{"id":"https://openalex.org/W7225792146","doi":"https://doi.org/10.1038/s41598-026-80456-7","title":"Well-aware stacking ensemble machine learning framework for oil production prediction in gas-lift wells: leakage-aware validation in the Halewah Field","display_name":"Well-aware stacking ensemble machine learning framework for oil production prediction in gas-lift wells: leakage-aware validation in the Halewah Field","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225792146","doi":"https://doi.org/10.1038/s41598-026-80456-7"},"language":"en","primary_location":{"id":"doi:10.1038/s41598-026-80456-7","doi":"https://doi.org/10.1038/s41598-026-80456-7","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-80456-7","pdf_url":null,"source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1038/s41598-026-80456-7","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160701373","display_name":"Amr Yahya Hamid","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I36197038","display_name":"Taiz University","ror":"https://ror.org/03jwcxq96","country_code":"YE","type":"education","lineage":["https://openalex.org/I36197038"]}],"countries":["YE"],"is_corresponding":true,"raw_author_name":"Amr Yahya Hamid","raw_affiliation_strings":["Department of Geology, Taiz University, Taiz City, Taiz, Yemen"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geology, Taiz University, Taiz City, Taiz, Yemen","institution_ids":["https://openalex.org/I36197038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078947975","display_name":"Mysara Eissa Mohyaldinn","orcid":"https://orcid.org/0000-0001-5200-8491","observed_orcids":["https://orcid.org/0000-0001-5200-8491"]},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Mysara Eissa Mohyaldinn","raw_affiliation_strings":["Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093832213","display_name":"Mahdi Ali Lathbl","orcid":"https://orcid.org/0000-0002-4365-4501","observed_orcids":["https://orcid.org/0000-0002-4365-4501"]},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Mahdi Ali Lathbl","raw_affiliation_strings":["Department of Geoscience, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geoscience, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092537103","display_name":"Mohammed Basheer Alameen","orcid":"https://orcid.org/0009-0008-3143-8765","observed_orcids":["https://orcid.org/0009-0008-3143-8765"]},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"M. Basheer Alameen","raw_affiliation_strings":["Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003590905","display_name":"Shamsuddeen Adamu","orcid":"https://orcid.org/0009-0008-4015-1723","observed_orcids":["https://orcid.org/0009-0008-4015-1723"]},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Shamsuddeen Adamu","raw_affiliation_strings":["Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, 32610, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5160701373"],"corresponding_institution_ids":["https://openalex.org/I36197038"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13050","display_name":"Oil and Gas Production Techniques","score":0.8310999870300293,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13050","display_name":"Oil and Gas Production Techniques","score":0.8310999870300293,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.1581999957561493,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883000135421753},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.6256999969482422},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.6079999804496765},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C9395851","wikidata":"https://www.wikidata.org/wiki/Q177929","display_name":"Stack (abstract data type)","level":2,"score":0.5299999713897705},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.5296000242233276},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48649999499320984},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.44679999351501465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43939998745918274},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41290000081062317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4074000120162964},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.37209999561309814},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C2776364302","wikidata":"https://www.wikidata.org/wiki/Q1845437","display_name":"Oil field","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C38601921","wikidata":"https://www.wikidata.org/wiki/Q1757693","display_name":"Protocol stack","level":3,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1038/s41598-026-80456-7","doi":"https://doi.org/10.1038/s41598-026-80456-7","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-80456-7","pdf_url":null,"source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1038/s41598-026-80456-7","doi":"https://doi.org/10.1038/s41598-026-80456-7","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-80456-7","pdf_url":null,"source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Lift-gas":[1],"decisions":[2],"in":[3,35,93],"the":[4,70,100,111,136,140,149,165,173],"mature":[5],"Halewah":[6],"Field,":[7],"Yemen,":[8],"require":[9],"fast,":[10],"well-specific":[11,121,166],"oil-rate":[12],"predictions.":[13],"PIPESIM":[14,145],"models":[15,150],"of":[16,78,113],"wells":[17],"H-06":[18,161],"and":[19,40,86,134,158,162,164,181,190],"H-09,":[20,163],"each":[21,36],"calibrated":[22,174],"to":[23,102,118],"a":[24,54,89,120,192],"stabilized":[25,139],"field":[26],"operating":[27,33,175],"point,":[28],"were":[29,44,151,179],"evaluated":[30],"at":[31,172],"350":[32],"scenarios":[34,146],"well.":[37,52],"Six":[38],"learners":[39],"22":[41],"ensemble":[42],"configurations":[43],"compared,":[45],"including":[46],"WellAware_Stack,":[47],"one":[48],"six-learner":[49],"stack":[50],"per":[51],"On":[53,142],"105-record":[55],"holdout,":[56],"WellAware_Stack":[57,128,153],"reached":[58,154],"R\u00b2":[59,83,155],"=":[60,84,156],"0.996":[61],"(RMSE":[62,129],"42.0":[63],"STB/d),":[64,133],"against":[65,131],"0.949":[66],"(155.1":[67],"STB/d)":[68],"for":[69,160,170,195],"best":[71],"pooled":[72],"stack.":[73,141],"Repeated":[74],"scenario-grouped":[75],"nested":[76],"cross-validation":[77],"these":[79],"two":[80],"finalists":[81],"gave":[82],"0.994":[85],"0.857,":[87],"exposing":[88],"cross-well":[90],"information":[91,104],"path":[92],"row-wise":[94],"splits.":[95],"A":[96],"controlled":[97],"comparison":[98],"attributed":[99],"gain":[101],"well":[103],"rather":[105],"than":[106,127],"stacking:":[107],"supplying":[108],"it":[109],"reduced":[110],"error":[112],"every":[114],"model":[115],"family":[116],"five-":[117],"tenfold,":[119],"Gaussian":[122,167],"process":[123,168],"was":[124],"more":[125],"accurate":[126],"23.6":[130],"44.9":[132],"standardizing":[135],"Gaussian-process":[137],"target":[138],"60":[143],"fresh":[144],"simulated":[147],"after":[148],"frozen,":[152],"0.995":[157],"0.999":[159,169],"both;":[171],"points":[176],"their":[177],"errors":[178],"10\u201317%":[180],"below":[182],"4%,":[183],"respectively.":[184],"The":[185],"study":[186],"quantifies":[187],"surrogate-to-simulator":[188],"fidelity":[189],"provides":[191],"leakage-aware":[193],"protocol":[194],"paired":[196],"simulation":[197],"data.":[198]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
