{"id":"W4415171745","doi":"10.2118/228052-ms","title":"Water-Alternating-Gas and CO2 Storage Optimization Using Time-Lapse Geophysical Monitoring and Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Reinforcement learning; Enhanced oil recovery; Lead (geology); Process (computing); Artificial neural network; Brine; Reservoir simulation; Reservoir engineering; Process control; Control (management)","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":[0.0006588855,0.0007305274,0.0005036727,0.0002743003,0.000184052,0.0005367331,0.0006741349,0.0007380557,0.0006323668],"category_scores_gemma":[0.00180642,0.0002940973,0.0003418827,0.0001783381,0.000751199,0.0005233817,0.0005469879,0.0006930214,0.00007469556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008102043,"about_ca_system_score_gemma":0.001197899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01113653,"about_ca_topic_score_gemma":0.007188001,"domain_scores_codex":[0.9998661,0.00003432565,0.000005907657,0.0000362322,0.00002765515,0.00002969187],"domain_scores_gemma":[0.999371,0.0003570236,0.00009846204,0.00003549759,0.00008982032,0.00004822977],"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":[0.00002278808,0.00002260223,0.0005622919,0.00001102268,0.000008828685,0.00001863473,0.000005520563,0.9946302,0.001035984,0.0004814753,0.00008113932,0.003119483],"study_design_scores_gemma":[0.00000212673,0.000006966644,0.00003368722,5.461289e-7,9.31493e-7,8.40068e-7,9.27819e-7,0.9996238,0.0001873129,0.0001192124,0.00002289928,7.254405e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.421202,0.0004356386,0.5723707,0.0005454714,0.00007406665,0.00008173956,0.0001086709,0.0007673506,0.00441436],"genre_scores_gemma":[0.9813228,0.00003221186,0.01806645,0.00005064575,0.00000483075,0.00003467238,0.00003265018,0.00001702943,0.0004388369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01113653,"threshold_uncertainty_score":0.02214342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01385532293827596,"score_gpt":0.2669366816315627,"score_spread":0.2530813586932867,"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."}}