{"id":"W2057350475","doi":"10.2118/152811-ms","title":"Parallel Preconditioners for Reservoir Simulation on GPU","year":2012,"lang":"en","type":"article","venue":"SPE Latin America and Caribbean Petroleum Engineering Conference","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; CMG Reservoir Simulation Foundation; University of Calgary; Nvidia","keywords":"Solver; Parallel computing; Computer science; Domain decomposition methods; Block (permutation group theory); Computational science; LU decomposition; Linear system; Matrix (chemical analysis); Iterative method; Sparse matrix; Algorithm; Matrix decomposition; Finite element method; Mathematics; Chemistry; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000320521,0.0003190362,0.0004776189,0.0002854425,0.0004286291,0.0004922506,0.0006599739,0.0005026099,0.004586865],"category_scores_gemma":[0.001192763,0.0002296227,0.0003870979,0.0004589,0.0004684196,0.0004835768,0.0009330253,0.00078286,0.000914558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004971789,"about_ca_system_score_gemma":0.0008687418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00692909,"about_ca_topic_score_gemma":0.006273424,"domain_scores_codex":[0.9996675,0.00009867628,0.00001573135,0.00003166417,0.0001502754,0.00003611375],"domain_scores_gemma":[0.9995797,0.0001118512,0.00003370842,0.00009253658,0.0001506517,0.00003158368],"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":[0.0001656895,0.00006607592,0.001237755,0.00009169219,0.00004840994,0.0001757191,0.0001472113,0.861943,0.0247857,0.04484006,0.006993042,0.0595056],"study_design_scores_gemma":[0.00001195822,0.000009589865,0.00005735189,0.000003017637,0.000001793242,0.00001005621,0.000006796382,0.9920441,0.002426659,0.002874331,0.002551505,0.000002870226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03575675,0.0001360569,0.9490065,0.0002598429,0.00008957693,0.00006415409,0.0001580845,0.002368687,0.01216046],"genre_scores_gemma":[0.4343098,0.0001732582,0.5556211,0.0001218222,0.00005558362,0.0002177254,0.0003821644,0.0005604501,0.008558007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00692909,"threshold_uncertainty_score":0.01534462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03296705028312258,"score_gpt":0.2882824768558893,"score_spread":0.2553154265727667,"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."}}