{"id":"https://openalex.org/W4391149005","doi":"https://doi.org/10.1109/ic-nidc59918.2023.10390718","title":"Discrimination of Deepwater Drilling Overflow Based on EDF-FM Model","display_name":"Discrimination of Deepwater Drilling Overflow Based on EDF-FM Model","publication_year":2023,"publication_date":"2023-11-03","ids":{"openalex":"https://openalex.org/W4391149005","doi":"https://doi.org/10.1109/ic-nidc59918.2023.10390718"},"language":"en","primary_location":{"id":"doi:10.1109/ic-nidc59918.2023.10390718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic-nidc59918.2023.10390718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","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/A5108941848","display_name":"Kai Lang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kai Lang","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100532515","display_name":"Zhiming Yin","orcid":"https://orcid.org/0000-0003-0624-3241"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiming Yin","raw_affiliation_strings":["CNOOC Research Institute Co. Ltd,Beijing,China,100028"],"affiliations":[{"raw_affiliation_string":"CNOOC Research Institute Co. Ltd,Beijing,China,100028","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101650885","display_name":"Yanzhao Chen","orcid":"https://orcid.org/0000-0002-5657-413X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanzhao Chen","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695314","display_name":"Yonghua Li","orcid":"https://orcid.org/0000-0002-8173-8351"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghua Li","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China,100876"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China,100876","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110071310","display_name":"Zhong Li","orcid":"https://orcid.org/0000-0001-6421-6749"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong Li","raw_affiliation_strings":["CNOOC Research Institute Co. Ltd,Beijing,China,100028"],"affiliations":[{"raw_affiliation_string":"CNOOC Research Institute Co. Ltd,Beijing,China,100028","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019153748","display_name":"Deqiang Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deqiang Tian","raw_affiliation_strings":["CNOOC Research Institute Co. Ltd,Beijing,China,100028"],"affiliations":[{"raw_affiliation_string":"CNOOC Research Institute Co. Ltd,Beijing,China,100028","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5108941848"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.2034,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55135723,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"172","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9987999796867371,"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"}},{"id":"https://openalex.org/T10892","display_name":"Drilling and Well Engineering","score":0.9983000159263611,"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"}},{"id":"https://openalex.org/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/computer-science","display_name":"Computer science","score":0.7708004117012024},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4875604510307312},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48113954067230225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44906631112098694},{"id":"https://openalex.org/keywords/submarine-pipeline","display_name":"Submarine pipeline","score":0.44670921564102173},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4226991832256317},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.3641100227832794},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35135433077812195},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15740644931793213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708004117012024},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4875604510307312},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48113954067230225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44906631112098694},{"id":"https://openalex.org/C162284963","wikidata":"https://www.wikidata.org/wiki/Q17106102","display_name":"Submarine pipeline","level":2,"score":0.44670921564102173},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4226991832256317},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.3641100227832794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35135433077812195},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15740644931793213},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic-nidc59918.2023.10390718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic-nidc59918.2023.10390718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 8th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","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":12,"referenced_works":["https://openalex.org/W2096451472","https://openalex.org/W2295739661","https://openalex.org/W2919303361","https://openalex.org/W2935952048","https://openalex.org/W2972025550","https://openalex.org/W3153244761","https://openalex.org/W3198901893","https://openalex.org/W4205378138","https://openalex.org/W4312597122","https://openalex.org/W4360996767","https://openalex.org/W4362498764","https://openalex.org/W4379116557"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2364874348","https://openalex.org/W2353320871"],"abstract_inverted_index":{"In":[0],"the":[1,6,39,53,85,102,115,129],"realm":[2],"of":[3,19,41,55,104],"oilfield":[4],"production,":[5],"transition":[7],"from":[8,45],"offshore":[9],"to":[10,31,100],"deepwater":[11,24,46,138],"exploration":[12],"represents":[13],"a":[14,28,76],"burgeoning":[15],"frontier.":[16],"The":[17],"incorporation":[18],"artificial":[20],"intelligence,":[21],"particularly":[22],"in":[23,107,133],"drilling":[25,109],"operations,":[26],"offers":[27],"transformative":[29],"approach":[30],"addressing":[32],"inherent":[33],"challenges.":[34],"One":[35],"pivotal":[36],"application":[37],"is":[38],"analysis":[40],"multifaceted":[42],"production":[43],"data":[44],"oil":[47],"and":[48,93,126],"gas":[49],"wellbores,":[50],"which":[51],"facilitates":[52],"detection":[54,106],"potential":[56],"overflow":[57,105,135],"scenarios.":[58],"To":[59],"address":[60],"this":[61],"critical":[62],"concern,":[63],"our":[64],"study":[65],"introduces":[66],"an":[67],"innovative":[68],"end-to-end":[69],"deep":[70,120],"learning":[71,121],"fusion":[72],"model":[73,117],"augmented":[74],"with":[75],"factorization":[77],"machine":[78],"(EDF-FM).":[79],"This":[80],"proposed":[81],"architecture":[82],"seamlessly":[83],"integrates":[84],"Factorization":[86],"Machine":[87],"(FM),":[88],"Convolutional":[89],"Neural":[90],"Network":[91],"(CNN),":[92],"Long":[94],"Short-Term":[95],"Memory":[96],"(LSTM)":[97],"model,":[98],"aiming":[99],"enhance":[101],"accuracy":[103,125],"real-world":[108],"operations.":[110],"Comparative":[111],"evaluations":[112],"reveal":[113],"that":[114],"EDF-FM":[116],"outperforms":[118],"conventional":[119],"paradigms,":[122],"achieving":[123],"superior":[124],"Area":[127],"Under":[128],"Curve":[130],"(AUC)":[131],"metrics":[132],"identifying":[134],"conditions":[136],"within":[137],"wellbores.":[139]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}