{"id":"W1970212884","doi":"10.1021/ef9013218","title":"Performance Prediction of Waterflooding in Western Canadian Heavy Oil Reservoirs Using Artificial Neural Network","year":2010,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Research Council (Canada); University of Regina","funders":"","keywords":"Artificial neural network; Backpropagation; Petroleum engineering; Multivariate statistics; Computer science; Reservoir simulation; Predictive modelling; Partial least squares regression; Minification; Machine learning; Environmental science; Process engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001997338,0.0001403186,0.0002086089,0.0002390765,0.00009748679,0.00003226117,0.0001640006,0.0001452554,0.00004451258],"category_scores_gemma":[0.00001038961,0.0001353773,0.00006166854,0.0003233001,0.00003380882,0.0001930748,0.00002317222,0.0002980688,0.000003120634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006979747,"about_ca_system_score_gemma":0.00004012577,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05891332,"about_ca_topic_score_gemma":0.2622276,"domain_scores_codex":[0.9989054,0.00002641009,0.0003205673,0.0001593822,0.0001480389,0.0004402504],"domain_scores_gemma":[0.999525,0.00001896352,0.00003284555,0.0002491781,0.00002619671,0.0001477848],"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":[0.00000658415,0.000006568907,0.01895681,0.00003612992,0.00002342021,0.000008237808,0.0001438618,0.959956,0.01761493,0.000007972649,0.00002853096,0.003210877],"study_design_scores_gemma":[0.0001663381,0.00002525554,0.0133243,0.0001047083,0.00002928598,0.0000111383,0.00003145437,0.9005119,0.08104984,0.00004006483,0.004482265,0.0002235022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973362,0.00008858249,0.0001373186,0.00006625301,0.000841776,0.00001444879,0.000005952121,0.00006513988,0.001444295],"genre_scores_gemma":[0.9989814,0.00005546058,0.0001573578,0.00002173794,0.000583878,0.000005366259,0.00001703179,0.0000297379,0.000148059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2033143,"threshold_uncertainty_score":0.9473535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01301883761948123,"score_gpt":0.2041712475468277,"score_spread":0.1911524099273465,"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."}}