{"id":"W3000664444","doi":"10.2523/iptc-20344-ms","title":"Data Mining: A Novel Strategy for Production Forecast in Tight Hydrocarbon Resource in Canada by Random Forest Analysis","year":2020,"lang":"en","type":"article","venue":"International Petroleum Technology Conference","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tight gas; Petrophysics; Petroleum engineering; Computer science; Production (economics); Oil shale; Fossil fuel; Productivity; Data mining; Environmental science; Geology; Engineering; Hydraulic fracturing; Geotechnical engineering","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":[0.0001463757,0.0001817901,0.0003610449,0.0006848014,0.00002361551,0.00003375587,0.0008724789,0.0001272587,0.00002820157],"category_scores_gemma":[0.0002346056,0.0001899122,0.00004311555,0.001139356,0.00005481796,0.0001920168,0.0001017364,0.0002712852,0.000001718497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000275124,"about_ca_system_score_gemma":0.0002155586,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02692773,"about_ca_topic_score_gemma":0.8435751,"domain_scores_codex":[0.9985128,0.00001620154,0.0004782194,0.0004848971,0.0002513347,0.0002565699],"domain_scores_gemma":[0.9993794,0.00005369478,0.00008412855,0.0003524509,0.00006591052,0.0000644346],"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":[0.00013171,0.0000538829,0.05529536,0.00002550554,0.0006298511,0.00001588696,0.0001289609,0.9334127,0.005619288,0.0003533576,0.002682901,0.001650643],"study_design_scores_gemma":[0.001086734,0.00002429441,0.00074679,0.00001696446,0.00005962739,0.000001961992,0.0006194731,0.9882205,0.0007861718,0.00008185578,0.008171824,0.0001838194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9144145,0.0001820207,0.06845839,0.01450458,0.0001566212,0.0002506484,0.0003760523,0.0001781794,0.001478965],"genre_scores_gemma":[0.9984399,0.00003897929,0.0003178311,0.00007928485,0.00004503545,0.00009843187,0.000877709,0.00001841301,0.00008438788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8166474,"threshold_uncertainty_score":0.979552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0365635331189019,"score_gpt":0.2412709148714822,"score_spread":0.2047073817525803,"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."}}