{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006147909,0.0002067593,0.0002976418,0.0001857817,0.0001865393,0.000165359,0.000770179,0.0001200223,0.00005047347],"category_scores_gemma":[0.0004151254,0.0001839828,0.0001415625,0.00005532683,0.0001006612,0.0003063671,0.00003015561,0.0006365149,0.000002660846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007178216,"about_ca_system_score_gemma":0.0002280679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006827464,"about_ca_topic_score_gemma":0.00007972727,"domain_scores_codex":[0.9988393,0.00001739154,0.0004125964,0.00009596838,0.0002046858,0.0004300938],"domain_scores_gemma":[0.9987379,0.0001213306,0.0001211792,0.0004705534,0.00009007665,0.0004589479],"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":[0.000002599755,9.230357e-7,0.00003686706,0.00004940467,0.00004434252,0.00005099225,0.0002373047,0.8404521,0.158554,0.0002351,0.0001607491,0.000175685],"study_design_scores_gemma":[0.000503595,0.00001178764,0.0002041844,0.0002938578,0.00003983858,0.0002914897,0.00001769461,0.9415058,0.05055683,0.0003392138,0.005874106,0.000361617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577519,0.0008881016,0.0388545,0.0000975263,0.001309445,0.0000600519,0.000003267891,0.00006325177,0.0009719457],"genre_scores_gemma":[0.9833757,0.000004148899,0.01607355,0.00001452227,0.0004275445,0.000001334546,7.873548e-7,0.0000615594,0.0000408727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1079971,"threshold_uncertainty_score":0.7502601,"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."}}