{"id":"W2580129580","doi":"10.1007/978-981-10-3650-7_70","title":"Developing Optimum Production Strategy of Kailashtilla Gas Field and Economic Analysis","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Separator (oil production); Natural gas field; Petroleum engineering; Production (economics); Drilling; Production rate; Environmental science; Engineering; Natural gas; Process engineering; Waste management; Economics; Mechanical engineering; Physics","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":[0.0001956519,0.000451586,0.0003788327,0.0004221341,0.0003262037,0.0008480763,0.0005507465,0.0005160469,0.003195301],"category_scores_gemma":[0.0002809324,0.0003178504,0.0006138833,0.0005471421,0.0002545932,0.0009129707,0.0003875439,0.000504173,0.0003937591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009266135,"about_ca_system_score_gemma":0.001109555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005878298,"about_ca_topic_score_gemma":0.007126872,"domain_scores_codex":[0.9999416,0.00001267564,0.00000281689,0.00001310657,0.00001897064,0.00001076654],"domain_scores_gemma":[0.9999657,0.0000163806,0.000003756096,0.000001863233,0.000009806463,0.000002535266],"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":[0.00004150018,0.00002888726,0.0006795445,0.0003063215,0.0000289687,0.0001976343,0.0001094565,0.7779728,0.008364013,0.1082187,0.00373791,0.1003144],"study_design_scores_gemma":[0.000005506153,0.00002796146,0.0003825892,0.00003179294,0.00001789167,0.00007506376,0.00006489496,0.9538752,0.002556033,0.03588645,0.007063912,0.00001267223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04596002,0.002765424,0.8440261,0.0006477867,0.0001075085,0.0001021586,0.000156521,0.0002498742,0.1059846],"genre_scores_gemma":[0.7620381,0.004198056,0.1924739,0.00008801335,0.00005689071,0.0001774998,0.0002359543,0.0001522416,0.04057943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005878298,"threshold_uncertainty_score":0.01168817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0351098898852764,"score_gpt":0.2811562283345905,"score_spread":0.2460463384493141,"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."}}