{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003210678,0.0001672723,0.0001538438,0.0004856046,0.00009881352,0.0001243781,0.0002968576,0.0001598673,0.0008090896],"category_scores_gemma":[0.00008913711,0.0001708305,0.0000594404,0.0001394247,0.00003352466,0.0001294562,0.00004352513,0.0003924638,0.00002679266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003214,"about_ca_system_score_gemma":0.00009909416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144438,"about_ca_topic_score_gemma":0.001681066,"domain_scores_codex":[0.9990154,0.00002007443,0.0002653063,0.0002404673,0.0001931134,0.0002656771],"domain_scores_gemma":[0.9992817,0.0000881511,0.0000370811,0.0001566776,0.0001404031,0.000295991],"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":[0.0008120579,0.00002597331,0.00442327,0.00006040471,0.00017062,0.00001232513,0.0001799772,0.8909762,0.02938153,0.0112633,0.0003359291,0.06235842],"study_design_scores_gemma":[0.001634835,0.0001330971,0.0007506682,0.0000397375,0.00001006257,0.00001365813,0.00004821933,0.8410822,0.002994637,0.0003539597,0.152709,0.0002299122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9374845,0.0000416291,0.05526153,0.0005966567,0.001315262,0.0001310528,0.00002401931,0.00006994464,0.005075428],"genre_scores_gemma":[0.9804788,0.00001563874,0.01750842,0.0000968217,0.0003824198,0.00002564282,0.00001291597,0.00002071294,0.001458641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1523731,"threshold_uncertainty_score":0.8858964,"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."}}