{"id":"W3007474278","doi":"10.35767/gscpgbull.67.4.283","title":"Uncovering potential of seismic for reservoir characterization in Canadian oil sands","year":2019,"lang":"en","type":"article","venue":"Bulletin of Canadian Petroleum Geology","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cenovus Energy (Canada)","funders":"","keywords":"Geology; Reservoir modeling; Oil sands; Facies; Oil shale; Petroleum engineering; Seismic inversion; Probabilistic logic; Characterization (materials science); Compaction; Petrology; Seismology; Geotechnical engineering; Computer science; Paleontology; Azimuth; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058731,0.0003189815,0.0001617654,0.002820751,0.0004733099,0.0008522178,0.0003562411,0.0001780077,0.001744671],"category_scores_gemma":[0.001008898,0.0001625985,0.0001865197,0.0021751,0.0003207774,0.0002975823,0.0005812506,0.0001815577,0.0003021634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002024024,"about_ca_system_score_gemma":0.003438473,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.661211,"about_ca_topic_score_gemma":0.8249663,"domain_scores_codex":[0.9998512,0.000008818501,0.000004912375,0.00001969849,0.0000723038,0.00004298046],"domain_scores_gemma":[0.9996532,0.00005100005,0.00003331339,0.00002509218,0.0001965405,0.00004082467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004767134,0.000102673,0.4570895,0.0001958158,0.0001249965,0.0007368306,0.001176082,0.1374176,0.06847397,0.004288706,0.00297562,0.3269415],"study_design_scores_gemma":[0.00001766766,0.00004560151,0.4271193,0.00003677582,0.000060651,0.0001389122,0.001664426,0.5337009,0.0284036,0.001093303,0.00765958,0.00005927586],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722952,0.0001160125,0.01672141,0.0001262841,0.000006230487,0.00003366413,0.001749401,0.0005506924,0.008401128],"genre_scores_gemma":[0.9909949,0.00005377308,0.007287477,0.00000676177,0.000001416142,0.000004854119,0.0005899614,0.00002288813,0.001038008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.338789,"threshold_uncertainty_score":0.6815684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005400169607227999,"score_gpt":0.1778464229873105,"score_spread":0.1724462533800825,"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."}}