{"id":"W4283321742","doi":"10.1002/cjce.24512","title":"A new analytical model to predict heavy oil production rate in the <scp>SAGD</scp> process","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Minification; Robustness (evolution); Petroleum engineering; Range (aeronautics); Steam-assisted gravity drainage; Novelty; Oil production; Genetic algorithm; Process (computing); Drainage; Applied mathematics; Mathematical optimization; Computer science; Mathematics; Engineering; Oil sands; Chemistry; Materials science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004016188,0.000567532,0.0007744986,0.0007680795,0.0004486923,0.0009223612,0.001107262,0.001042726,0.001254406],"category_scores_gemma":[0.0008976271,0.0003941097,0.0006583541,0.0005847243,0.0005093087,0.0005329215,0.0004776712,0.0006448569,0.0002859026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157102,"about_ca_system_score_gemma":0.001649082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02773233,"about_ca_topic_score_gemma":0.01308146,"domain_scores_codex":[0.999837,0.00002899196,0.000008824687,0.00003470674,0.00006056734,0.00002984012],"domain_scores_gemma":[0.9997738,0.0000945731,0.00003139028,0.00001062122,0.00008005255,0.000009580081],"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":[0.00001178768,0.00001681481,0.000322749,0.00001767918,0.000007470094,0.00004082026,0.00001431802,0.9937727,0.00114852,0.0008082789,0.0001223161,0.003716659],"study_design_scores_gemma":[0.000001263575,0.000004229286,0.00003981761,9.740901e-7,0.000001685817,0.000002632837,0.000001527682,0.9996345,0.0001443924,0.00008354065,0.00008402494,0.000001252172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.201599,0.0006407833,0.7774245,0.0006289523,0.0001425779,0.0001770838,0.0005062051,0.001172847,0.0177081],"genre_scores_gemma":[0.9754274,0.0003002116,0.01865485,0.00004372673,0.00002412786,0.0001514476,0.0001465651,0.00004976202,0.005201954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02773233,"threshold_uncertainty_score":0.05514187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0183635308679695,"score_gpt":0.2431566847127968,"score_spread":0.2247931538448273,"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."}}