{"id":"W3107768742","doi":"10.1016/j.petrol.2020.108133","title":"A machine learning model for predicting multi-stage horizontal well production","year":2020,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canada First Research Excellence Fund","keywords":"Completion (oil and gas wells); Stage (stratigraphy); Perforation; Production (economics); Petroleum engineering; Production planning; Geology; Computer science; Statistics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000606182,0.0001185702,0.0001968591,0.0002070147,0.0001308775,0.00007378035,0.000164704,0.00003442006,0.000002052644],"category_scores_gemma":[0.0003884487,0.0001009844,0.00007511482,0.0002714438,0.00003335741,0.0004688891,0.00002734624,0.0003387308,7.390888e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006717271,"about_ca_system_score_gemma":0.00003721418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003354099,"about_ca_topic_score_gemma":0.000001837582,"domain_scores_codex":[0.9990368,0.000004040544,0.0002645966,0.0001390867,0.0003051083,0.0002503151],"domain_scores_gemma":[0.9995449,0.00002087899,0.00006890857,0.00005680021,0.0001016956,0.0002068088],"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":[0.000007471197,0.000004982448,0.0003794127,0.00009897202,0.000022918,0.000003330424,0.0005117994,0.9363483,0.06213419,0.000002105516,0.00001931846,0.000467181],"study_design_scores_gemma":[0.0002689783,0.00008263582,0.0001397313,0.00003744436,0.00002808611,0.0000214491,0.0001434229,0.9932372,0.004572649,8.730844e-7,0.001360315,0.0001072435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4937686,0.0005987791,0.504895,0.0002938346,0.000247774,0.00004295641,0.00000214331,0.00009223953,0.00005870285],"genre_scores_gemma":[0.9905555,0.0001465357,0.008896973,0.00001247232,0.0002958977,0.00000216846,5.090853e-7,0.0000193389,0.00007065851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4967869,"threshold_uncertainty_score":0.4118025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732373067938664,"score_gpt":0.2260921401139579,"score_spread":0.2087684094345713,"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."}}