{"id":"W1975651324","doi":"10.2118/07-02-02","title":"Direct Prediction of Reservoir Performance With Bayesian Updating","year":2007,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Geostatistics; Variable (mathematics); Computer science; Data mining; Prior probability; Multivariate statistics; Bayesian probability; Reservoir modeling; Posterior probability; Spatial analysis; Statistics; Machine learning; Spatial variability; Petroleum engineering; Artificial intelligence; Geology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000741423,0.0001068838,0.000254053,0.002892127,0.00003927305,0.000008314274,0.0002117017,0.0001633876,0.00001596769],"category_scores_gemma":[0.00009445679,0.00009571949,0.00004066572,0.0009770223,0.0000444894,0.00014454,0.000006702826,0.000376711,8.009809e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720989,"about_ca_system_score_gemma":0.0001248274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000375917,"about_ca_topic_score_gemma":0.004011847,"domain_scores_codex":[0.998982,0.00001221341,0.0004617873,0.000067605,0.0001801381,0.0002962261],"domain_scores_gemma":[0.9993191,0.00005028272,0.0001270763,0.000186137,0.0001482753,0.0001691435],"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":[0.00002521111,0.000004843056,0.03219929,0.00006044366,0.00006975544,0.00004420224,0.00004560547,0.959521,0.003440605,0.0002369382,0.0002905604,0.004061537],"study_design_scores_gemma":[0.00299374,0.001850692,0.08454711,0.0007463821,0.0001195326,0.001193954,0.0009161472,0.6125966,0.123046,0.0002069758,0.1711581,0.0006247621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9477277,0.0002740106,0.04008986,0.000183922,0.0002183245,0.00003268231,0.000006556197,0.0000876773,0.01137925],"genre_scores_gemma":[0.9745449,0.00008147501,0.02520855,0.000005365855,0.00008067791,8.791725e-7,0.000001145849,0.00002509341,0.00005185167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3469244,"threshold_uncertainty_score":0.3903328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007743768167443751,"score_gpt":0.2125998319526165,"score_spread":0.2048560637851727,"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."}}