{"id":"W4307664825","doi":"10.2118/211053-ms","title":"Intelligent Production Optimization Decisions: Prioritizing Production Optimization Through Machine Learning Aided Uplift Quantification","year":2022,"lang":"en","type":"article","venue":"","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Imperial Oil (Canada)","funders":"","keywords":"Workflow; Production (economics); Computer science; Asset (computer security); Workover; Field (mathematics); Production manager; Risk analysis (engineering); Engineering; Petroleum engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007723452,0.0002603251,0.0002145691,0.0003355427,0.0008117815,0.00009295961,0.0001767388,0.00007914524,0.000391599],"category_scores_gemma":[0.00051749,0.0002871441,0.00007593854,0.001075186,0.00003655594,0.0008978761,0.00009161387,0.0005065767,0.00001808032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003654461,"about_ca_system_score_gemma":0.00002676587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004440923,"about_ca_topic_score_gemma":0.00000664233,"domain_scores_codex":[0.9978391,0.0001610256,0.0006178582,0.0006375125,0.0004638284,0.0002806637],"domain_scores_gemma":[0.999086,0.00003601299,0.0001559118,0.0004435305,0.0002287133,0.00004985532],"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":[0.00003647182,0.00008103156,0.00008846061,0.00003214586,0.00002323633,6.467355e-7,0.000527404,0.9673845,0.003780177,0.000183883,0.00166742,0.02619469],"study_design_scores_gemma":[0.0001410344,0.0001183945,0.00003637028,0.0000395319,0.00003887672,0.00007769508,0.0008357186,0.8578383,0.1259531,0.000511953,0.01396318,0.0004457606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009151318,0.0009258607,0.9795522,0.001500297,0.003217903,0.001003592,0.000005153874,0.003637217,0.001006437],"genre_scores_gemma":[0.7335078,0.004118469,0.2580245,0.00005779411,0.0006153875,0.0005572523,0.0006821177,0.0001426434,0.00229398],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7243565,"threshold_uncertainty_score":0.9999581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013517959431121,"score_gpt":0.2562182377272049,"score_spread":0.2260830581328937,"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."}}