{"id":"W2091078619","doi":"10.2118/153275-ms","title":"A Method for Probabilistic Forecasting of Oil Rates in Naturally Fractured Reservoirs","year":2012,"lang":"en","type":"article","venue":"SPE Latin America and Caribbean Petroleum Engineering Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Petroleum engineering; Probabilistic logic; Fossil fuel; Reservoir engineering; Mathematical optimization; Computer science; Environmental science; Geology; Petroleum; Statistics; Mathematics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001284828,0.0004561213,0.0006840361,0.0009199932,0.0004221222,0.0006625673,0.001195119,0.0007599674,0.001626335],"category_scores_gemma":[0.004334033,0.0005084738,0.0006422795,0.0006326223,0.0004506982,0.0007763825,0.0005809219,0.0008651644,0.0003091134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008645094,"about_ca_system_score_gemma":0.001276682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009241274,"about_ca_topic_score_gemma":0.005469677,"domain_scores_codex":[0.999558,0.0001628473,0.00002463414,0.00007496387,0.0001438329,0.00003563714],"domain_scores_gemma":[0.9982327,0.001182627,0.0001348188,0.00007296827,0.0003180403,0.00005872953],"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":[0.00003563813,0.000008346949,0.0007198019,0.00002248964,0.00001911223,0.0000431807,0.00002405611,0.9611053,0.001260953,0.005483356,0.000310759,0.03096705],"study_design_scores_gemma":[0.000001540278,0.000003063796,0.00003570684,0.000001943,0.000001103501,0.000005569319,0.000001012506,0.9989632,0.0001316278,0.0007050868,0.0001476032,0.000002596943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005002739,0.0000514091,0.9942573,0.0000450431,0.00001333745,0.00002036641,0.00003862165,0.0001961465,0.0003749468],"genre_scores_gemma":[0.4168096,0.0002618211,0.5798868,0.00006349899,0.00006348373,0.0003037579,0.0002042079,0.0001403616,0.002266522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009241274,"threshold_uncertainty_score":0.01837498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427378491907161,"score_gpt":0.2742184320439272,"score_spread":0.2499446471248555,"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."}}