{"id":"W2013615662","doi":"10.2118/81006-ms","title":"Numerical Simulation and Screening of Oil Reservoirs for Gravity Assisted Tertiary Gas-Injection Processes","year":2003,"lang":"en","type":"article","venue":"SPE Latin American and Caribbean Petroleum Engineering Conference","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Petroleum engineering; Residual oil; Process (computing); Displacement (psychology); Residual; Enhanced oil recovery; Micromodel; Environmental science; Fossil fuel; Water injection (oil production); Process engineering; Positive displacement meter; Water cut; Computer science; Geology; Porous medium; Waste management; Engineering; Geotechnical engineering; Mechanical engineering; Algorithm","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":[0.0003353089,0.0003971054,0.0006490017,0.0004708484,0.0005356187,0.0006167396,0.0007974298,0.001377548,0.001977844],"category_scores_gemma":[0.001070329,0.0002670158,0.0004879955,0.0005479585,0.0006642596,0.0003568512,0.0004390238,0.0004109175,0.0001241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009883723,"about_ca_system_score_gemma":0.001132383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226953,"about_ca_topic_score_gemma":0.006046514,"domain_scores_codex":[0.9998757,0.00003112684,0.000006857892,0.0000166375,0.00002964733,0.00003999221],"domain_scores_gemma":[0.9992582,0.0004878334,0.00008413987,0.00003607938,0.00008530104,0.00004844735],"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":[0.00006440486,0.00004705772,0.00125025,0.00003128669,0.000007614056,0.00009579242,0.00003224073,0.9939061,0.002847389,0.0006874527,0.00005819609,0.0009722056],"study_design_scores_gemma":[0.00001388902,0.00002982743,0.0001855616,0.000002181984,0.000002355874,0.000004629385,0.00001282615,0.9985384,0.00106135,0.00007795453,0.00006790561,0.000003207452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972203,0.0001224641,0.01974091,0.0001538036,0.00002479326,0.00007573047,0.0002566318,0.0001476965,0.007274924],"genre_scores_gemma":[0.9927427,0.00005327076,0.006071274,0.00001333007,0.000002717859,0.000053746,0.00008607947,0.000009232622,0.0009677283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01226953,"threshold_uncertainty_score":0.02439624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297167775240468,"score_gpt":0.238416246685127,"score_spread":0.2254445689327224,"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."}}