{"id":"W2051094230","doi":"10.2118/165491-ms","title":"Differential Evolution for Assisted History Matching Process: SAGD Case Study","year":2013,"lang":"en","type":"article","venue":"SPE Heavy Oil Conference-Canada","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Differential evolution; Particle swarm optimization; Computer science; Mathematical optimization; Matching (statistics); Convergence (economics); Reservoir simulation; Population; Mathematics; Algorithm; Engineering; Petroleum engineering; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001039929,0.0002544605,0.0003020751,0.00007558653,0.0001093549,0.00005822762,0.0001685577,0.00007519376,0.0004924859],"category_scores_gemma":[0.00006501206,0.0002589409,0.00004396099,0.0001004306,0.00001492983,0.0001613962,0.00001841415,0.0002292403,0.000004043031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244159,"about_ca_system_score_gemma":0.001056469,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.901037,"about_ca_topic_score_gemma":0.9619477,"domain_scores_codex":[0.9986116,0.00004475335,0.000369218,0.0002547465,0.0003410468,0.0003786286],"domain_scores_gemma":[0.9991275,0.0001452536,0.00004911233,0.0002797249,0.0002153067,0.0001830741],"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":[0.00006873065,0.0002304735,0.003432907,0.001947653,0.0004494734,0.0005743634,0.00324979,0.8902223,0.001344541,0.0004380135,0.04031695,0.05772486],"study_design_scores_gemma":[0.001861832,0.00009178849,0.008455122,0.00008653334,0.00006943035,0.0001419303,0.004579382,0.9770824,0.0001788925,0.0003238787,0.006307152,0.0008216934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.947807,0.00012532,0.04808774,0.00007858845,0.001501553,0.0003482624,0.00001779623,0.0002076857,0.001826092],"genre_scores_gemma":[0.9964226,0.000003619727,0.001799831,0.00002107846,0.0001474532,0.0001943172,0.00001736004,0.00004705169,0.001346645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08686012,"threshold_uncertainty_score":0.9999863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03156282635957575,"score_gpt":0.2522047600027664,"score_spread":0.2206419336431907,"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."}}