{"id":"W2169200877","doi":"10.2118/94980-ms","title":"Use of Quantitative Risk Analysis for Uncertainty Quantification on Drilling Operations-Review and Lessons Learned","year":2005,"lang":"en","type":"article","venue":"SPE Latin American and Caribbean Petroleum Engineering Conference","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Authorization; Computer science; Drilling; Monte Carlo method; Function (biology); Risk analysis (engineering); Oil drilling; Cumulative distribution function; Petroleum engineering; Petroleum industry; Reliability engineering; Operations research; Engineering; Probability density function; Mechanical engineering; Statistics; Computer security; Mathematics; Environmental engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001998093,0.0002500476,0.000550342,0.0002533976,0.0000672692,0.00006997778,0.00008600173,0.00004383491,0.0000077309],"category_scores_gemma":[0.0001775802,0.0002544491,0.00009664251,0.0004259504,0.0000793649,0.000121098,0.00001571359,0.0001999169,0.000001629816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003831435,"about_ca_system_score_gemma":0.00001497916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001880109,"about_ca_topic_score_gemma":0.0001336263,"domain_scores_codex":[0.9989514,0.00002400011,0.0003569365,0.0003017662,0.0001200104,0.0002458302],"domain_scores_gemma":[0.999186,0.0002868503,0.0000798094,0.0002521103,0.00009030382,0.0001049433],"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":[0.000005723561,0.00001014672,0.0003698499,0.0002164203,0.0002660713,1.633139e-7,0.0001726841,0.9792276,0.001783396,0.004757281,0.00001528896,0.01317538],"study_design_scores_gemma":[0.0001387114,0.00008418469,0.003173317,0.0001715631,0.000454496,5.253918e-7,0.00009846838,0.9931797,0.0006265194,0.000008808071,0.001786108,0.0002776084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2311656,0.002138674,0.7652152,0.0005160489,0.00007666665,0.0002458918,0.000280663,0.0002681732,0.00009318149],"genre_scores_gemma":[0.9661769,0.008404491,0.02518128,0.0000297325,0.00003110572,0.00003464319,0.00005961833,0.00003591249,0.00004637321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7400339,"threshold_uncertainty_score":0.9999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04760074998095002,"score_gpt":0.2815991911181434,"score_spread":0.2339984411371933,"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."}}