{"id":"W2916737519","doi":"10.2118/194105-ms","title":"Prediction of Penetration Rate Ahead of the Bit through Real-Time Updated Machine Learning Models","year":2019,"lang":"en","type":"article","venue":"SPE/IADC International Drilling Conference and Exhibition","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Haliburton Forest & Wild Life Reserve","funders":"University of Southern California","keywords":"Drilling; Rate of penetration; Computer science; Artificial neural network; Idle; Process (computing); Maximization; Real-time computing; Simulation; Artificial intelligence; Engineering; Mechanical engineering; Mathematical optimization; Mathematics","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":[0.0001810475,0.0001290713,0.000163996,0.00008428865,0.00003900906,0.0000282433,0.0001057778,0.00008339225,0.0001167507],"category_scores_gemma":[0.00001492387,0.0001137363,0.00005607113,0.0001164912,0.00003286211,0.0003544025,0.00002542877,0.0001660131,0.00001231973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003496986,"about_ca_system_score_gemma":0.00001647948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001183444,"about_ca_topic_score_gemma":0.000007939027,"domain_scores_codex":[0.9991669,0.0000281316,0.0003229106,0.0001561954,0.0002057491,0.0001201037],"domain_scores_gemma":[0.9995634,0.00003770791,0.0001008825,0.0001194251,0.0001567439,0.00002186702],"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":[0.00001455406,0.000008883134,0.001024712,0.00005936427,0.00003933553,1.884189e-7,0.000362008,0.7480561,0.2448848,0.005117304,0.00002767666,0.0004050212],"study_design_scores_gemma":[0.0002822636,0.00003717055,0.001625592,0.0002665542,0.00001588281,0.000003074536,0.00004174791,0.9450998,0.04936372,0.00296958,0.0001993635,0.00009524416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265479,0.0000636812,0.05578439,0.00007362547,0.0006193203,0.0001683708,0.00006659945,0.0001333659,0.01654273],"genre_scores_gemma":[0.9980133,0.0008048185,0.0004824771,0.000006995385,0.0000705394,0.000003745329,0.0002420434,0.00001968048,0.0003564194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1970437,"threshold_uncertainty_score":0.4638031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946503261162046,"score_gpt":0.2037019117441636,"score_spread":0.1842368791325431,"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."}}