{"id":"W2029094163","doi":"10.2118/165555-ms","title":"A Practical Approach to History-matching Large, Multi-well SAGD Simulation Models: A MacKay River Case Study","year":2013,"lang":"en","type":"article","venue":"SPE Heavy Oil Conference-Canada","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Suncor Energy (Canada)","funders":"Suncor Energy Incorporated","keywords":"Reservoir simulation; Computer science; Matching (statistics); Process (computing); Simulation modeling; Industrial engineering; Petroleum engineering; Engineering; Mathematics; Statistics","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":[0.0003036264,0.0003385781,0.0003825123,0.0001117971,0.000108408,0.00008206654,0.0001827754,0.0001039403,0.0002956515],"category_scores_gemma":[0.0001213338,0.0003455656,0.00004626195,0.0001876915,0.00001516381,0.0003675173,0.00006362587,0.0004467597,0.00003146615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060971,"about_ca_system_score_gemma":0.0007822871,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9094067,"about_ca_topic_score_gemma":0.865435,"domain_scores_codex":[0.9979207,0.0001602042,0.0004514902,0.0004006377,0.0005285137,0.0005384837],"domain_scores_gemma":[0.9985617,0.0002854113,0.00004816386,0.0004720222,0.0002259779,0.0004067135],"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":[0.000008888663,0.0001031608,0.0001459111,0.00008378863,0.0000508668,0.0001782675,0.002129769,0.9914887,0.00000943763,0.0001396326,0.004155316,0.001506271],"study_design_scores_gemma":[0.0007904384,0.00002432782,0.0002571329,0.00001966208,0.00002251331,0.00004448594,0.002069815,0.9851117,0.000006726697,0.00004940752,0.01119705,0.0004068169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4269432,0.0000531174,0.5665219,0.00009489562,0.000496728,0.0003881454,0.00001386596,0.0001828313,0.005305377],"genre_scores_gemma":[0.9183777,0.000005006053,0.07994006,0.0001226159,0.00007796316,0.00008988352,0.00001225167,0.00006073505,0.001313778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4914346,"threshold_uncertainty_score":0.9998996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08800450963508583,"score_gpt":0.2969467543685791,"score_spread":0.2089422447334933,"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."}}