Hierarchical Structures of Complex-Valued Neural Networks publication-title: Neural. Netw. doi: 10.1016/j.neunet.2013.02.002 – volume: 10 start-page: 1 year: 2024 ident: ref_41 article-title: Study on the Erosion Performance of QT1100 Coiled Tubing Steel in Liquid-Solid Two-phase Flow publication-title: China Pet. Mach. – volume: 373 start-page: 121194 year: 2023 ident: ref_2 article-title: Effects of the Surfactant, Polymer, and crude oil properties on the formation and stabilization of oil-based foam liquid films: Insights from the microscale publication-title: J. Mol. Liq. doi: 10.1016/j.molliq.2022.121194 – volume: 22 start-page: 1276 year: 2022 ident: ref_3 article-title: Coiled Tubing Erosion Prediction and Fracturing Fluid Parameters Optimization During Hydraulic Jet Fracturing publication-title: J. Fail. Anal. Prev. doi: 10.1007/s11668-022-01400-1 – volume: 221 start-page: 47 year: 2012 ident: ref_10 article-title: An Eulerian, lattice-based cellular automata approach for modeling multiphase flows publication-title: Powder Technol. doi: 10.1016/j.powtec.2011.12.016 – volume: 323 start-page: 533 year: 1986 ident: ref_36 article-title: Learning Representations by Back-Propagating Errors publication-title: Nature doi: 10.1038/323533a0 – volume: 407 start-page: 137153 year: 2023 ident: ref_42 article-title: Influence of coal ashes on fired clay brick quality: Random forest regression and artificial neural networks modeling publication-title: J. Clean. Product. doi: 10.1016/j.jclepro.2023.137153 – volume: 284 start-page: 336 year: 2015 ident: ref_24 article-title: Performance Investigation of Micro- and Nano-Sized Particle Erosion in a 90° Elbow Using an ANFIS Model publication-title: Powder Technol. doi: 10.1016/j.powtec.2015.06.073 – volume: 378–379 start-page: 198 year: 2017 ident: ref_23 article-title: A Computational Fluid Dynamics Based Artificial Neural Network Model to Predict Solid Particle Erosion publication-title: Wear doi: 10.1016/j.wear.2017.02.028 – volume: 1–2 start-page: 21 year: 2013 ident: ref_30 article-title: Influence of particle size, density, particle concentration on bend erosive wear in pneumatic conveyors publication-title: Wear doi: 10.1016/j.wear.2013.02.014 – volume: 214 start-page: 110376 year: 2022 ident: ref_7 article-title: Experimental Study on Erosion-Corrosion Behavior of Liquid–Solid Swirling Flow in Pipeline publication-title: Mater. Des. doi: 10.1016/j.matdes.2021.110376 – ident: ref_20 doi: 10.3390/en15051901 – volume: 142 start-page: 106688 year: 2022 ident: ref_13 article-title: Research on Casing Erosion in Reservoir Section of Gas Storage Wells Based on Gas-Solid Two-Phase Flow publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2022.106688 – ident: ref_22 doi: 10.3390/app14125234 – ident: ref_6 doi: 10.3390/ma12030358 – volume: 119 start-page: 104909 year: 2021 