{"id":"W2900819374","doi":"10.3997/2214-4609.201803025","title":"Implementation Of Meta Heuristic Algorithm And Pressure Match Method To Observe Aquifer Constant In Retrograde Gas Condensate Reservoirs","year":2018,"lang":"en","type":"article","venue":"Proceedings","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Aquifer; Constant (computer programming); Petroleum engineering; Genetic algorithm; Porosity; Porous medium; Relative permeability; Permeability (electromagnetism); Mathematical optimization; Computer science; Material balance; Algorithm; Mathematics; Applied mathematics; Geology; Geotechnical engineering; Process engineering; Engineering; Groundwater; Chemistry","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":[0.0008959913,0.0001827416,0.0003750483,0.0002118135,0.00003112833,0.00005010947,0.0001323066,0.00008584754,0.00006922545],"category_scores_gemma":[0.00009065602,0.0001750041,0.00004816804,0.0004594915,0.00003908526,0.0001734664,0.00005712866,0.0001514406,0.00000267502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002817651,"about_ca_system_score_gemma":0.000009986744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001104036,"about_ca_topic_score_gemma":0.000009945635,"domain_scores_codex":[0.9988261,0.00002583325,0.0004020232,0.0002434395,0.0002196697,0.0002829243],"domain_scores_gemma":[0.9994418,0.00009610927,0.00005212848,0.0001077187,0.0001892282,0.0001130062],"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":[0.0004226195,0.0001954241,0.1371571,0.008425462,0.005955874,0.00003941256,0.04351598,0.3769657,0.2921686,0.02233417,0.01074267,0.1020771],"study_design_scores_gemma":[0.001491291,0.0002738971,0.01760707,0.0001190792,0.000461477,0.00001871109,0.001443957,0.8815075,0.0853662,0.003659164,0.007499357,0.0005523416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8545974,0.0007968434,0.1424872,0.0002225951,0.0001439559,0.0005654484,0.00004172437,0.0002283687,0.0009164949],"genre_scores_gemma":[0.6630431,0.00003126185,0.3367136,0.0000165975,0.00005403514,0.00004459877,0.000002795215,0.00003275856,0.00006128388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5045418,"threshold_uncertainty_score":0.7136461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129824709464483,"score_gpt":0.331048949840166,"score_spread":0.2997507027455211,"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."}}