{"id":"W3099593397","doi":"10.2118/201760-ms","title":"Network Optimization Models at Greater Kuparuk Area Using Neural Networks and Genetic Algorithms","year":2020,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Gas lift; Lift (data mining); Header; Artificial neural network; Computer science; Genetic algorithm; Mathematical optimization; Environmental science; Simulation; Petroleum engineering; Algorithm; Engineering; Mathematics; Machine learning","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":[0.0007623186,0.0009537257,0.0008500406,0.001139087,0.0008088602,0.001308308,0.001057329,0.001763356,0.002099088],"category_scores_gemma":[0.002457811,0.0006316728,0.0007359858,0.0008121197,0.0006814081,0.000883173,0.0006966894,0.0009363666,0.0001889893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003151388,"about_ca_system_score_gemma":0.001633889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1488877,"about_ca_topic_score_gemma":0.07149076,"domain_scores_codex":[0.9997259,0.000110815,0.00001500379,0.00006335932,0.00003115651,0.000053774],"domain_scores_gemma":[0.998863,0.0007926445,0.00009871682,0.00001882605,0.0001915816,0.00003524045],"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":[0.000004825423,0.000004891746,0.0002267669,0.000002242387,0.000002832754,0.000008055471,0.000003053738,0.9992197,0.00002002938,0.0001074434,0.00002221427,0.0003779324],"study_design_scores_gemma":[0.000001227502,0.000002202999,0.00005701264,7.474577e-7,8.711407e-7,4.097012e-7,0.000002908267,0.9998287,0.0000164719,0.00007467353,0.00001391949,9.494882e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8422047,0.0004160237,0.1435797,0.0006897622,0.0000513763,0.0001822805,0.0006185825,0.0004224405,0.011835],"genre_scores_gemma":[0.9841871,0.0001153438,0.01183707,0.0000337819,0.000009370703,0.0001553179,0.0002655734,0.00002219542,0.003374198],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1488877,"threshold_uncertainty_score":0.2960421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05661487058460563,"score_gpt":0.262451582205862,"score_spread":0.2058367116212564,"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."}}