{"id":"W2064909836","doi":"10.2118/165431-ms","title":"An Integrated Practical Approach to Forecasting Multi-well SAGD Production using Analog, Analytical, and Numerical Modeling Techniques","year":2013,"lang":"en","type":"article","venue":"SPE Heavy Oil Conference-Canada","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Suncor Energy (Canada)","funders":"Suncor Energy Incorporated","keywords":"Computer science; Reservoir simulation; Probabilistic logic; Monte Carlo method; Reservoir engineering; Field (mathematics); Industrial engineering; Petroleum engineering; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000265211,0.0002701683,0.0003322741,0.0001214002,0.0001111712,0.0001616388,0.0001270682,0.0001107384,0.0000395672],"category_scores_gemma":[0.0003370554,0.0002562882,0.00002449071,0.0003781109,0.00002447339,0.0003246175,0.00003131872,0.0004116703,0.000001552795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003078018,"about_ca_system_score_gemma":0.0005245702,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6670208,"about_ca_topic_score_gemma":0.1954219,"domain_scores_codex":[0.9983849,0.00009285047,0.0003919055,0.0003977805,0.0003060391,0.0004265089],"domain_scores_gemma":[0.9989616,0.000070774,0.00003294864,0.0002917853,0.0002696821,0.0003731918],"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":[0.000008825452,0.0000231995,0.0001926044,0.00007628746,0.00002355719,0.000003344287,0.00006900438,0.983838,0.0006669894,0.00005303404,0.0004004209,0.01464471],"study_design_scores_gemma":[0.0001057535,0.00002400518,0.00008976275,0.00005300202,0.00001723866,0.00002501467,0.0003294036,0.9972789,0.0007578362,0.00002570325,0.0009808826,0.0003125125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3173023,0.00001917363,0.6812216,0.0001954371,0.0001498746,0.0001520705,0.000004012333,0.0001775487,0.0007779771],"genre_scores_gemma":[0.6358802,0.000007922369,0.3638799,0.00003642854,0.0000819562,0.00001956988,0.00001380381,0.00003059915,0.00004960462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4715989,"threshold_uncertainty_score":0.9999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1012133125017595,"score_gpt":0.3071346765772444,"score_spread":0.2059213640754849,"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."}}