{"id":"W2032984299","doi":"10.2118/170108-ms","title":"Forecasting Reservoir Water Losses in a SAGD Operation. A Combined Approach","year":2014,"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":"Steam-assisted gravity drainage; Petroleum engineering; Flexibility (engineering); Steam injection; Asphalt; Environmental science; Process (computing); Reservoir engineering; Engineering; Computer science; Geology; Petroleum; Oil sands","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":[],"consensus_categories":[],"category_scores_codex":[0.0003986348,0.0002399143,0.0003292656,0.00009408087,0.00008241442,0.0001003367,0.0002495408,0.00008654591,0.0001423373],"category_scores_gemma":[0.0001622805,0.0002070561,0.00002950436,0.0001779052,0.00002000944,0.0001360532,0.00004280168,0.0003054168,0.000004960797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002776642,"about_ca_system_score_gemma":0.0004252249,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.672611,"about_ca_topic_score_gemma":0.9307224,"domain_scores_codex":[0.9984052,0.0001059013,0.0004029004,0.0002533578,0.0003444655,0.0004882284],"domain_scores_gemma":[0.9992555,0.0001578197,0.00001998429,0.0003293387,0.00008977429,0.0001475767],"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":[0.00001512715,0.000009250735,0.0009332114,0.0001843725,0.00001455611,0.000007110414,0.0001730043,0.9944423,0.0001451341,0.0003360085,0.00140666,0.002333307],"study_design_scores_gemma":[0.0006900703,0.00001912234,0.0005587319,0.00005428538,0.000004083732,0.000004041898,0.00009968238,0.9830865,0.00172375,0.0001687149,0.01330054,0.0002905119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9301981,0.00006566585,0.03032558,0.0005716289,0.0005559181,0.0001273259,0.00001104991,0.0001546813,0.03799004],"genre_scores_gemma":[0.9882044,0.00001439765,0.01048946,0.00008077898,0.000101812,0.00003540678,0.0000575138,0.00004015423,0.0009760413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2581114,"threshold_uncertainty_score":0.8443502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03597749322232406,"score_gpt":0.2282711054683364,"score_spread":0.1922936122460124,"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."}}