{"id":"W2077368152","doi":"10.2118/165511-pa","title":"A New and Practical Workflow for Large Multipad SAGD Simulation—An Oil-Sands Case Study","year":2014,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Statoil; Imperial Oil Limited","keywords":"Workflow; Reservoir simulation; Process (computing); Steam-assisted gravity drainage; Grid; Simulation modeling; Computer simulation; Computer science; Petroleum engineering; Engineering; Oil sands; Simulation; Geology; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007186607,0.0001495749,0.0003212561,0.001673006,0.00009218929,0.00005254459,0.0001088306,0.0002173368,0.00001968806],"category_scores_gemma":[0.0006911324,0.0001467252,0.00005241431,0.0003868183,0.00001444206,0.000202646,0.00001152458,0.0004035799,0.000001104925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009704736,"about_ca_system_score_gemma":0.0001223058,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007818358,"about_ca_topic_score_gemma":0.02467538,"domain_scores_codex":[0.9989628,0.00005050882,0.0003961116,0.0001250523,0.0001196828,0.0003458376],"domain_scores_gemma":[0.9987859,0.000315701,0.00008136486,0.0002260641,0.0001194808,0.0004715594],"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":[0.0000210782,0.00002812843,0.00278998,0.00002063725,0.0000811303,0.0005526355,0.0001997166,0.9636731,0.00005939662,0.0005178789,0.000455943,0.03160045],"study_design_scores_gemma":[0.002101475,0.0004413966,0.0002693416,0.00001499639,0.00004736136,0.001611999,0.0004240032,0.8897158,0.0000222259,0.0003104794,0.1048874,0.0001535517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6176759,0.0001431791,0.3810062,0.0006638394,0.0002484183,0.0000608947,0.000005038432,0.0000852302,0.0001112828],"genre_scores_gemma":[0.8957745,0.00001199745,0.1038363,0.00002635158,0.0002106568,0.000005059302,9.400088e-7,0.00003398289,0.0001001989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2780986,"threshold_uncertainty_score":0.9931217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02099530970848127,"score_gpt":0.3101402717311136,"score_spread":0.2891449620226323,"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."}}