{"id":"https://openalex.org/W4404471097","doi":"https://doi.org/10.1145/3696952.3696964","title":"Deep learning time series prediction models in Logging Curve prediction and generation","display_name":"Deep learning time series prediction models in Logging Curve prediction and generation","publication_year":2024,"publication_date":"2024-11-18","ids":{"openalex":"https://openalex.org/W4404471097","doi":"https://doi.org/10.1145/3696952.3696964"},"language":"en","primary_location":{"id":"doi:10.1145/3696952.3696964","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696952.3696964","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 9th International Conference on Intelligent Information Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3696952.3696964","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xi Wang","orcid":"https://orcid.org/0009-0007-4640-3883"},"institutions":[{"id":"https://openalex.org/I14116566","display_name":"Wuhan Polytechnic University","ror":"https://ror.org/05w0e5j23","country_code":"CN","type":"education","lineage":["https://openalex.org/I14116566"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Wang","raw_affiliation_strings":["Wuhan Polytechnic, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0009-0007-4640-3883","affiliations":[{"raw_affiliation_string":"Wuhan Polytechnic, Wuhan, Hubei, China","institution_ids":["https://openalex.org/I14116566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100532370","display_name":"Zhen Ning","orcid":"https://orcid.org/0000-0002-2901-2991"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhen Ning","raw_affiliation_strings":["Huazhong University of Scicence and Technology, Wuhan, Hubei, China"],"raw_orcid":"https://orcid.org/0000-0002-2901-2991","affiliations":[{"raw_affiliation_string":"Huazhong University of Scicence and Technology, Wuhan, Hubei, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044581192","display_name":"Tangyan Liu","orcid":"https://orcid.org/0000-0001-5586-8892"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tangyan Liu","raw_affiliation_strings":["Tongji University, Shanghai, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5586-8892","affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102220945","display_name":"Yan Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130811","display_name":"Rajamangala University of Technology Tawan-ok","ror":"https://ror.org/03cvxzw02","country_code":"TH","type":"education","lineage":["https://openalex.org/I10245363","https://openalex.org/I4210130811"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Yan Cao","raw_affiliation_strings":["Rajamangala University of Technology Tawan-Ok, Bangkok, Bangkok, Thailand"],"raw_orcid":"https://orcid.org/0009-0002-4535-573X","affiliations":[{"raw_affiliation_string":"Rajamangala University of Technology Tawan-Ok, Bangkok, Bangkok, Thailand","institution_ids":["https://openalex.org/I4210130811"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2815,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52729487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"80","last_page":"88"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.7024451494216919},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6362614631652832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5847014784812927},{"id":"https://openalex.org/keywords/logging","display_name":"Logging","score":0.5672587752342224},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5250106453895569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5195220112800598},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4973175823688507},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.482006698846817},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42031028866767883},{"id":"https://openalex.org/keywords/learning-curve","display_name":"Learning curve","score":0.4164879024028778},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09483516216278076},{"id":"https://openalex.org/keywords/forestry","display_name":"Forestry","score":0.06334128975868225}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7024451494216919},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6362614631652832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5847014784812927},{"id":"https://openalex.org/C125620115","wikidata":"https://www.wikidata.org/wiki/Q845249","display_name":"Logging","level":2,"score":0.5672587752342224},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5250106453895569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5195220112800598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4973175823688507},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.482006698846817},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42031028866767883},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.4164879024028778},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09483516216278076},{"id":"https://openalex.org/C97137747","wikidata":"https://www.wikidata.org/wiki/Q38112","display_name":"Forestry","level":1,"score":0.06334128975868225},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3696952.3696964","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696952.3696964","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 9th International Conference on Intelligent Information Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3696952.3696964","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696952.3696964","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 9th International Conference on Intelligent Information Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W10891775","https://openalex.org/W2064675550","https://openalex.org/W2766259095","https://openalex.org/W2896338148","https://openalex.org/W2902264310","https://openalex.org/W2910852930","https://openalex.org/W3036970862","https://openalex.org/W4310540641"],"related_works":["https://openalex.org/W2373969208","https://openalex.org/W2578023326","https://openalex.org/W2800852182","https://openalex.org/W2177102068","https://openalex.org/W60557881","https://openalex.org/W1972416716","https://openalex.org/W2378381981","https://openalex.org/W4385335406","https://openalex.org/W4282583532","https://openalex.org/W4256076151"],"abstract_inverted_index":{"Due":[0],"to":[1,18,114,189],"the":[2,6,19,32,37,54,92,97,109,115,143,159,171,177,180,184,204,207],"complex":[3,116],"underground":[4,117],"conditions,":[5],"logging":[7,27,58,111,178],"data":[8,59,94,112,149,154,162],"are":[9,122],"often":[10],"distorted":[11,146,172],"or":[12,56,147,173],"missing":[13,55,110,148,161,174],"which":[14],"brings":[15],"great":[16],"challenges":[17],"work":[20,205],"of":[21,26,36,60,67,176,183,209],"well":[22,210],"logging.":[23,211],"Precise":[24],"prediction":[25,44],"curve":[28],"is":[29,155,164,191],"critical":[30],"for":[31],"exploration":[33],"and":[34,107,133,150,152,158,193],"development":[35],"petroleum":[38],"engineering.":[39],"In":[40],"this":[41,199],"paper":[42,200],"different":[43],"models":[45,186],"using":[46],"deep":[47,69],"learning":[48,70],"methods":[49,196],"was":[50,88],"presented":[51],"based":[52,123],"on":[53,124],"incomplete":[57],"oil":[61],"wells":[62,98],"in":[63,99,198,206],"China.":[64],"The":[65,102,119,195],"performance":[66,181],"three":[68,185],"methods,":[71],"namely":[72],"Back":[73],"Propagation":[74],"Neural":[75],"Network(BPNN),":[76],"Long":[77,83],"Short-Term":[78,84],"Memory":[79,85],"(LSTM)":[80],"model,":[81],"enhanced":[82],"(FC_LSTM)":[86],"model":[87,104],"experimentally":[89],"investigated":[90],"with":[91],"training":[93,103],"collected":[95],"from":[96,187],"adjacent":[100],"areas.":[101],"could":[105,201],"predict":[106],"complete":[108],"due":[113],"conditions.":[118],"evaluation":[120],"criteria":[121],"mean":[125,129,134],"square":[126],"error":[127,131,137],"(MSE),":[128],"absolute":[130,135],"(MAE)":[132],"percentage":[136],"(MAPE).":[138],"Without":[139],"additional":[140],"measurement":[141],"cost,":[142],"correlation":[144],"between":[145],"previous":[151],"subsequent":[153],"fully":[156],"considered":[157],"whole":[160],"block":[163],"reconstructed":[165],"through":[166],"iterative":[167],"strategy.":[168],"When":[169],"predicting":[170],"part":[175],"data,":[179],"ranking":[182],"high":[188],"low":[190],"FC_LSTM,LSTM":[192],"BPNN.":[194],"proposed":[197],"accurately":[202],"improves":[203],"fields":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
