{"id":"W2011639575","doi":"10.2118/120583-ms","title":"Determining the Optimal Artificial Lift Strategy When Operating a Mature CO2 Flood in the Real World","year":2009,"lang":"en","type":"article","venue":"SPE Production and Operations Symposium","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"Lift (data mining); Flood myth; Artificial lift; Computer science; Operations research; Risk analysis (engineering); Artificial intelligence; Engineering; Petroleum engineering; 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":[0.001197768,0.000173709,0.0002745455,0.0005949419,0.0002937817,0.0007991773,0.0002928581,0.0002746122,0.0003910621],"category_scores_gemma":[0.004011103,0.0001029261,0.0001192936,0.0005718687,0.0003016367,0.0006156179,0.0001770175,0.0001373325,0.00005980923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008364015,"about_ca_system_score_gemma":0.00104832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008456603,"about_ca_topic_score_gemma":0.01800284,"domain_scores_codex":[0.9996258,0.0001333984,0.00003131812,0.00003750241,0.0001255151,0.00004647985],"domain_scores_gemma":[0.9978074,0.001289849,0.0003845058,0.00004316936,0.0004295076,0.00004541532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00136959,0.0005805981,0.3976064,0.0007966656,0.0001755383,0.001260924,0.001208124,0.3712445,0.08298916,0.002446549,0.001333512,0.1389885],"study_design_scores_gemma":[0.00006816726,0.003856488,0.5191475,0.00009455291,0.0001019402,0.0002848229,0.006485351,0.409673,0.05476014,0.002764894,0.002657534,0.0001055582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971198,0.00008034224,0.001998366,0.00003218856,0.000001397692,0.00002144233,0.00004914034,0.000009206106,0.000688168],"genre_scores_gemma":[0.9982686,0.00007085483,0.001495291,0.000004073663,9.239722e-7,0.000009387577,0.0000459152,0.000001260903,0.0001036591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008456603,"threshold_uncertainty_score":0.01681471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01401991977226471,"score_gpt":0.2462949070710237,"score_spread":0.232274987298759,"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."}}