{"id":"W2101972823","doi":"10.3390/en4111950","title":"Analysis of Injection and Production Data for Open and Large Reservoirs","year":2011,"lang":"en","type":"article","venue":"Energies","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Computer science; Decoupling (probability); Reservoir engineering; Production (economics); Reservoir modeling; Scheduling (production processes); Data mining; Petroleum engineering; Mathematical optimization; Geology; Petroleum; Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003796646,0.00005062113,0.0001284384,0.0001431758,0.00002943343,0.00001835225,0.0001252819,0.00002890257,0.000006471884],"category_scores_gemma":[0.00009744753,0.00004815237,0.00001175484,0.0002279111,0.00001191191,0.0002661258,0.00009655845,0.00002570735,5.507517e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000410098,"about_ca_system_score_gemma":0.000002342695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006025162,"about_ca_topic_score_gemma":0.00006183999,"domain_scores_codex":[0.9996485,0.00001522034,0.0001046556,0.0001268466,0.00003788282,0.00006688373],"domain_scores_gemma":[0.9996132,0.00003788274,0.00001665163,0.0002888748,0.00002521448,0.00001814752],"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":[0.00001639787,0.000006972428,0.007846532,0.00007381192,0.0002447323,7.723025e-8,0.0005797513,0.9878083,0.0006515475,0.0005341048,0.000281382,0.001956379],"study_design_scores_gemma":[0.0002097762,0.0000269976,0.03723226,0.00001035082,0.0001764992,5.756577e-7,0.0002387733,0.9544517,0.00342983,0.0003360336,0.00379427,0.00009292989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9550642,0.000483617,0.043857,0.00001211704,0.00009847554,0.00009297927,0.00002838307,0.00005757175,0.0003056795],"genre_scores_gemma":[0.956857,0.0001590967,0.04272329,0.000001527677,0.00002412544,0.00001212032,0.00005318188,0.00001001686,0.0001596154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0333566,"threshold_uncertainty_score":0.1963597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08780355781228567,"score_gpt":0.3241813885645898,"score_spread":0.2363778307523042,"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."}}