{"id":"W3157575065","doi":"10.1016/j.petrol.2021.108735","title":"Optimization of steam injection in SAGD using reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canada First Research Excellence Fund; University of Alberta","keywords":"Reinforcement learning; Steam injection; Time horizon; Bellman equation; Production (economics); Reservoir simulation; Function (biology); Mathematical optimization; Computer science; Petroleum engineering; Engineering; Mathematics; Artificial intelligence","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":[0.0008231857,0.0006993269,0.001316323,0.0004954998,0.0003920253,0.0008644904,0.0005947429,0.001318696,0.001989055],"category_scores_gemma":[0.00201608,0.0006209914,0.0004837711,0.0002946238,0.0008044135,0.0006747147,0.0008732391,0.0008711764,0.0001942213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007552725,"about_ca_system_score_gemma":0.001018197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00990105,"about_ca_topic_score_gemma":0.005989478,"domain_scores_codex":[0.9998028,0.0000730528,0.000009777562,0.00003442024,0.00003682585,0.00004317693],"domain_scores_gemma":[0.9990253,0.0006190734,0.00009651676,0.0000297912,0.0001631345,0.00006613123],"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":[0.00003228388,0.00001585889,0.0001820248,0.00001307647,0.000006931476,0.00001797408,0.000005614384,0.9965449,0.0003274547,0.000299644,0.00007225754,0.002482084],"study_design_scores_gemma":[0.000003782547,0.00001009538,0.00002522074,0.000001131807,0.00000144664,0.000001177038,0.000001209055,0.9997627,0.0000743587,0.00009406654,0.0000239653,8.031211e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2684593,0.0006411943,0.7166734,0.0006763905,0.0001567653,0.0001210324,0.000101772,0.0006293063,0.01254078],"genre_scores_gemma":[0.9899791,0.00004734793,0.008745649,0.00003479106,0.000009892357,0.00002824053,0.00002376629,0.0000228614,0.001108292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00990105,"threshold_uncertainty_score":0.01968682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382787458441278,"score_gpt":0.2541123855040688,"score_spread":0.240284510919656,"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."}}