{"id":"W4323308319","doi":"10.2118/212568-ms","title":"Field Test Results for Real-time ROP Optimization Using Machine Learning and Downhole Vibration Monitoring - A Case Study","year":2023,"lang":"en","type":"article","venue":"SPE/IADC International Drilling Conference and Exhibition","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Calibration; Field (mathematics); Simulation; Vibration; Computer science; Torque; Machine learning; Artificial intelligence; Engineering; Real-time computing; Acoustics; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002743923,0.0001660884,0.0001473886,0.0002264531,0.0002021563,0.0001962964,0.00004628886,0.00008076779,0.000008819517],"category_scores_gemma":[0.000167156,0.0001842246,0.00002528043,0.000149114,0.000013439,0.0003116279,0.00003257639,0.0001443244,0.000004364892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004637199,"about_ca_system_score_gemma":0.00001027624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000193565,"about_ca_topic_score_gemma":0.00001952512,"domain_scores_codex":[0.9990961,0.00001668855,0.0002920376,0.0002609375,0.0001464233,0.0001878237],"domain_scores_gemma":[0.9994482,0.0002447859,0.00005620774,0.00007308249,0.0001183639,0.00005932758],"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":[0.00003192464,0.00001728871,0.002166755,0.0000498606,0.00003662319,0.00005593591,0.0009703877,0.9770412,0.0166816,0.00003734939,0.00001493388,0.002896092],"study_design_scores_gemma":[0.0006291587,0.0001579856,0.0001227621,0.0001667297,0.0000255578,0.00008382948,0.0006654917,0.9949495,0.00284961,0.00007375489,0.00007815752,0.00019744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.823069,0.00003775797,0.1748489,0.00009287538,0.000497818,0.0003104998,0.00005980007,0.0005178024,0.0005655456],"genre_scores_gemma":[0.9933063,0.0006940017,0.005252806,0.000003881535,0.0003315397,0.00002334086,0.0002205366,0.00003236947,0.0001352328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1702373,"threshold_uncertainty_score":0.751246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266126743564028,"score_gpt":0.2685267700532383,"score_spread":0.245865502617598,"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."}}