{"id":"W2899977071","doi":"10.2118/193224-ms","title":"Applications of Machine Learning and Data Mining in SpeedWise® Drilling Analytics: A Case Study","year":2018,"lang":"en","type":"article","venue":"","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Drilling; Casing; Computer science; Analytics; Artificial neural network; Petroleum engineering; Big data; Oil field; Field (mathematics); Data mining; Completion (oil and gas wells); Artificial intelligence; Machine learning; Geology; Engineering; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002619043,0.0005354673,0.0003260383,0.002187924,0.0007679416,0.001162073,0.0009534418,0.001046533,0.0009242807],"category_scores_gemma":[0.006582546,0.000219997,0.0005241195,0.002957956,0.000743615,0.001243459,0.0008898557,0.0007532821,0.0002419016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225615,"about_ca_system_score_gemma":0.00151571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01445526,"about_ca_topic_score_gemma":0.01966891,"domain_scores_codex":[0.9984038,0.0006207805,0.0001617355,0.0001657753,0.0005289746,0.0001189305],"domain_scores_gemma":[0.9947532,0.003370993,0.0003935829,0.0003935938,0.0008568984,0.00023172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001045117,0.001833494,0.1911543,0.001359344,0.0002333658,0.02057077,0.004149568,0.3364838,0.0105659,0.01199284,0.01687496,0.4037366],"study_design_scores_gemma":[0.0001083016,0.0004966223,0.0333069,0.0002328988,0.00008359087,0.002248958,0.005108855,0.9005772,0.02072921,0.007487416,0.02953397,0.00008612293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451185,0.0006975165,0.04287288,0.002632716,0.00007800651,0.0003992439,0.001504046,0.0006886891,0.006008563],"genre_scores_gemma":[0.9208505,0.000419469,0.07537906,0.0001255162,0.00002893996,0.0001224939,0.001092307,0.00004643274,0.001935333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01445526,"threshold_uncertainty_score":0.02874225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370232963061042,"score_gpt":0.2795419492779134,"score_spread":0.255839619647303,"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."}}