{"id":"W2596399451","doi":"10.1016/j.petrol.2017.03.031","title":"History matching by integrating regional multi-property image perturbation methods with a multivariate sensitivity analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Universidade Estadual de Campinas; Petrobras; Conselho Nacional de Desenvolvimento Científico e Tecnológico; CMG Reservoir Simulation Foundation","keywords":"Petrophysics; Categorical variable; Multivariate statistics; Reservoir modeling; Permeability (electromagnetism); Computer science; Reservoir simulation; Mathematical optimization; Homogeneity (statistics); Matching (statistics); Data mining; Econometrics; Algorithm; Geology; Mathematics; Porosity; Statistics; Petroleum engineering; Machine learning; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003237991,0.000194354,0.0003623203,0.0005014863,0.0002375998,0.000252636,0.0002678291,0.00005499705,0.00000444099],"category_scores_gemma":[0.0006431782,0.0001314469,0.00009139237,0.0002368645,0.000129961,0.001391657,0.00004320423,0.0003931347,3.411502e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003406231,"about_ca_system_score_gemma":0.00006453112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000624021,"about_ca_topic_score_gemma":0.000004841386,"domain_scores_codex":[0.9987332,0.00004784194,0.0003293413,0.0001860283,0.0004293423,0.0002742344],"domain_scores_gemma":[0.99891,0.0001607298,0.0001929146,0.0002711174,0.0002777639,0.0001875302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000742151,0.000007096149,0.0002237661,0.00002784767,0.00009088691,0.00001029202,0.0004060695,0.6841473,0.3134153,0.00002141145,0.00003027837,0.001612324],"study_design_scores_gemma":[0.0003811861,0.00003203395,0.01086929,0.00006865483,0.00009246843,0.00005095584,0.0001031971,0.9850491,0.00222391,0.000004952826,0.0009340845,0.0001902058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.278636,0.0002583725,0.7206501,0.00005530231,0.0002151413,0.00002415917,0.000001174978,0.00004528129,0.0001144267],"genre_scores_gemma":[0.5932722,0.00003360651,0.4065334,0.000007020627,0.00005703459,0.000001350002,5.015769e-7,0.0000164485,0.00007840545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3146362,"threshold_uncertainty_score":0.5360249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540829576362986,"score_gpt":0.2976617523554339,"score_spread":0.2722534565918041,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}