ident: ref_25 article-title: Life Prediction of Slotted Screen Based on Back-Propagation Neural Network publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2020.104909 – volume: 63 start-page: 205 year: 1995 ident: ref_34 article-title: Artificial Neural Network Technology as a Method to Evaluate the Fatigue Life of Weldments with Welding Defects publication-title: Int. J. Press. Vessels. Pip. doi: 10.1016/0308-0161(94)00055-N – volume: 8 start-page: 22 year: 2020 ident: ref_37 article-title: A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm publication-title: Syst. Sci. Control. Eng. doi: 10.1080/21642583.2019.1708830 – volume: 33 start-page: 14771 year: 2021 ident: ref_31 article-title: A Novel Grey-Based Feature Ranking Method for Feature Subset Selection publication-title: J. Chin. Inst. Eng. – volume: 5 start-page: 3892 year: 2012 ident: ref_32 article-title: A Numerical Corrosion Rate Prediction Method for Direct Assessment of Wet Gas Gathering Pipelines Internal Corrosion publication-title: Energies doi: 10.3390/en5103892 – volume: 11 start-page: 3195 year: 2022 ident: ref_33 article-title: Gray Correlation Analysis of Factors Influencing Compressive Strength and Durability of Nano-SiO2 and PVA Fiber Reinforced Geopolymer Mortar publication-title: Nanotechnol. Rev. doi: 10.1515/ntrev-2022-0493 – volume: 20 start-page: 1843 year: 2023 ident: ref_1 article-title: Subsection and Superposition Method for Reservoir Formation Damage Evaluation of Complex-Structure Wells publication-title: Pet. Sci. doi: 10.1016/j.petsci.2023.01.010 – volume: 426–427 start-page: 570 year: 2019 ident: ref_11 article-title: Experiments and CFD Simulations of Erosion of a 90° Elbow in Liquid-Dominated Liquid-Solid and Dispersed-Bubble-Solid Flows publication-title: Wear doi: 10.1016/j.wear.2019.01.015 – volume: 33 start-page: 14771 year: 2021 ident: ref_29 article-title: An Intelligent Model to Predict the Life Condition of Crude Oil Pipelines Using Artificial Neural Networks publication-title: Neural. Comput. Appl. doi: 10.1007/s00521-021-06116-1 – volume: 264 start-page: 279 year: 2008 ident: ref_18 article-title: Coiled Tubing Erosion During Hydraulic Fracturing Slurry Flow publication-title: Wear doi: 10.1016/j.wear.2007.03.016 – ident: ref_21 doi: 10.3390/en14206609 – volume: 81 start-page: 270 year: 2017 ident: ref_35 article-title: Fracture Mechanics and Mechanical Fault Detection by Artificial Intelligence Methods: A review publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2017.07.011 – ident: ref_28 doi: 10.3390/pr8060661 – volume: 34 start-page: 1067 year: 2013 ident: ref_40 article-title: Numerical simulation of Erosion for API Round Thread Connection in a Solid-liquid Two-phase Flow publication-title: China Pet. Mach. – volume: 18 start-page: 640 year: 2018 ident: ref_5 article-title: Erosion–Corrosion