{"id":"W2172627038","doi":"","title":"Evaluation of Stimulation Techniques for Oil Wells","year":2015,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Petroleum engineering; Well stimulation; Measure (data warehouse); Investment (military); Earnings; Oil well; Engineering; Payback period; Crude oil; Environmental science; Geology; Computer science; Production (economics); Petroleum; Economics; Data mining; Reservoir engineering; Accounting","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007920736,0.0002723807,0.0001989017,0.0005413531,0.0002064335,0.0004001567,0.0003085989,0.0002502311,0.002180115],"category_scores_gemma":[0.00258326,0.00009959441,0.0002286623,0.000518382,0.0002199515,0.0004679133,0.0003239608,0.0001987328,0.0002506913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651369,"about_ca_system_score_gemma":0.0003524045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004330084,"about_ca_topic_score_gemma":0.0006054276,"domain_scores_codex":[0.9993223,0.0001702084,0.00003666902,0.00004473134,0.0003809735,0.00004516242],"domain_scores_gemma":[0.9987336,0.0006073089,0.0001446244,0.0001214976,0.0003417188,0.00005126244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00207176,0.0006336251,0.01199777,0.001405476,0.00007714242,0.0002960806,0.0003090111,0.11465,0.4532968,0.008493139,0.001053885,0.4057152],"study_design_scores_gemma":[0.0001979817,0.01141781,0.0320657,0.0001906063,0.000247544,0.0008014698,0.0008449722,0.283387,0.644284,0.004951385,0.02149345,0.00011807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8379594,0.002228273,0.1405824,0.0001798395,0.00009059151,0.0004191447,0.000354992,0.0003984134,0.01778693],"genre_scores_gemma":[0.9670439,0.0007147493,0.03052695,0.00001553622,0.000008357186,0.0000815534,0.0001022224,0.00002456475,0.001482229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002180115,"threshold_uncertainty_score":0.007293284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03305092605459673,"score_gpt":0.3956039465972757,"score_spread":0.362553020542679,"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."}}