{"id":"W4386204379","doi":"10.1016/j.geoen.2023.212279","title":"Real-time prediction of logging parameters during the drilling process using an attention-based Seq2Seq model","year":2023,"lang":"en","type":"article","venue":"Geoenergy Science and Engineering","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Science Fund for Distinguished Young Scholars; National Key Research and Development Program of China; China Scholarship Council","keywords":"Computer science; Leverage (statistics); Process (computing); Logging; Drilling; Logging while drilling; Machine learning; Artificial intelligence; 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":[0.0006965804,0.0008526605,0.0009602165,0.000440181,0.0003057891,0.0005362569,0.001059572,0.000788,0.001290826],"category_scores_gemma":[0.001640941,0.000377454,0.0006235939,0.0006973689,0.0002647384,0.0007491622,0.0005996433,0.001292746,0.0006030891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003744909,"about_ca_system_score_gemma":0.001039446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01899104,"about_ca_topic_score_gemma":0.02809305,"domain_scores_codex":[0.9997998,0.00002672296,0.000009701,0.00009849451,0.00003323488,0.0000320685],"domain_scores_gemma":[0.999081,0.000587419,0.00006209311,0.00004847882,0.000156091,0.00006494128],"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":[0.001303113,0.0003933913,0.03304062,0.0001834732,0.0001745149,0.0003564894,0.0001707385,0.8411447,0.02841456,0.001016765,0.005117439,0.08868424],"study_design_scores_gemma":[0.00000734025,0.00003067981,0.001717582,0.000002232275,0.00001297736,0.00001107666,0.000007005438,0.9967269,0.000955103,0.0003625105,0.0001588337,0.000007832053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6647201,0.001329652,0.321093,0.0009660442,0.0004988282,0.00005714664,0.005155406,0.004119708,0.002060074],"genre_scores_gemma":[0.9663215,0.0002121519,0.02589686,0.0002543365,0.00009381847,0.00005842535,0.005353278,0.0001037641,0.001705876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01899104,"threshold_uncertainty_score":0.03776103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296143625878935,"score_gpt":0.2031808853472421,"score_spread":0.1902194490884528,"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."}}