{"id":"W2791626696","doi":"10.2118/189770-ms","title":"Geologically Consistent History Matching of SAGD Process Using Probability Perturbation Method","year":2018,"lang":"en","type":"article","venue":"SPE Canada Heavy Oil Technical Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Facies; Geology; Channelized; Petroleum engineering; Reservoir modeling; Overburden; Permeability (electromagnetism); Geostatistics; Steam injection; Borehole; Outcrop; Reservoir simulation; Steam-assisted gravity drainage; Structural basin; Oil sands; Geotechnical engineering; Computer science; Geomorphology; Spatial variability; Mathematics","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":[0.0005439015,0.000179808,0.000326279,0.00005487104,0.00004725302,0.00001133173,0.0002447798,0.0001428411,0.0001863929],"category_scores_gemma":[0.0004360874,0.0001698156,0.00004925535,0.0001600787,0.0001370021,0.00007670301,0.00003655841,0.0002730322,0.000001519856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006984164,"about_ca_system_score_gemma":0.0006945,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01317258,"about_ca_topic_score_gemma":0.02581921,"domain_scores_codex":[0.9985991,0.00009798734,0.0004474478,0.0002583623,0.0003301883,0.000266985],"domain_scores_gemma":[0.9988881,0.0002205,0.00007164886,0.0003348633,0.0003517633,0.0001331757],"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":[0.00005092285,0.00003343135,0.0003901913,0.0007830302,0.00003458437,0.000007415469,0.0001974549,0.9523245,0.0320948,0.004795203,0.0004615815,0.00882687],"study_design_scores_gemma":[0.0003109079,0.00009103271,0.001590993,0.0001789963,0.00003196486,0.00002082464,0.00005026743,0.9738675,0.009034891,0.008319902,0.006047575,0.0004550993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4508635,0.0002495773,0.5390968,0.0001441408,0.0004029299,0.0001443743,0.00001368186,0.000329255,0.00875583],"genre_scores_gemma":[0.8053693,0.000008497433,0.1944269,0.00004071845,0.00004431192,0.00001030925,0.000003436324,0.00001639584,0.00008018519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3545058,"threshold_uncertainty_score":0.9933988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05667812219421945,"score_gpt":0.2928594872319157,"score_spread":0.2361813650376963,"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."}}