{"id":"W2030206965","doi":"10.2118/146671-ms","title":"A Semi-analytical Approach for Estimating Optimal Solvent Use in Solvent Aided SAGD Process","year":2011,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cenovus Energy (Canada)","funders":"Cenovus Energy","keywords":"Solvent; Context (archaeology); Process (computing); Process engineering; Dilution; Viscosity; Petroleum engineering; Asphalt; Genetic algorithm; Computer science; Oil field; Environmental science; Chemistry; Materials science; Engineering; Organic chemistry; Thermodynamics; Geology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001166928,0.0009032583,0.001131949,0.0009766928,0.0004559141,0.001331076,0.00101384,0.001429049,0.001394133],"category_scores_gemma":[0.003069882,0.0008310922,0.001038726,0.0005748114,0.0008491304,0.0007480098,0.0009495101,0.001113575,0.0005046762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009565791,"about_ca_system_score_gemma":0.001352941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003964759,"about_ca_topic_score_gemma":0.002271619,"domain_scores_codex":[0.9993871,0.0001893476,0.00003174726,0.0001177691,0.000221213,0.00005277933],"domain_scores_gemma":[0.9985045,0.001062477,0.0001359062,0.00004887912,0.0002270303,0.00002117993],"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":[0.00004471238,0.00003495463,0.0004073222,0.0002280774,0.0000227389,0.00008047469,0.00005428416,0.9711667,0.009727692,0.003387129,0.0001940099,0.01465186],"study_design_scores_gemma":[0.000001803949,0.00001621909,0.00005922133,0.000007143615,0.000004985844,0.000009379419,0.000006585345,0.9973089,0.001775816,0.0005742777,0.0002307487,0.000005038471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01593407,0.0004796167,0.9803721,0.0001187855,0.00002098155,0.00008009434,0.0001000795,0.0003281376,0.002566229],"genre_scores_gemma":[0.7804984,0.0009809075,0.2137793,0.00009102646,0.00004153413,0.0004307265,0.0001868515,0.0001163343,0.0038749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003964759,"threshold_uncertainty_score":0.00788337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05278590553047988,"score_gpt":0.2865176687588207,"score_spread":0.2337317632283408,"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."}}