{"id":"W1997268507","doi":"10.2118/132948-ms","title":"Integrated Uncertainty Quantification by Probabilistic Forecasting Approach in the Field Development Project","year":2010,"lang":"en","type":"article","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Monte Carlo method; Probabilistic logic; Reservoir simulation; Computer science; Permeability (electromagnetism); Uncertainty quantification; Development plan; Reservoir engineering; Data mining; Mathematical optimization; Petroleum engineering; Geology; Engineering; Statistics; Petroleum; Machine learning; Mathematics; Civil engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007294602,0.0001041087,0.00008772386,0.0000706692,0.0000394567,0.00005269047,0.0001718847,0.00007678683,0.0000146392],"category_scores_gemma":[0.0003597507,0.00006807909,0.00001755511,0.0003267698,0.000009410092,0.00005604294,0.000007900484,0.0003295632,0.000002557636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000249422,"about_ca_system_score_gemma":0.00002394501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005588081,"about_ca_topic_score_gemma":0.00005806397,"domain_scores_codex":[0.9993445,0.00004042084,0.000220438,0.0001239987,0.0001139731,0.0001566358],"domain_scores_gemma":[0.9993996,0.0003534397,0.00001408937,0.0001851286,0.00002973965,0.00001797672],"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":[0.000003942633,0.00002759389,0.0003259501,0.00007636506,0.000006779891,3.909686e-7,0.0009674813,0.9791651,0.003111651,0.000653737,0.001881834,0.0137792],"study_design_scores_gemma":[0.0001094252,0.000006140902,0.0003331039,0.000007482381,0.000001559446,0.000002083485,0.0001762618,0.9875428,0.001172259,0.00004982478,0.01049711,0.0001019594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5722654,0.00001409964,0.4214399,0.00004547517,0.0001265586,0.0003347508,0.000001011717,0.0001832306,0.005589607],"genre_scores_gemma":[0.8956744,0.000001115799,0.1040175,0.00002026612,0.0000191728,0.0001107503,0.00004360442,0.00001204606,0.0001011066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.323409,"threshold_uncertainty_score":0.2776185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05060408358402611,"score_gpt":0.289864452027759,"score_spread":0.2392603684437328,"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."}}