{"id":"W4286462274","doi":"10.1111/1365-2478.13249","title":"A machine‐learning framework to estimate saturation changes from 4D seismic data using reservoir models","year":2022,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Seismic to simulation; Reservoir simulation; Seismic inversion; Reservoir modeling; Infill; Well control; Geology; Leverage (statistics); Reservoir engineering; Computer science; Hydrogeology; Fluid dynamics; Data assimilation; Geobiology; Regional geology; Petroleum engineering; Artificial intelligence; Geotechnical engineering; Engineering; Civil engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009748418,0.0006062533,0.0005889516,0.0008154514,0.0003094498,0.0006032854,0.001026574,0.0009235528,0.001294252],"category_scores_gemma":[0.002231131,0.0006154877,0.0006396174,0.0004824614,0.0005433125,0.000542521,0.0007404195,0.0008754135,0.0002755683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006080148,"about_ca_system_score_gemma":0.001221709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0147375,"about_ca_topic_score_gemma":0.01066801,"domain_scores_codex":[0.9998065,0.00007331891,0.00001328186,0.00004156072,0.00004142369,0.0000239234],"domain_scores_gemma":[0.9992589,0.0004555401,0.00007625848,0.00003622902,0.0001392441,0.00003383288],"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":[0.0000126846,0.00001949065,0.000384022,0.000009744726,0.00001570018,0.000008931892,0.00000975237,0.9866676,0.0006535706,0.0008942303,0.00008220825,0.01124215],"study_design_scores_gemma":[5.034042e-7,0.000001378074,0.0000179053,4.32386e-7,3.769748e-7,4.236195e-7,4.51674e-7,0.9997872,0.00004759867,0.0001261496,0.00001692258,6.123902e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03175612,0.0000825085,0.9669322,0.0001111063,0.00001341984,0.00002770272,0.00006426234,0.0004226988,0.0005899145],"genre_scores_gemma":[0.7543301,0.0001265723,0.2431757,0.00007145558,0.00004553643,0.0002351474,0.0003150418,0.00008769226,0.001612884],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0147375,"threshold_uncertainty_score":0.02930349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05528039155895335,"score_gpt":0.3254742582726055,"score_spread":0.2701938667136521,"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."}}