{"id":"https://openalex.org/W4294982774","doi":"https://doi.org/10.1109/tgrs.2022.3205301","title":"Reservoir Prediction Based on Closed-Loop CNN and Virtual Well-Logging Labels","display_name":"Reservoir Prediction Based on Closed-Loop CNN and Virtual Well-Logging Labels","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294982774","doi":"https://doi.org/10.1109/tgrs.2022.3205301"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3205301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3205301","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5066815623","display_name":"Cao Song","orcid":"https://orcid.org/0000-0003-2401-2084"},"institutions":[{"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":true,"raw_author_name":"Cao Song","raw_affiliation_strings":["Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2401-2084","affiliations":[{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011509629","display_name":"Wenkai Lu","orcid":"https://orcid.org/0000-0003-0249-2144"},"institutions":[{"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":"Wenkai Lu","raw_affiliation_strings":["Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0249-2144","affiliations":[{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461336","display_name":"Yuqing Wang","orcid":"https://orcid.org/0000-0002-9789-4295"},"institutions":[{"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":"Yuqing Wang","raw_affiliation_strings":["Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9789-4295","affiliations":[{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055727134","display_name":"Songbai Jin","orcid":"https://orcid.org/0000-0002-0099-0361"},"institutions":[{"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":"Songbai Jin","raw_affiliation_strings":["Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061960050","display_name":"Jinliang Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I106994412","display_name":"Sinopec (China)","ror":"https://ror.org/0161q6d74","country_code":"CN","type":"company","lineage":["https://openalex.org/I106994412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinliang Tang","raw_affiliation_strings":["Sinopec Geophysical Research Institute, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sinopec Geophysical Research Institute, Nanjing, China","institution_ids":["https://openalex.org/I106994412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100333398","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0001-5857-7812"},"institutions":[{"id":"https://openalex.org/I106994412","display_name":"Sinopec (China)","ror":"https://ror.org/0161q6d74","country_code":"CN","type":"company","lineage":["https://openalex.org/I106994412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["Sinopec Geophysical Research Institute, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sinopec Geophysical Research Institute, Nanjing, China","institution_ids":["https://openalex.org/I106994412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5066815623"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":4.3555,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.95433071,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9991999864578247,"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":0.9987999796867371,"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":0.8262474536895752},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.659500241279602},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5900635719299316},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5854529738426208},{"id":"https://openalex.org/keywords/logging","display_name":"Logging","score":0.5654447078704834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4649551808834076},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45532822608947754},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45503494143486023},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41561439633369446},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3577292263507843}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8262474536895752},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.659500241279602},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5900635719299316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5854529738426208},{"id":"https://openalex.org/C125620115","wikidata":"https://www.wikidata.org/wiki/Q845249","display_name":"Logging","level":2,"score":0.5654447078704834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4649551808834076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45532822608947754},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45503494143486023},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41561439633369446},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3577292263507843},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3205301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