{"id":"W2203629972","doi":"10.3968/7578","title":"Forecasting Petroleum Production Using the Time-Series Prediction of Artificial Neural Network","year":2015,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Series (stratigraphy); Production (economics); Residual; Algorithm; Computer science; Time series; Artificial intelligence; Backpropagation; Data mining; Machine learning; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003735522,0.0001181829,0.0001568507,0.00008212812,0.0001033423,0.00003232974,0.00004639888,0.00003863344,0.000002632002],"category_scores_gemma":[0.00003760368,0.00009727827,0.00001524215,0.0001715705,0.00003613787,0.0008449432,0.00001570357,0.00009319458,0.000002948108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009184112,"about_ca_system_score_gemma":0.00003194217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003122359,"about_ca_topic_score_gemma":0.00007905974,"domain_scores_codex":[0.9990447,0.00004981338,0.0004055649,0.0001416908,0.0001944032,0.0001638355],"domain_scores_gemma":[0.9997157,0.0000170499,0.00008516685,0.00008896497,0.00005223262,0.00004088232],"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":[0.00005400186,0.000007749862,0.0008398866,0.00002244656,0.000008377202,5.772408e-7,0.0008756442,0.9772409,0.0006323224,0.00003751157,0.0000575511,0.02022306],"study_design_scores_gemma":[0.0002389897,0.00004426723,0.0001137933,0.00004416852,0.000004575531,0.00001687664,0.001615382,0.9822512,0.001779578,0.000233192,0.01355985,0.00009809464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7144608,0.006461066,0.2685601,0.0004240893,0.007265253,0.0007349974,0.000005978321,0.0004817601,0.001606018],"genre_scores_gemma":[0.9970938,0.00009668471,0.00234427,0.00001085735,0.0002868544,0.00007020936,0.00001116134,0.00001500588,0.00007118867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.282633,"threshold_uncertainty_score":0.3966893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03726554959646914,"score_gpt":0.2362203466844808,"score_spread":0.1989547970880116,"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."}}