{"id":"W2930638270","doi":"10.4236/apm.2019.93012","title":"Stochastic Modeling and Assisted History-Matching Using Multiple Techniques of Multi-Phase Flowback from Multi-Fractured Horizontal Tight Oil Wells","year":2019,"lang":"en","type":"article","venue":"Advances in Pure Mathematics","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Alberta Innovates - Technology Futures; University of Calgary","keywords":"Matching (statistics); Monte Carlo method; Key (lock); Reservoir simulation; Uncertainty quantification; Computer science; Mathematical optimization; Range (aeronautics); Field (mathematics); Set (abstract data type); Algorithm; Petroleum engineering; Mathematics; Machine learning; Statistics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002448747,0.0002670393,0.0005018756,0.000173228,0.0000237269,0.00001695428,0.0001668493,0.000158785,0.00002078078],"category_scores_gemma":[0.0001408152,0.0002615644,0.0000647339,0.0001180565,0.00002717413,0.0003574663,0.00004547575,0.0002840644,0.000002482698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000179964,"about_ca_system_score_gemma":0.0000145273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002310516,"about_ca_topic_score_gemma":0.00002505513,"domain_scores_codex":[0.9987508,0.00003549263,0.0005557425,0.0002282784,0.0001944786,0.0002351897],"domain_scores_gemma":[0.9991146,0.0003393934,0.0001106146,0.0003243549,0.00004615046,0.00006490193],"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":[0.000008514899,0.00008905448,0.00008527751,0.0005359492,0.00002237067,0.000002169528,0.001375342,0.9596131,0.0341971,0.00002213139,9.81251e-7,0.004047941],"study_design_scores_gemma":[0.001307256,0.00002048746,0.000007459272,0.0005457147,0.00002271863,0.000002452755,0.0001454143,0.9949391,0.002252459,0.0003165106,0.0001656138,0.0002748134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4468712,0.003113849,0.549508,0.000001211648,0.0002147141,0.0001178938,0.00001403967,0.0001222879,0.00003678649],"genre_scores_gemma":[0.5090784,0.00007869575,0.4907438,0.000002274581,0.00002101634,0.000007451121,0.000009149471,0.0000419635,0.00001723599],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06220719,"threshold_uncertainty_score":0.9999837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02716578003868394,"score_gpt":0.3044388795886885,"score_spread":0.2772730995500046,"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."}}