{"id":"W3103970562","doi":"10.2118/201571-ms","title":"Multivariate Time Series Modelling Approach for Production Forecasting in Unconventional Resources","year":2020,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Deep learning; Multivariate statistics; Computer science; Artificial neural network; Recurrent neural network; Time series; Production (economics); Artificial intelligence; Machine learning; Process (computing); Data mining","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000538714,0.0006716666,0.000489274,0.0007571111,0.0002200148,0.0007608149,0.0006996858,0.0006303167,0.001304753],"category_scores_gemma":[0.001183849,0.0002950505,0.0008089726,0.00117291,0.0002294201,0.0007701457,0.0004012568,0.001261867,0.0002083146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006617356,"about_ca_system_score_gemma":0.0006438086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01484593,"about_ca_topic_score_gemma":0.009949022,"domain_scores_codex":[0.9998081,0.00004459111,0.00001538566,0.00005628513,0.00005605954,0.00001965954],"domain_scores_gemma":[0.9996434,0.0001722609,0.00007482029,0.00002398,0.0000696098,0.00001591751],"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":[0.00001800663,0.000014946,0.001210138,0.00002277684,0.0000241378,0.00004938319,0.00001649528,0.9850597,0.0009356338,0.002178199,0.0002316399,0.01023892],"study_design_scores_gemma":[2.503061e-7,0.000001851986,0.0001146167,8.915875e-7,0.000001241033,0.000001341981,0.000001417033,0.999496,0.00008269533,0.0002398558,0.00005851129,0.000001342111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1265472,0.0005969191,0.8683099,0.0004874459,0.00008742216,0.00003252794,0.0005514559,0.0005715365,0.002815612],"genre_scores_gemma":[0.9628108,0.0004849085,0.03385064,0.00003657398,0.00004028186,0.0000564844,0.0004242439,0.00004596592,0.002250238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01484593,"threshold_uncertainty_score":0.02951902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0647420103458704,"score_gpt":0.2666739936206114,"score_spread":0.201931983274741,"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."}}