{"id":"W2045050774","doi":"10.2118/137133-ms","title":"Method to Improve Thermal EOR Performance Using Intelligent Well Technology: Orion SAGD Field Trial","year":2010,"lang":"en","type":"article","venue":"Canadian Unconventional Resources and International Petroleum Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shell (Canada)","funders":"Shell","keywords":"Injector; Operability; Oil field; Petroleum engineering; Oil well; Engineering; Process engineering; Thermal; Enhanced oil recovery; Completion (oil and gas wells); Computer science; Mechanical engineering; Reliability 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001361599,0.0004231978,0.0004212601,0.0006022895,0.0003555906,0.000488884,0.0007409927,0.0004353574,0.002562926],"category_scores_gemma":[0.0008700321,0.0002051129,0.0002104666,0.0003208416,0.0003043613,0.0005534629,0.000564534,0.0006564585,0.0006296425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439001,"about_ca_system_score_gemma":0.0005909249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008484545,"about_ca_topic_score_gemma":0.001037144,"domain_scores_codex":[0.9993445,0.0001309832,0.00003847253,0.00009819125,0.0003189112,0.00006887671],"domain_scores_gemma":[0.9993381,0.00006989982,0.00009692273,0.0001210087,0.0003127743,0.00006126812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000496262,0.0005188364,0.002247799,0.0001754955,0.00001889895,0.000148527,0.0002289524,0.002822957,0.9172471,0.0008631147,0.001048341,0.0741837],"study_design_scores_gemma":[0.0001693108,0.005925271,0.005084979,0.00001466757,0.000026485,0.0001921634,0.0001017521,0.01236723,0.9660687,0.0001483771,0.009851981,0.00004912719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9008147,0.0003079524,0.08908204,0.0002362356,0.00007962726,0.00111625,0.0002795117,0.001440512,0.006643128],"genre_scores_gemma":[0.9056767,0.0001077762,0.08836708,0.00008857198,0.00001554702,0.0002282643,0.0002327009,0.0001198337,0.00516342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002562926,"threshold_uncertainty_score":0.00857389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976641284062814,"score_gpt":0.2784790077305841,"score_spread":0.258712594889956,"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."}}