{"id":"W1974932257","doi":"10.2118/62941-ms","title":"Conditioning reservoir models to dynamic data - A forward modeling perspective","year":2000,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Permeability (electromagnetism); Computer science; Monte Carlo method; Reservoir simulation; Markov chain Monte Carlo; Geology; Mathematics; Petroleum engineering; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.002527238,0.0006154429,0.0008500257,0.0005249025,0.0003070546,0.0009866285,0.0008183749,0.0009158542,0.003342214],"category_scores_gemma":[0.008523416,0.0005874126,0.0005303821,0.0004328674,0.00102575,0.001518541,0.0009382077,0.001384571,0.0002232775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009372959,"about_ca_system_score_gemma":0.001072619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01651695,"about_ca_topic_score_gemma":0.006799534,"domain_scores_codex":[0.9995197,0.0002454096,0.00001784723,0.00007109995,0.00008556603,0.00006039028],"domain_scores_gemma":[0.9950655,0.004128614,0.0002382712,0.0001607041,0.0003565142,0.00005043746],"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":[0.00001141265,0.000006293828,0.0002703165,0.0000056077,0.000006782289,0.000009648958,0.000006512389,0.9963937,0.00009793222,0.001656097,0.00003828124,0.001497352],"study_design_scores_gemma":[0.000001483425,0.000003678718,0.00002610402,0.000001048595,0.000001229072,8.142511e-7,0.000001118123,0.9988934,0.00008570664,0.0009606936,0.00002343333,0.000001202603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.148876,0.0001905326,0.8449168,0.0009183277,0.00006590353,0.00008343629,0.000239143,0.0006724035,0.004037549],"genre_scores_gemma":[0.9696804,0.0001908062,0.02743923,0.00009837694,0.00004490956,0.0001479802,0.0001549007,0.00004426025,0.002199057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01651695,"threshold_uncertainty_score":0.03284162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04499303278695668,"score_gpt":0.2932961188563089,"score_spread":0.2483030860693522,"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."}}