{"id":"https://openalex.org/W4385483109","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191130","title":"Deep Feature Extraction for Data Assimilation with Ensemble Smoother","display_name":"Deep Feature Extraction for Data Assimilation with Ensemble Smoother","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385483109","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191130"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191130","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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/A5079691666","display_name":"Rodrigo Exterkoetter","orcid":"https://orcid.org/0000-0002-3639-1635"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rodrigo Exterkoetter","raw_affiliation_strings":["LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil"],"affiliations":[{"raw_affiliation_string":"LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088307781","display_name":"Gustavo Rachid Dutra","orcid":"https://orcid.org/0000-0002-9091-9541"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gustavo Rachid Dutra","raw_affiliation_strings":["LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil"],"affiliations":[{"raw_affiliation_string":"LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056979330","display_name":"Leandro Passos de Figueiredo","orcid":"https://orcid.org/0000-0002-3694-3938"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leandro Passos de Figueiredo","raw_affiliation_strings":["LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil"],"affiliations":[{"raw_affiliation_string":"LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037040676","display_name":"Fernando Bordignon","orcid":"https://orcid.org/0000-0001-5205-8042"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fernando Luis Bordignon","raw_affiliation_strings":["LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil"],"affiliations":[{"raw_affiliation_string":"LTrace Geophysical Solutions,Florian&#x00F3;polis,Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053643611","display_name":"Alexandre A. Emerick","orcid":"https://orcid.org/0000-0002-4921-4902"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alexandre Anoze Emerick","raw_affiliation_strings":["Petrobras,Rio de Janeiro,Brazil","Petrobras, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Petrobras,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I32393484"]},{"raw_affiliation_string":"Petrobras, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I32393484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045510293","display_name":"Gilson Moura Silva Neto","orcid":"https://orcid.org/0000-0003-1218-3572"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Gilson Moura Silva Neto","raw_affiliation_strings":["Petrobras,Rio de Janeiro,Brazil","Petrobras, Rio de Janeiro, Brazil"],"affiliations":[{"raw_affiliation_string":"Petrobras,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I32393484"]},{"raw_affiliation_string":"Petrobras, Rio de Janeiro, Brazil","institution_ids":["https://openalex.org/I32393484"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090517396","display_name":"Mauro Roisenberg","orcid":"https://orcid.org/0000-0001-9707-0360"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mauro Roisenberg","raw_affiliation_strings":["Federal University of Santa Catarina,Florian&#x00F3;polis,Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Santa Catarina,Florian&#x00F3;polis,Brazil","institution_ids":["https://openalex.org/I4104125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5079691666"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2034,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49365214,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"55","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994000196456909,"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"}},{"id":"https://openalex.org/T10892","display_name":"Drilling and Well Engineering","score":0.9990000128746033,"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/autoencoder","display_name":"Autoencoder","score":0.7770577669143677},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.660483181476593},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6420652270317078},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5559040307998657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5276383757591248},{"id":"https://openalex.org/keywords/data-assimilation","display_name":"Data assimilation","score":0.5170907974243164},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4364210069179535},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4219893217086792},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41434794664382935},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4004255533218384},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.36571013927459717},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3281899690628052}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7770577669143677},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.660483181476593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6420652270317078},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5559040307998657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5276383757591248},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.5170907974243164},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4364210069179535},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4219893217086792},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41434794664382935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4004255533218384},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.36571013927459717},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3281899690628052},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191130","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W1987308763","https://openalex.org/W2004124997","https://openalex.org/W2007339694","https://openalex.org/W2059422848","https://openalex.org/W2078327207","https://openalex.org/W2103553931","https://openalex.org/W2528150856","https://openalex.org/W2744373723","https://openalex.org/W2766701559","https://openalex.org/W2891219912","https://openalex.org/W2952122856","https://openalex.org/W2968171911","https://openalex.org/W2981785001","https://openalex.org/W3014897790","https://openalex.org/W3047026237","https://openalex.org/W3106811345","https://openalex.org/W3137388315","https://openalex.org/W4224293263","https://openalex.org/W4253970696","https://openalex.org/W4283017325","https://openalex.org/W4388322712","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6654991188","https://openalex.org/W6764020417","https://openalex.org/W6847512338"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2803255133"],"abstract_inverted_index":{"History":[0,101],"matching":[1],"is":[2,46,88,112,165],"applied":[3,139],"to":[4,19,109,118,123,140,178,215],"update":[5,179,233],"reservoir":[6,44,181,217],"parameters,":[7],"such":[8],"as":[9,93],"the":[10,15,30,34,40,47,69,78,82,91,119,124,131,149,161,180,231,236,241],"porosity":[11],"and":[12,26,74,96,147,174,239],"permeability":[13],"of":[14,52,81,106,127,130,170,184],"sub-surface":[16],"rocks,":[17],"according":[18],"new":[20],"indirect":[21,65],"observations.":[22],"Local":[23],"fluid":[24,72,79],"production":[25,59],"pressure":[27,75],"measurements":[28],"in":[29,39,71,90,230],"drilled":[31],"wells":[32],"are":[33],"commonly":[35],"dynamic":[36,43],"observations":[37],"used":[38],"process.":[41],"Another":[42],"observation":[45],"time-lapse":[48,171],"seismic":[49,110,172],"data,":[50],"consisting":[51],"several":[53],"elastic":[54],"parameter":[55],"cubes":[56],"at":[57],"different":[58],"time":[60,238],"steps.":[61],"This":[62],"data":[63,111,128,146,151,173],"provide":[64],"spatial":[66],"information":[67,154],"about":[68],"changes":[70],"saturation":[73],"caused":[76],"by":[77],"flow":[80],"production.":[83],"Ensemble":[84],"Smoother":[85],"Multi-Data":[86],"Assimilation":[87],"presented":[89],"literature":[92],"a":[94,114,186,220,227],"sound":[95],"stable":[97],"algorithm":[98,108],"for":[99,144,167],"solving":[100],"Matching":[102],"problems.":[103],"The":[104,224],"application":[105],"this":[107,159,210],"still":[113],"significant":[115],"challenge":[116],"due":[117],"dimensionality":[120],"issues":[121],"related":[122],"high":[125],"number":[126],"points":[129],"seismic.":[132],"Deep":[133],"learning":[134,163,222],"methods":[135],"have":[136],"been":[137],"successfully":[138],"learn":[141],"feature":[142,168],"representations":[143],"high-dimensional":[145],"represent":[148],"original":[150],"into":[152],"latent":[153],"with":[155,176,196,202],"lower":[156],"dimensions.":[157],"In":[158],"paper,":[160],"deep":[162,187,194],"method":[164],"exploited":[166],"extraction":[169],"integrated":[175],"ES-MDA":[177,232],"parameters.":[182],"Instead":[183],"training":[185],"model":[188,245],"from":[189,207],"scratch,":[190],"we":[191],"propose":[192],"using":[193],"models":[195],"fully":[197],"convolutional":[198],"autoencoder":[199],"structures":[200],"trained":[201],"natural":[203],"images":[204],"dataset.":[205],"Different":[206],"other":[208],"proposals,":[209],"work":[211],"can":[212],"be":[213],"adapted":[214],"any":[216],"case":[218],"through":[219],"transfer":[221],"step.":[223],"result":[225],"shows":[226],"considered":[228],"improvement":[229],"process,":[234],"reducing":[235],"processing":[237],"increasing":[240],"ensemble":[242],"variability":[243],"between":[244],"realizations.":[246]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
