{"id":"W2596937899","doi":"10.3997/2214-4609.201600782","title":"Accelerating Large-scale Reservoir Simulations Using Supercomputers","year":2016,"lang":"en","type":"article","venue":"78th EAGE Conference and Exhibition 2016","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Supercomputer; Computer science; Workstation; Computational science; Solver; Parallel computing; Reservoir simulation; Discretization; Scale (ratio); Nonlinear system; Reservoir computing; Petroleum engineering; Geology; Operating system; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002040571,0.0001671043,0.0001764429,0.0001237564,0.0001493672,0.00009725816,0.00009027204,0.0001065416,0.0002564891],"category_scores_gemma":[0.00004960311,0.0001280592,0.00004302343,0.0001227309,0.00003369223,0.0005442783,0.00004098219,0.000100219,0.00002465075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003949167,"about_ca_system_score_gemma":0.00001924039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006510303,"about_ca_topic_score_gemma":0.00001230995,"domain_scores_codex":[0.9990425,0.0000518078,0.0002595025,0.0002148397,0.0001332851,0.0002980625],"domain_scores_gemma":[0.9993646,0.0001774873,0.00002497998,0.0002199707,0.00008969379,0.0001232629],"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":[0.00001500285,0.00003271956,0.00402654,0.0001325487,0.00004689955,0.000007801844,0.001071948,0.7871673,0.1926453,0.00161815,0.00105289,0.01218298],"study_design_scores_gemma":[0.0007908433,0.00001959123,0.001476758,0.0002278734,0.000008621498,0.000004622232,0.00008557837,0.9895816,0.004309992,0.0006073316,0.002618532,0.0002686129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5483529,0.0000599999,0.4505742,0.0000825369,0.0001389263,0.00006806762,0.00003054127,0.0001865088,0.0005063041],"genre_scores_gemma":[0.9766368,0.00009952055,0.02278542,0.00003191763,0.0001615128,0.000005836397,0.00001397483,0.00002789211,0.000237069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.428284,"threshold_uncertainty_score":0.5222103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05544569194615866,"score_gpt":0.2892201157212071,"score_spread":0.2337744237750485,"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."}}