{"id":"W2177444036","doi":"10.2118/89363-ms","title":"Streamline Technology for the Evaluation of Full Field Compositional Processes; Midale, A Case Study","year":2004,"lang":"en","type":"article","venue":"SPE/DOE Symposium on Improved Oil Recovery","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Apache (Canada)","funders":"","keywords":"Field (mathematics); Computer science; Reservoir simulation; Grid; Simulation modeling; Flood myth; Flooding (psychology); Scale (ratio); Calibration; Simulation; Industrial engineering; Petroleum engineering; Geology; Engineering","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":[0.0006506175,0.000300153,0.0002904753,0.0004736462,0.0004286751,0.0005097239,0.0005749505,0.000375394,0.001710235],"category_scores_gemma":[0.001239931,0.0001673539,0.0002459264,0.0004684269,0.0002525777,0.0006667842,0.0003424993,0.000360053,0.0001278698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009717445,"about_ca_system_score_gemma":0.0005684053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01319052,"about_ca_topic_score_gemma":0.01795763,"domain_scores_codex":[0.9997912,0.00007336088,0.00001104373,0.0000313834,0.00006856179,0.00002448878],"domain_scores_gemma":[0.9993423,0.0002781918,0.00005071727,0.00007530268,0.0001984173,0.00005496971],"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":[0.0006505401,0.0008681103,0.03018191,0.0001198684,0.00004030393,0.001084272,0.0002656133,0.8715011,0.03752977,0.004606966,0.001668589,0.05148289],"study_design_scores_gemma":[0.00006097968,0.0005799407,0.008177032,0.00000917123,0.00001069673,0.00007890347,0.0001382588,0.9644585,0.02351817,0.0007365346,0.002211994,0.00001984592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754702,0.00005431172,0.01869687,0.00009946858,0.00001228667,0.0001470855,0.000554193,0.0003821634,0.004583287],"genre_scores_gemma":[0.9897612,0.00002936546,0.009141597,0.000008353335,0.000002307751,0.0000372778,0.0002496083,0.00001280266,0.0007574835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01319052,"threshold_uncertainty_score":0.02622747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352575079022868,"score_gpt":0.3059740604475495,"score_spread":0.2824483096573209,"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."}}