3205301","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2518711674","display_name":null,"funder_award_id":"41674116","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3078455064","display_name":null,"funder_award_id":"41974126","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G931848387","display_name":null,"funder_award_id":"2018YFA0702501","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1996020380","https://openalex.org/W2044843071","https://openalex.org/W2136358092","https://openalex.org/W2194775991","https://openalex.org/W2314470212","https://openalex.org/W2335411654","https://openalex.org/W2494440777","https://openalex.org/W2775766718","https://openalex.org/W2883622716","https://openalex.org/W2894410771","https://openalex.org/W2896338148","https://openalex.org/W2911281319","https://openalex.org/W2921353619","https://openalex.org/W2930361040","https://openalex.org/W2934792905","https://openalex.org/W2962793481","https://openalex.org/W2968247551","https://openalex.org/W2970966188","https://openalex.org/W2973222818","https://openalex.org/W2980122601","https://openalex.org/W2984603160","https://openalex.org/W2990011639","https://openalex.org/W3005407459","https://openalex.org/W3006114742","https://openalex.org/W3013404951","https://openalex.org/W3014516442","https://openalex.org/W3034667926","https://openalex.org/W3037649759","https://openalex.org/W3049491668","https://openalex.org/W3090542416","https://openalex.org/W3096139392","https://openalex.org/W3097879628","https://openalex.org/W3102406053","https://openalex.org/W3106541595","https://openalex.org/W3117351614","https://openalex.org/W3126401903","https://openalex.org/W3133999455","https://openalex.org/W3140751667","https://openalex.org/W3157386224","https://openalex.org/W3159428095","https://openalex.org/W3162849538","https://openalex.org/W3170600903","https://openalex.org/W3175147126","https://openalex.org/W3194166122","https://openalex.org/W3197253964","https://openalex.org/W3197823310","https://openalex.org/W3199826914","https://openalex.org/W3200923794","https://openalex.org/W3211037489","https://openalex.org/W3212186901","https://openalex.org/W3212474542","https://openalex.org/W4200554830","https://openalex.org/W4207022706","https://openalex.org/W4210291024","https://openalex.org/W4242084824","https://openalex.org/W4285272371","https://openalex.org/W6676132248"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W2748454020","https://openalex.org/W3119610945","https://openalex.org/W4306674287","https://openalex.org/W4287776258","https://openalex.org/W3027997911","https://openalex.org/W2767651786","https://openalex.org/W3021430260","https://openalex.org/W2912288872","https://openalex.org/W564581980"],"abstract_inverted_index":{"Reservoir":[0],"prediction":[1,21,51,173],"is":[2,11,83],"a":[3,15,46,56,61],"significant":[4],"issue":[5],"in":[6,36,65,156],"seismic":[7,98],"interpretation,":[8],"and":[9,23,73,88,134,147,169],"it":[10],"difficult":[12],"to":[13,142],"reach":[14],"tradeoff":[16],"point":[17],"for":[18,49,54],"the":[19,67,86,97,120,135,144,150,177],"reservoir":[20,37,125,152,172],"accuracy":[22,148],"spatial":[24,145],"continuity.":[25],"Nowadays,":[26],"though":[27],"numerous":[28],"machine":[29],"learning":[30],"methods":[31],"have":[32],"been":[33],"widely":[34],"applied":[35],"prediction,":[38],"so":[39],"few":[40],"available":[41],"well-logging":[42,75,104,109,113],"labels":[43,76,105,114],"are":[44,77,116,166],"still":[45],"major":[47],"obstacle":[48],"improving":[50],"performance.":[52],"Considering":[53],"such":[55],"critical":[57],"factor,":[58],"we":[59],"propose":[60],"semisupervised":[62],"deep-learning":[63],"framework,":[64],"which":[66,82],"closed-loop":[68,80],"convolutional":[69],"neural":[70],"network":[71,157],"(CNN).":[72],"virtual":[74,112],"used.":[78],"The":[79,111],"CNN,":[81],"consisting":[84],"of":[85,122,149],"predictive":[87],"generative":[89],"subnetworks,":[90],"can":[91],"be":[92],"trained":[93],"directly":[94],"by":[95,118],"using":[96],"attribute":[99],"data":[100,165],"not":[101],"only":[102],"with":[103],"but":[106],"also":[107],"without":[108],"labels.":[110],"(Vl)":[115],"generated":[117],"fusing":[119],"results":[121,174],"two":[123],"existing":[124,182],"predicting":[126],"methods,":[127],"one":[128],"based":[129,137],"on":[130,138,163],"polynomial":[131],"linear":[132],"regression":[133],"other":[136],"CNN.":[139],"Vl":[140],"contributes":[141],"improve":[143],"continuity":[146],"predicted":[151],"as":[153],"constraint":[154],"items":[155],"training":[158],"process.":[159],"Finally,":[160],"cross-validation":[161],"experiments":[162],"real-field":[164],"carried":[167],"out,":[168],"3-D":[170],"field":[171],"show":[175],"that":[176],"proposed":[178],"method":[179],"outperforms":[180],"several":[181],"machine-learning-based":[183],"methods.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2025-10-10T00:00:00"}