{"id":"https://openalex.org/W4409606089","doi":"https://doi.org/10.2118/224549-ms","title":"Leveraging Machine Learning Capabilities in Seismic Reservoir Characterization","display_name":"Leveraging Machine Learning Capabilities in Seismic Reservoir Characterization","publication_year":2025,"publication_date":"2025-04-21","ids":{"openalex":"https://openalex.org/W4409606089","doi":"https://doi.org/10.2118/224549-ms"},"language":"en","primary_location":{"id":"doi:10.2118/224549-ms","doi":"https://doi.org/10.2118/224549-ms","is_oa":false,"landing_page_url":"https://doi.org/10.2118/224549-ms","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GOTECH","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5001762934","display_name":"Omar Aly","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"O. Aly","raw_affiliation_strings":["SLB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5001762934"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":17.4084,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96379087,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":96},"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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9326000213623047,"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":0.9326000213623047,"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/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.04969999939203262,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T13067","display_name":"Geological Modeling and Analysis","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.871999979019165},{"id":"https://openalex.org/keywords/seismic-reservoir-characterization","display_name":"seismic reservoir characterization","score":0.9769999980926514},{"id":"https://openalex.org/keywords/facies-classification","display_name":"facies classification","score":0.8840000033378601},{"id":"https://openalex.org/keywords/porosity-prediction","display_name":"porosity estimation","score":0.7490000128746033},{"id":"https://openalex.org/keywords/lithology-prediction","display_name":"lithology prediction","score":0.9160000085830688},{"id":"https://openalex.org/keywords/multiple-linear-regression","display_name":"multiple linear regression","score":0.6959999799728394},{"id":"https://openalex.org/keywords/bayesian-inversion","display_name":"Bayesian inversion","score":0.9129999876022339},{"id":"https://openalex.org/keywords/reservoir-parameter-estimation","display_name":"reservoir parameter estimation","score":0.6909999847412109}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6429926753044128},{"id":"https://openalex.org/C14641988","wikidata":"https://www.wikidata.org/wiki/Q7315329","display_name":"Reservoir modeling","level":2,"score":0.6388068795204163},{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.5662731528282166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3219796419143677},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3060571551322937},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.16521227359771729},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.058226585388183594},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2118/224549-ms","doi":"https://doi.org/10.2118/224549-ms","is_oa":false,"landing_page_url":"https://doi.org/10.2118/224549-ms","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GOTECH","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W4378231090","https://openalex.org/W4387230270"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3107994849","https://openalex.org/W4247143848","https://openalex.org/W2324615561","https://openalex.org/W2009883749","https://openalex.org/W2735573198","https://openalex.org/W2086120259","https://openalex.org/W3022094251"],"abstract_inverted_index":{"Abstract":[0],"Objectives":[1],"&":[2,66],"Scope":[3],"The":[4,38],"primary":[5],"objective":[6],"of":[7,17,50,85,104],"this":[8,91],"study":[9,113],"is":[10],"to":[11,70,81,94],"explore":[12],"the":[13,48,83,102,139],"application":[14],"and":[15,24,35,47,58,100,110,124,131,136,143],"advantages":[16],"machine":[18,121,140],"learning":[19,122,141],"(ML)":[20],"in":[21],"seismic":[22,62,71,132,148],"petrophysics":[23],"reservoir":[25,52,72,108,133],"characterization,":[26],"with":[27],"a":[28,117],"focus":[29],"on":[30],"quantitative":[31],"interpretation,":[32],"facies":[33,45,149],"prediction,":[34],"petro-elastic":[36,128],"modeling.":[37],"scope":[39],"includes":[40],"evaluating":[41],"ML":[42],"techniques":[43],"for":[44,106,127,147],"classification":[46,150],"prediction":[49,96,135],"essential":[51],"properties,":[53],"such":[54],"as":[55],"porosity,":[56],"lithology,":[57],"fluid":[59],"saturation,":[60],"from":[61],"inversion":[63,145],"attributes.":[64],"Challenges":[65],"Procedures":[67],"Traditional":[68],"approaches":[69],"characterization":[73],"involve":[74],"complex,":[75],"time-intensive":[76],"workflows":[77],"that":[78],"often":[79],"struggle":[80],"capture":[82],"heterogeneity":[84],"subsurface":[86],"formations.":[87],"By":[88],"leveraging":[89],"ML,":[90],"work":[92],"aims":[93],"improve":[95],"accuracy,":[97],"streamline":[98],"workflows,":[99],"enhance":[101],"reliability":[103],"interpretations":[105],"efficient":[107],"management":[109],"development.":[111],"This":[112],"will":[114,137],"also":[115],"show":[116],"comparative":[118],"analysis":[119],"between":[120],"algorithms":[123,142],"multilinear":[125],"regressions":[126],"modeling":[129],"(PEM)":[130],"properties":[134],"compare":[138],"Bayesian":[144],"technique":[146],"model.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}