{"id":"W2408410785","doi":"10.2118/180716-ms","title":"Integration of Artificial Intelligence and Production Data Analysis for Shale Heterogeneity Characterization in SAGD Reservoirs","year":2016,"lang":"en","type":"article","venue":"SPE Canada Heavy Oil Technical Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Oil shale; Petroleum engineering; Workflow; Reservoir modeling; Geology; Flow (mathematics); Shale oil; Artificial neural network; Computer science; Artificial intelligence; Mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001836825,0.0008127232,0.0005813611,0.002467509,0.000338517,0.001366559,0.0007136814,0.0006522344,0.0006134029],"category_scores_gemma":[0.00387482,0.000394452,0.0009378171,0.001297935,0.0003919054,0.001042317,0.000974801,0.0005864459,0.0001294257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009985056,"about_ca_system_score_gemma":0.001026949,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034691,"about_ca_topic_score_gemma":0.008120955,"domain_scores_codex":[0.9993235,0.0002338225,0.00008203575,0.0001343936,0.0001781233,0.00004811217],"domain_scores_gemma":[0.9980583,0.0009690042,0.0002669763,0.0002409343,0.0003875309,0.0000772877],"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":[0.0001076082,0.0002450472,0.02459036,0.00007380152,0.0001608082,0.0001757879,0.0001049077,0.8994739,0.004101827,0.00125825,0.0003138361,0.0693938],"study_design_scores_gemma":[0.00000167083,0.00001446885,0.001873805,0.000003796029,0.000008264181,0.00000715571,0.0000175744,0.9964293,0.0008658105,0.0006437916,0.0001286116,0.000005777455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.400918,0.0002780352,0.5923089,0.0005468245,0.00003813926,0.0002090572,0.0005370233,0.002046982,0.003116975],"genre_scores_gemma":[0.9225804,0.00009793608,0.0765176,0.0000372328,0.00001271659,0.00007803477,0.0003101763,0.00002908276,0.0003368749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9896531,"threshold_uncertainty_score":0.02057338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1043011050012296,"score_gpt":0.3154678539440515,"score_spread":0.2111667489428219,"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."}}