{"id":"W2512931854","doi":"10.2118/181479-ms","title":"Multi-thermal Fluid Assisted Gravity Drainage Process to Enhance the Heavy Oil Recovery for the Post-SAGD Reservoirs","year":2016,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; CMG Reservoir Simulation Foundation; National Science Foundation","keywords":"Steam-assisted gravity drainage; Petroleum engineering; Steam injection; Enhanced oil recovery; Oil field; Process (computing); Thermal; Drainage; Scaling; Geology; Environmental science; Oil sands; Materials science; Thermodynamics; Asphalt; Computer science","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":[0.0001641174,0.0002330107,0.0002857785,0.000360566,0.000204374,0.0002655972,0.0003054083,0.0002110486,0.0007672083],"category_scores_gemma":[0.0002265041,0.000116893,0.0002958284,0.0002949791,0.0002732852,0.0005295367,0.0004318173,0.0002711347,0.0001344891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002564792,"about_ca_system_score_gemma":0.0002942297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006890144,"about_ca_topic_score_gemma":0.0008931766,"domain_scores_codex":[0.9998909,0.00001058342,0.00001001392,0.00001545942,0.00005126857,0.00002170142],"domain_scores_gemma":[0.9999099,0.00001599022,0.00002236537,0.00001615703,0.0000249803,0.0000105547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001167125,0.00007752088,0.001369996,0.0001968456,0.00000715235,0.0001226896,0.00006244524,0.006726879,0.9673427,0.001308383,0.0002565855,0.02241215],"study_design_scores_gemma":[0.00002977265,0.0002152782,0.00128967,0.000007595512,0.000009942077,0.00007145159,0.00004675045,0.09896187,0.8964296,0.0002850804,0.00263534,0.0000175663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542053,0.0003595621,0.04313341,0.0001097646,0.00004444641,0.00005504486,0.00008403181,0.0003641105,0.001644245],"genre_scores_gemma":[0.9905433,0.00007956263,0.008875801,0.00001240846,0.000003018697,0.00001243876,0.00002556801,0.000009528327,0.0004383599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007672083,"threshold_uncertainty_score":0.002566516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185917950595494,"score_gpt":0.2877243846250468,"score_spread":0.2691325895654974,"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."}}