Behavior of 20Cr Steel in Corrosive Solid–Liquid Two-Phase Flow Conditions publication-title: J. Fail. Anal. Prev. doi: 10.1007/s11668-018-0456-y – volume: 37 start-page: 1871 year: 2023 ident: ref_12 article-title: Study on Liquid-Solid Jet Erosion Characteristics of 316L Stainless Steel publication-title: J. Mech. Sci. Technol. doi: 10.1007/s12206-023-0325-9 – volume: 22 start-page: 80 year: 2016 ident: ref_15 article-title: Erosion Wear Law of Coiled Tubing Outer Wall publication-title: China Powder Sci. Technol. – volume: 9 start-page: e21275 year: 2023 ident: ref_8 article-title: Experimental Study on Particle Movement and Erosion Behavior of the Elbow in Liquid-Solid Flow publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e21275 – volume: 7 start-page: 316 year: 2019 ident: ref_9 article-title: Euerian Multi-Phase CFD model for predicting the performance of a centrifugal dredge pump publication-title: Int. J. Comp. Meth. Exp. Meas. – volume: 99 start-page: 104423 year: 2022 ident: ref_19 article-title: Numerical Study on the Particle Erosion of Elbows Mounted in Series in the Gas-Solid Flow publication-title: J. Nat. Gas. Sci. Eng. doi: 10.1016/j.jngse.2022.104423
SSID	ssj0000913810
Score	2.3112464
Snippet	Coiled tubing has been widely used in oilfield development because it can significantly improve oil well productivity and recovery efficiency. However, with...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	9519
SubjectTerms	Accuracy Algorithms Back propagation back-propagation neural network coiled tubing erosion rate prediction Experimental methods Flow velocity grey relational analysis Machine learning Neural networks Oil field services Oil fields Petroleum industry Petroleum mining Research methodology Simulation sparrow search algorithm Turbulence models Viscosity
SummonAdditionalLinks	– databaseName: ProQuest Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fb9MwED5BxwN7ADZAKwzkh0n8kKLZcVLHT6ibNoEEVQRDGk9RfHYLojQl7fj7uUvcMiTgicc2VmTrznffF5-_AzjSuQ_E_tPE1UgERXuZOFeTL1szpe3kUt_rzL41k0lxeWnLeD16FcsqNzGxC9S92jPXbVMQPvYN8hfzY61SbsBG-PnV8nvCPaT4rDU21LgJOyy8JQewU755V37afnNhDcxCyf6ania2z6fEKksZZtjfElOn3_-3KN2lnvO7_3fS9-BOhKBi3PvMHtwIi33YvSZMuA97ccuvxPOoS_3iPizLlk912JKimYrThgKKFxdXxK1n4owWxQ_eE3YVJ5QavaBfH5adxqPoi5rFeD6j-aw_f6MR-DUpW2Lss841BIuE0KQmfVW64BZt8wfw8fzs4vR1Ehs2JJhJvU7qkcbc2BAMwYACcyyCcXZKadiiqotAKTHYtFBZbil5TmvpUXpWSPMFKjOS-iEMFs0iHIDInCWanzoTCJ8qdMQKRzKlFxc2NajlEF5urFVhVDPnphrzilgNm7a6ZtohHG0HL3sRjz8PO2Gzb4ew8nb3R9POqriRK-VrROUQM88SqromBOnr3BLRN96O3BCesdNUHB9oQljHaw60LFbaqsYFN5wn2ExLONw4TRUDx6r65SOP_v34MdxOCV_1dYWHMFi3V-EJ3MIf6y-r9mn0_J9A9RQE priority: 102 providerName: ProQuest
Title	Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model
URI	https://www.proquest.com/docview/3120525230 https://doaj.org/article/1dacc1bcc4d14823a056da598377d96b
Volume	14
