{"id":"W3170802253","doi":"10.1016/j.petrol.2021.109089","title":"Efficient tracking and estimation of solvent chamber development during warm solvent injection in heterogeneous reservoirs via machine learning","year":2021,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Solvent; Process engineering; Computer science; Tracking (education); Oil shale; Petroleum engineering; Asphalt; Environmental science; Simulation; Materials science; Chemistry; Geology; Waste management; Engineering; Organic chemistry","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":[0.0002350925,0.0003599824,0.0004415752,0.0002919612,0.0002023867,0.0004490487,0.0004021581,0.0005270515,0.0003072175],"category_scores_gemma":[0.0008428956,0.0002632042,0.000194549,0.0002746054,0.0003311268,0.0007649708,0.0004986029,0.0004683879,0.0001034322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003332127,"about_ca_system_score_gemma":0.0005100773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002871593,"about_ca_topic_score_gemma":0.002813021,"domain_scores_codex":[0.9999325,0.000008783562,0.000003040122,0.00002177758,0.00001992123,0.00001394467],"domain_scores_gemma":[0.9996784,0.0001567806,0.00007404151,0.0000207107,0.0000498543,0.00002038868],"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":[0.0005894068,0.0001530676,0.01234855,0.0001429155,0.00004688805,0.000209981,0.0001422541,0.7253968,0.1747794,0.001509756,0.0005744868,0.08410648],"study_design_scores_gemma":[0.000002416448,0.00001419053,0.0006767904,0.00000126449,0.000002123323,0.000007003608,0.00000471513,0.9902576,0.008873107,0.0001096072,0.00004706394,0.000004009609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6863462,0.0003223975,0.3110715,0.000139554,0.00003075877,0.00002912671,0.0001006406,0.0006200995,0.001339729],"genre_scores_gemma":[0.9826847,0.00006316722,0.01669417,0.00001095421,0.000005227732,0.000009718302,0.00003843725,0.00001694099,0.0004766914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002871593,"threshold_uncertainty_score":0.005709708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093067004298763,"score_gpt":0.2417974431785272,"score_spread":0.2308667731355395,"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."}}