{"id":"W2744600285","doi":"10.1002/cjce.22965","title":"Reservoir history matching using constrained ensemble Kalman filtering","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Correlogram; Ensemble Kalman filter; Covariance matrix; Kalman filter; Computer science; Permeability (electromagnetism); Covariance; Spatial correlation; Algorithm; Mathematical optimization; Mathematics; Extended Kalman filter; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006202461,0.0004805328,0.0008054237,0.0005643309,0.0003526103,0.0007050033,0.0009561003,0.000632374,0.001249488],"category_scores_gemma":[0.003412845,0.0004216281,0.0006033921,0.0007681743,0.0003488135,0.001374086,0.001085433,0.000655394,0.0002484496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005344656,"about_ca_system_score_gemma":0.001287501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0182658,"about_ca_topic_score_gemma":0.01579454,"domain_scores_codex":[0.9996141,0.0000801023,0.0000266309,0.0001022234,0.0001334311,0.00004343692],"domain_scores_gemma":[0.999027,0.0004227726,0.0001620052,0.000147154,0.000204774,0.00003639831],"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":[0.00005561967,0.00003042119,0.001329735,0.00003065058,0.00005910714,0.00005256522,0.00004782084,0.9051328,0.003372215,0.005637594,0.0004743062,0.08377716],"study_design_scores_gemma":[0.000002342138,0.000005341172,0.0001615814,0.000001770459,0.000002810182,0.000005060167,0.000002045818,0.9982076,0.0004923152,0.0009507772,0.000164458,0.000003984567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01564841,0.00004822998,0.9834593,0.00003182088,0.00001241108,0.00001458629,0.00003613868,0.0002037345,0.0005453528],"genre_scores_gemma":[0.7123033,0.000171644,0.2841468,0.00006442627,0.00004466087,0.0001155631,0.0003711381,0.00009421107,0.002688185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0182658,"threshold_uncertainty_score":0.03631902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03836494289945474,"score_gpt":0.254705827245745,"score_spread":0.2163408843462902,"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."}}