WOSCitedRecordID	wos001341677100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fa9swEBaj7cP2UNpuY1m7oofCfoCZJdmR9ZiUlA26YLoOuichnZSsLEuCk_bv753llgxW9tJH28KcdHe6-9DpO8ZOVBkion-ZeQcIUFTIM-8d2rLRE3QnL0PimT3X43F1dWXqjVZfVBOW6IHTwn0WwQEID1AEoqxUDiN2cKVBYKWD6XvafXNtNsBUuwcbQdRV6UKeQlxP58GikJRQmL9CUMvU_9h-3AaZsz2222WHfJCk2mfP4vyAvdjgDDxg-503rviHjjL640u2rBs6cKFF5osJP12grwd-eYOwd8pHKAV9uMC0kg8xagWOT9-XLf0iT_XGfDCbLprr9a8_OAJ-Z3WDYHraao0TfwcKNU4F45y6p81esR9no8vTL1nXSyGDIlfrzPUVlNrEqDFCV1BCFbU3E4yQBoSrIkaraGQlitJgXJu4PEAeiLwsVCB0P1ev2dZ8MY9vGC-8QQQuvY6YOgrwCNj6ucQfV0ZqUHmPfbpfXgsd0Tj1u5hZBBykC7uhix47eRi8TPwa_x42JD09DCFS7PYFmortTMX-z1R67D1p2ZLrokDguhsIOC0iwbKDinrBY0aLUzi6NwTb-fTKKiGp6R9itrdPIc0hey4xQUqFgUdsa93cxHdsB27X16vmmG0PR-P64rg1a3yqv36rf94B_jr86g
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFAk4AC0gAgX2UMRDsljv2lnvAaG0tGrUNLIgSO3JeB8piBAHJwXxp_iNzPgRigTceuBoe73yrr_9ZmYf3wBsy9h5jP5FYHKLAYp0PDAmRyxrNcHhZISrdWaHajRKjo91ugY_2rMwtK2y5cSKqF1haY78hQwFpVxDj_nV_EtAWaNodbVNoVHD4tB__4Yh2-Ll4DX-38dC7O-Ndw-CJqtAYCMul0HekzZW2nuFtiqxsU28MnqCtkLbME888rbXIgmjWCPDT3LuLHck4-USG6oel1jvJViPEOy8A-vp4Cg9Wc3qkMpmEvL6IKCUmtM6dBgJcmT0b6avyhDwNztQGbf9G_9bt9yE640bzfo17jdgzc824do5ccVN2Ghoa8GeNtraz27BPC1pZYrQyIoJ2y2QFB0bnxl8he1ht9GDN-h_sx00747h1dt5pVPJ6o3ZrD89xfYvP3zGEvZTkJYFUnIFb0ZCJ_hRo3pnPaM0c9Pb8O5C-uEOdGbFzN8FFhntjRRGefSxQ2swsu1xgRUnWigreReet3jIbKPITolBphlGZgSe7Bx4urC9KjyvhUj-XGyHgLUqQurh1Y2iPM0aMspCl1sbGmsjRzKwMkcv2OWxTqRSTvdMF54QLDPiOPwgmzdHNbBZpBaW9bH5FOlTE7ZaWGYN-S2yX5i89-_Hj-DKwfhomA0Ho8P7cFWgv1jvk9yCzrI88w_gsv26_LgoHzbjjMH7i8bwT7fvZMM
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFCE4AC0gAgX2UMRDsmrv2lnvAaGkTUTVyrJKkXoz3odL1RAHJwXx1_h1zPgRigTceuBoe2N5N99-M7M7-w3Atoisw-ifezo3GKAI63ta54hlJQucTprbRmf2UCZJfHKi0jX40Z2FobTKjhNroraloTXyHRFwKrmGHvNO0aZFpHuTt_MvHlWQop3WrpxGA5ED9_0bhm-LN_t7-F8_53wyPt5957UVBjwT-mLp5QNhIqmck2i3YhOZ2EmtCrQbygR57JDDneJxEEYK2b7IfWt8S5JeNjaBHPgC33sN1qXAoKcH66Nxkh6tVnhIcTMO_OZQoBDKpz3pIOTk1KjfzGBdLeBvNqE2dJM7__MQ3YXbrXvNhs182IA1N9uEW5dEFzdho6WzBXvZam6_ugfztKIdK0IpKwu2WyJZWnZ8ofEnbIxDSA-O0C9nIzT7luHV-3mtX8mahG02nJ5i_5efPmMLc-6lVYlUXcOekQAKflTSZNwzKj83vQ8frmQcHkBvVs7cQ2ChVk4LrqVD3zswGiPegc_xxbHi0gi_D687bGSmVWqngiHTDCM2AlJ2CUh92F41njcCJX9uNiKQrZqQqnh9o6xOs5akssDmxgTamNCSPKzI0Tu2eaRiIaVVA92HFwTRjLgPP8jk7REO7BapiGVD7D6tAFAXtjqIZi0pLrJf-Hz078fP4AYCNzvcTw4ew02ObmSTPrkFvWV14Z7AdfN1ebaonrZTjsHHq4bwT33LbV0
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+Coiled+Tubing+Erosion+Rate+Based+on+Sparrow+Search+Algorithm+Back-Propagation+Neural+Network+Model&rft.jtitle=Applied+sciences&rft.au=Yinping+Cao&rft.au=Fengying+Fang&rft.au=Guowei+Wang&rft.au=Wenyu+Zhu&rft.date=2024-10-01&rft.pub=MDPI+AG&rft.eissn=2076-3417&rft.volume=14&rft.issue=20&rft.spage=9519&rft_id=info:doi/10.3390%2Fapp14209519&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_1dacc1bcc4d14823a056da598377d96b
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon