{"id":"W2578398986","doi":"10.1007/s10596-016-9611-2","title":"Gaussian Processes for history-matching: application to an unconventional gas reservoir","year":2017,"lang":"en","type":"article","venue":"Computational Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mathematical optimization; Gaussian; Reservoir simulation; Matching (statistics); Gaussian process; Computer science; Differential evolution; Hydrogeology; Algorithm; Optimization problem; Convergence (economics); Mathematics; Geology; Statistics; Petroleum 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":[0.002177681,0.0004689114,0.0009822536,0.0007858254,0.0008707701,0.001242798,0.001805496,0.002524215,0.002252647],"category_scores_gemma":[0.009574436,0.0004452908,0.0006532765,0.001544654,0.001116694,0.001569616,0.002297189,0.00145471,0.0002373283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138073,"about_ca_system_score_gemma":0.002197215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0262879,"about_ca_topic_score_gemma":0.01671692,"domain_scores_codex":[0.9997281,0.0001076627,0.00001734466,0.00005916034,0.00005501523,0.00003259696],"domain_scores_gemma":[0.9972234,0.001931729,0.0001540549,0.0001962744,0.0003268531,0.0001677862],"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":[0.0001252474,0.0001424468,0.001827617,0.00005131206,0.00003187987,0.0001650815,0.0001141823,0.9363021,0.001268842,0.0308716,0.0005947542,0.02850491],"study_design_scores_gemma":[0.000004171315,0.000003700317,0.00004562356,8.666264e-7,0.00000128926,0.00000346442,0.000003856979,0.9979559,0.0001328573,0.001779402,0.00006579492,0.000002942117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1623805,0.0002759715,0.8332788,0.0005979853,0.0001022582,0.00009833911,0.0001647976,0.0006181323,0.002483238],"genre_scores_gemma":[0.8477722,0.0002170772,0.1475454,0.0001045207,0.00004598254,0.0000797231,0.0001552849,0.0001736542,0.003906216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0262879,"threshold_uncertainty_score":0.05226982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04658954456701829,"score_gpt":0.3309594182852303,"score_spread":0.284369873718212,"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."}}