{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003179056,0.0006936633,0.0002166704,0.0006447469,0.0003686745,0.0005998951,0.0005081747,0.0002785407,0.0003065372],"category_scores_gemma":[0.001017792,0.0001868062,0.0002724675,0.0006990246,0.0002292578,0.0003766528,0.0003232161,0.0002439898,0.00008542687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003721942,"about_ca_system_score_gemma":0.003426357,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7084222,"about_ca_topic_score_gemma":0.7990732,"domain_scores_codex":[0.9998148,0.00001797046,0.00001071552,0.00003979996,0.00008374711,0.00003293041],"domain_scores_gemma":[0.9997002,0.00007195406,0.000048937,0.00001386244,0.0001431825,0.00002195266],"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":[0.0001417277,0.0001135123,0.1987142,0.00007326667,0.00007870253,0.0002077082,0.0002138973,0.7074376,0.01062439,0.0003409627,0.0005482307,0.08150581],"study_design_scores_gemma":[0.000004165117,0.00002623155,0.05711043,0.000006918528,0.00001225611,0.00001313567,0.0001406115,0.9375535,0.004668411,0.0001155708,0.0003295093,0.00001938947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850408,0.0000590294,0.01308836,0.0000506055,0.000003606419,0.00002204109,0.0003534365,0.0001292757,0.001252811],"genre_scores_gemma":[0.9946969,0.00005052062,0.004468145,0.000005029412,7.057997e-7,0.00000690112,0.0002781182,0.000008335652,0.000485366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2915778,"threshold_uncertainty_score":0.5865899,"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."}}