{"id":"https://openalex.org/W3010496653","doi":"https://doi.org/10.1109/ist48021.2019.9010576","title":"Imaging of flow pattern of gas-oil flows with convolutional neural network","display_name":"Imaging of flow pattern of gas-oil flows with convolutional neural network","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3010496653","doi":"https://doi.org/10.1109/ist48021.2019.9010576","mag":"3010496653"},"language":"en","primary_location":{"id":"doi:10.1109/ist48021.2019.9010576","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist48021.2019.9010576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065317043","display_name":"Zhuoqun Xu","orcid":"https://orcid.org/0000-0002-3535-4402"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoqun Xu","raw_affiliation_strings":["Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School at Shenzhen, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082575803","display_name":"Xinmeng Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinmeng Yang","raw_affiliation_strings":["Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School at Shenzhen, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101789411","display_name":"Bing Chen","orcid":"https://orcid.org/0000-0002-1079-6167"},"institutions":[{"id":"https://openalex.org/I98227222","display_name":"China National Petroleum Corporation (China)","ror":"https://ror.org/05269d038","country_code":"CN","type":"company","lineage":["https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Chen","raw_affiliation_strings":["CNPC Beijing Richfit Information Technology Co. Ltd, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CNPC Beijing Richfit Information Technology Co. Ltd, Beijing, China","institution_ids":["https://openalex.org/I98227222"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101951237","display_name":"Maomao Zhang","orcid":"https://orcid.org/0000-0002-1742-4665"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maomao Zhang","raw_affiliation_strings":["Shenzhen Leengstar Co. Ltd., Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Leengstar Co. Ltd., Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100421652","display_name":"Yi Li","orcid":"https://orcid.org/0000-0002-8855-4520"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Li","raw_affiliation_strings":["Graduate School at Shenzhen, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School at Shenzhen, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6055,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.70572876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11778","display_name":"Electrical and Bioimpedance Tomography","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12537","display_name":"Flow Measurement and Analysis","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9890000224113464,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7360414862632751},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6480159759521484},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.5111783146858215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3867899179458618},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.32471638917922974},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.20858609676361084},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09778770804405212},{"id":"https://openalex.org/keywords/mechanics","display_name":"Mechanics","score":0.09708860516548157}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7360414862632751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6480159759521484},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.5111783146858215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3867899179458618},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.32471638917922974},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.20858609676361084},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09778770804405212},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.09708860516548157}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist48021.2019.9010576","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist48021.2019.9010576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1976645109","https://openalex.org/W2116818806","https://openalex.org/W2138574603","https://openalex.org/W2166537410","https://openalex.org/W2313750921","https://openalex.org/W2462948808","https://openalex.org/W2753551423","https://openalex.org/W2914167033"],"related_works":["https://openalex.org/W2998526951","https://openalex.org/W3119610945","https://openalex.org/W2735477435","https://openalex.org/W2285788670","https://openalex.org/W3181746755","https://openalex.org/W2521062615","https://openalex.org/W2901465038","https://openalex.org/W3090822330","https://openalex.org/W2749468216","https://openalex.org/W4239686595"],"abstract_inverted_index":{"Gas":[0],"volume":[1],"fraction":[2],"(GVF)":[3],"is":[4,26,42,76,85,203,245],"an":[5,43,271],"important":[6],"parameter":[7],"for":[8,30,187,205,211,216],"the":[9,18,31,51,56,89,106,109,166,217,225,253,261,265,278],"measurement":[10,16,35,178,208],"of":[11,17,20,27,36,59,80,108,133,139,200,236,255,264,275],"oil-gas":[12,74,96,137],"two-phase":[13,24,97,143],"flow.":[14],"Online":[15],"GVF":[19,53,201],"oil":[21,92,140,188],"and":[22,34,48,73,91,117,122,136,141,182,194,214,259,277],"gas":[23,90,142,191],"flow":[25,71,93,135,144,167,171,189,192],"great":[28],"significance":[29],"safety":[32],"monitoring":[33],"oilfield":[37,60],"production":[38],"processes.":[39],"So":[40],"it":[41],"urgent":[44],"problem":[45,107],"to":[46,55,87,164],"quickly":[47],"accurately":[49],"detect":[50],"real-time":[52],"according":[54],"non-destructive":[57],"testing":[58],"field":[61],"devices.":[62],"In":[63],"this":[64],"paper,":[65],"a":[66],"method":[67,79],"based":[68],"on":[69],"different":[70,134],"rates":[72],"ratios":[75,138],"proposed.":[77],"The":[78,131,149,169,196,220,240,267],"convolutional":[81,183],"neural":[82,184],"network":[83,185],"(CNN)":[84],"used":[86,244],"predict":[88],"rate":[94],"in":[95],"flows.":[98],"Compared":[99],"with":[100],"traditional":[101,129],"algorithms,":[102],"CNN":[103,218,221],"algorithm":[104,162,181,186,222],"solves":[105],"relationship":[110],"between":[111],"high-dimensional":[112],"data":[113,119,132,150],"(streaming":[114],"image":[115,177,207],"pixels)":[116],"low-dimensional":[118],"(GVF":[120],"values":[121],"traffic)":[123],"that":[124],"cannot":[125],"be":[126],"solved":[127],"by":[128,147,152,175],"algorithms.":[130],"were":[145],"collected":[146,151],"experiment.":[148],"electrical":[153],"capacitance":[154],"tomography":[155],"(ECT)":[156],"was":[157],"reconstructed":[158,170],"using":[159],"linear":[160],"projection":[161],"(LBP)":[163],"obtain":[165],"pattern.":[168],"graphs":[172],"are":[173],"predicted":[174],"Binarized":[176,206],"algorithm,":[179,209,213],"SVM":[180,212],"rate,":[190,193],"GVF.":[195],"average":[197],"relative":[198],"error":[199,273],"prediction":[202],"43%":[204],"8%":[210],"5%":[215,276],"algorithm.":[219],"effectively":[223],"avoids":[224],"possible":[226],"over-fitting":[227],"problem.":[228],"Its":[229],"loss":[230],"function":[231],"uses":[232],"ElasticNet":[233],"regression":[234],"instead":[235],"least":[237],"squares":[238],"regression.":[239],"inception":[241],"V3":[242],"model":[243,269],"decomposed":[246],"into":[247],"small":[248],"convolutions,":[249],"which":[250],"can":[251,280],"reduce":[252,257],"amount":[254],"parameters,":[256],"over-fitting,":[258],"enhance":[260],"nonlinear":[262],"expression":[263],"network.":[266],"final":[268],"has":[270],"allowable":[272],"range":[274],"accuracy":[279],"reach":[281],"more":[282],"than":[283],"90%.":[284]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
