{"id":"https://openalex.org/W2966066480","doi":"https://doi.org/10.1109/access.2019.2931990","title":"Advancing Deep Learning to Improve Upstream Petroleum Monitoring","display_name":"Advancing Deep Learning to Improve Upstream Petroleum Monitoring","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2966066480","doi":"https://doi.org/10.1109/access.2019.2931990","mag":"2966066480"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2931990","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2931990","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08781783.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08781783.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036339479","display_name":"Cristina Heghedus","orcid":"https://orcid.org/0000-0003-2146-3824"},"institutions":[{"id":"https://openalex.org/I92008406","display_name":"University of Stavanger","ror":"https://ror.org/02qte9q33","country_code":"NO","type":"education","lineage":["https://openalex.org/I92008406"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Cristina Heghedus","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, University of Stavanger, Stavanger, Norway"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, University of Stavanger, Stavanger, Norway","institution_ids":["https://openalex.org/I92008406"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029080041","display_name":"Anton Shchipanov","orcid":"https://orcid.org/0000-0002-6422-5909"},"institutions":[{"id":"https://openalex.org/I4210107808","display_name":"NORCE Research AS","ror":"https://ror.org/02gagpf75","country_code":"NO","type":"facility","lineage":["https://openalex.org/I4210107808"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Anton Shchipanov","raw_affiliation_strings":["NORCE Norwegian Research Center, Stavanger, Norway"],"affiliations":[{"raw_affiliation_string":"NORCE Norwegian Research Center, Stavanger, Norway","institution_ids":["https://openalex.org/I4210107808"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108044249","display_name":"Chunming Rong","orcid":"https://orcid.org/0000-0002-8347-0539"},"institutions":[{"id":"https://openalex.org/I92008406","display_name":"University of Stavanger","ror":"https://ror.org/02qte9q33","country_code":"NO","type":"education","lineage":["https://openalex.org/I92008406"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Chunming Rong","raw_affiliation_strings":["Department of Computer Science and Electrical Engineering, University of Stavanger, Stavanger, Norway"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Electrical Engineering, University of Stavanger, Stavanger, Norway","institution_ids":["https://openalex.org/I92008406"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036339479"],"corresponding_institution_ids":["https://openalex.org/I92008406"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.3187,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.79831211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"106248","last_page":"106259"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","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"}},{"id":"https://openalex.org/T13050","display_name":"Oil and Gas Production Techniques","score":0.9984999895095825,"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":0.9886999726295471,"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/computer-science","display_name":"Computer science","score":0.7652150392532349},{"id":"https://openalex.org/keywords/upstream","display_name":"Upstream (networking)","score":0.7220122218132019},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6049177646636963},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5704519152641296},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.5496604442596436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5370290279388428},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.5196917653083801},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49184417724609375},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47087013721466064},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.462015300989151},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.41813042759895325},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41614654660224915},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.2820245027542114}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7652150392532349},{"id":"https://openalex.org/C191172861","wikidata":"https://www.wikidata.org/wiki/Q7899321","display_name":"Upstream (networking)","level":2,"score":0.7220122218132019},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6049177646636963},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5704519152641296},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.5496604442596436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5370290279388428},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.5196917653083801},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49184417724609375},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47087013721466064},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.462015300989151},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41813042759895325},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41614654660224915},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.2820245027542114},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/access.2019.2931990","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2931990","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08781783.