{"id":"W3007486725","doi":"10.35767/gscpgbull.67.4.273","title":"Data analytics and geostatistical workflows for modeling uncertainty in unconventional reservoirs","year":2019,"lang":"en","type":"article","venue":"Bulletin of Canadian Petroleum Geology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Geology; Workflow; Geostatistics; Analytics; Variogram; Data science; Kriging; Computer science; Statistics; Machine learning; Database; Spatial variability","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005756211,0.001016292,0.0007308024,0.002147459,0.00128794,0.00486506,0.00185815,0.0007692468,0.002518842],"category_scores_gemma":[0.01981315,0.0007273849,0.001524799,0.002911338,0.0008782115,0.003400264,0.002786661,0.001537519,0.0005674344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001966968,"about_ca_system_score_gemma":0.00728155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.047063,"about_ca_topic_score_gemma":0.04049793,"domain_scores_codex":[0.9977131,0.0007030477,0.0003973828,0.0003493858,0.0006929967,0.0001440128],"domain_scores_gemma":[0.9896814,0.005420871,0.000574728,0.001943095,0.001896478,0.000483444],"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":[0.0005902846,0.0004904607,0.02079907,0.0003456826,0.0002095893,0.0006758862,0.002102715,0.5925976,0.005900654,0.05698302,0.01158425,0.3077208],"study_design_scores_gemma":[0.00003363441,0.00002268946,0.0009852038,0.00003810206,0.00002037944,0.00003853101,0.0002672871,0.9591565,0.003745661,0.03014745,0.0055188,0.00002565909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05828802,0.0001938662,0.9156283,0.001320496,0.00007927849,0.0004067823,0.003816643,0.01690514,0.003361451],"genre_scores_gemma":[0.3540134,0.0003006877,0.6374745,0.0001157717,0.00003212434,0.000302621,0.005594658,0.0007913394,0.001374927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.047063,"threshold_uncertainty_score":0.0935781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03689721256054261,"score_gpt":0.2654548404645017,"score_spread":0.2285576279039591,"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."}}