{"id":"W4364356154","doi":"10.30632/pjv64n2-2023a9","title":"Spatial Data Analytics-Assisted Subsurface Modeling: A Duvernay Case Study","year":2023,"lang":"en","type":"article","venue":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Computer science; Data mining; Outlier; Analytics; Kriging; Geostatistics; Spatial analysis; Kernel density estimation; Variogram; Machine learning; Database; Artificial intelligence; Statistics; Spatial variability","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":[0.001096728,0.0006457702,0.0005133323,0.0008976805,0.002076572,0.001586545,0.001462964,0.001644743,0.00138758],"category_scores_gemma":[0.002328899,0.0004348139,0.0008937343,0.002526556,0.001250166,0.0007920808,0.001525602,0.0007184339,0.0002164801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006736185,"about_ca_system_score_gemma":0.005691169,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5101579,"about_ca_topic_score_gemma":0.5594461,"domain_scores_codex":[0.99911,0.0003342728,0.00004835168,0.0001064541,0.0002360937,0.00016477],"domain_scores_gemma":[0.9986585,0.0005040543,0.00006522683,0.0001737764,0.0004561097,0.000142264],"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":[0.0002520674,0.0003028516,0.04572724,0.0001455251,0.00007886802,0.004704649,0.001335607,0.9011016,0.004116229,0.01143464,0.003818993,0.02698186],"study_design_scores_gemma":[0.00005839191,0.0001123057,0.00701708,0.00002798745,0.00003413914,0.0003261131,0.001843658,0.9755622,0.003689993,0.002451946,0.008829617,0.00004641113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9428255,0.0002641837,0.04228075,0.001634329,0.00002464419,0.0002432811,0.001474215,0.0007423339,0.01051081],"genre_scores_gemma":[0.9548167,0.000210517,0.04159143,0.00006918228,0.000004797336,0.00007770106,0.0006523123,0.0000698512,0.00250752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5101579,"threshold_uncertainty_score":0.9854538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1713537022580515,"score_gpt":0.3372802035566085,"score_spread":0.165926501298557,"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."}}