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:16edbfe0bab94229944e995881d06ad7","is_oa":true,"landing_page_url":"https://doaj.org/article/16edbfe0bab94229944e995881d06ad7","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 106248-106259 (2019)","raw_type":"article"},{"id":"pmh:oai:norceresearch.brage.unit.no:11250/2652164","is_oa":true,"landing_page_url":"https://hdl.handle.net/11250/2652164","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access","raw_type":"info:eu-repo/semantics/other"},{"id":"pmh:oai:uis.brage.unit.no:11250/3046170","is_oa":true,"landing_page_url":"https://hdl.handle.net/11250/3046170","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"106248-106259","raw_type":"info:eu-repo/semantics/other"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2931990","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2931990","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08781783.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320315101","display_name":"Universitetet i Stavanger","ror":"https://ror.org/02qte9q33"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2966066480.pdf","grobid_xml":"https://content.openalex.org/works/W2966066480.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1989729184","https://openalex.org/W2008772556","https://openalex.org/W2016103205","https://openalex.org/W2024326516","https://openalex.org/W2042037135","https://openalex.org/W2076063813","https://openalex.org/W2167074184","https://openalex.org/W2295959395","https://openalex.org/W2298497999","https://openalex.org/W2543643230","https://openalex.org/W2556760470","https://openalex.org/W2587586954","https://openalex.org/W2761663138","https://openalex.org/W2761834891","https://openalex.org/W2765258233","https://openalex.org/W2809317444","https://openalex.org/W2824151737","https://openalex.org/W3104996215","https://openalex.org/W4255949318"],"related_works":["https://openalex.org/W2606910468","https://openalex.org/W2734185189","https://openalex.org/W2799656149","https://openalex.org/W4382361623","https://openalex.org/W2981300935","https://openalex.org/W1545859685","https://openalex.org/W2964588166","https://openalex.org/W4304142418","https://openalex.org/W2739620896","https://openalex.org/W1510432176"],"abstract_inverted_index":{"Data":[0],"analytics":[1],"is":[2,254],"rapidly":[3],"growing":[4],"field":[5],"in":[6,40,57,61,111,144,182,241,280,293],"both":[7,262],"academia":[8],"and":[9,14,17,32,63,76,89,118,129,211,284],"industry":[10,44,48],"dealing":[11],"with":[12,51,104,200,236,247,298],"processing":[13],"interpreting":[15],"large":[16,270],"complex":[18],"data":[19,101,134,271,283],"sets.":[20,272],"It":[21],"has":[22,228],"got":[23],"already":[24,142],"many":[25],"successful":[26,180],"applications":[27],"via":[28],"advancing":[29],"machine":[30],"(ML)":[31],"deep":[33],"learning":[34],"(DL)":[35],"techniques,":[36],"starting":[37],"to":[38,136,156,171,193],"evolve":[39],"the":[41,119,145,159,162,173,201,225,231,242,248,295],"upstream":[42],"petroleum":[43],"as":[45,73],"well.":[46,82],"The":[47,83,114,147,164,186,198,273],"operates":[49],"now":[50],"huge":[52],"amount":[53],"of":[54,70,107,139,161,221,224,239],"sensors":[55,67],"installed":[56],"different":[58,222],"facilities,":[59],"particularly":[60],"production":[62],"injection":[64],"wells.":[65,113,299],"These":[66],"provide":[68,275],"millions":[69],"measurements,":[71],"such":[72,112,266],"pressure,":[74],"temperature,":[75],"rate":[77,218,249],"every":[78,81],"year":[79],"for":[80,93,208,217,261,269,277,290],"measurements":[84],"may":[85],"be":[86],"highly":[87],"correlated":[88],"carry":[90],"crucial":[91,288],"information":[92],"decision":[94,291],"making.":[95],"This":[96],"paper":[97],"concentrates":[98],"on":[99,131,178],"pressure-rate":[100],"sets":[102],"accumulated":[103],"massive":[105],"installation":[106],"permanent":[108],"downhole":[109],"gauges":[110],"non-linear":[115],"autoregessive":[116],"(NARX)":[117],"long":[120],"short":[121],"term":[122],"memory":[123],"(LSTM)":[124],"neural":[125],"networks":[126],"were":[127],"assembled":[128],"tested":[130],"a":[132],"synthetic":[133],"set":[135],"compare":[137],"results":[138,207,274],"pressure":[140,209,232],"prediction,":[141,210],"addressed":[143],"literature.":[146],"LSTM":[148,174,199,226],"provided":[149,204],"better":[150],"predictions,":[151],"but":[152],"did":[153],"not":[154],"manage":[155],"capture":[157],"entirely":[158],"pattern":[160],"data.":[163],"shifting":[165,202],"window":[166,203],"method":[167,187],"was":[168,213],"then":[169,214],"applied":[170,216],"improve":[172],"prediction":[175,194,233,263],"capabilities,":[176],"based":[177],"previous":[179],"application":[181],"forecasting":[183],"electricity":[184],"demand.":[185],"implies":[188],"smooth":[189],"transition":[190],"from":[191],"training":[192],"improving":[195],"network":[196,227],"performance.":[197],"more":[205],"accurate":[206],"it":[212],"successfully":[215],"prediction.":[219],"Testing":[220],"configurations":[223],"shown":[229],"that":[230],"performs":[234],"well":[235,281],"less":[237],"number":[238],"nodes":[240],"hidden":[243],"layers":[244],"if":[245],"compared":[246],"predictions.":[250],"Significant":[251],"error":[252],"decrease":[253],"achieved":[255],"relatively":[256],"fast":[257],"(after":[258],"20":[259],"iterations)":[260],"tasks,":[264],"making":[265,292],"predictions":[267],"feasible":[268],"basis":[276],"filling":[278],"gaps":[279],"monitoring":[282],"short-term":[285],"performance":[286],"forecast,":[287],"tasks":[289],"all":[294],"industries":[296],"operating":[297]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
