{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219678,0.001190727,0.001042898,0.003835465,0.0009648615,0.001262837,0.001671002,0.0007250506,0.0009844388],"category_scores_gemma":[0.003044205,0.0004817076,0.001361084,0.003173096,0.0004752047,0.001045071,0.0007814625,0.0009608975,0.0002690058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001906303,"about_ca_system_score_gemma":0.00607802,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3306213,"about_ca_topic_score_gemma":0.2463313,"domain_scores_codex":[0.9991979,0.0001080382,0.0000763407,0.0002310497,0.0002240849,0.0001626547],"domain_scores_gemma":[0.9990252,0.000335156,0.00009866028,0.0000710393,0.0004158446,0.00005405184],"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":[0.0002544127,0.0002644447,0.02962898,0.0001832468,0.0002212391,0.0003513698,0.0001375477,0.5815888,0.003531356,0.002729915,0.006004055,0.3751047],"study_design_scores_gemma":[0.000007518266,0.00001140878,0.00155724,0.000006990013,0.00001873092,0.00001376939,0.00003268783,0.9965013,0.000614609,0.0007610101,0.0004675558,0.000007174267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1435512,0.0007682532,0.8454846,0.0007720117,0.00007536363,0.0003781401,0.003159857,0.003736908,0.002073709],"genre_scores_gemma":[0.6755076,0.0005259325,0.3140491,0.0001546199,0.00005644087,0.000289164,0.007113594,0.0001307141,0.002172848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6693788,"threshold_uncertainty_score":0.6573937,"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."}}