{"id":"W4366599893","doi":"10.1007/s13202-023-01632-3","title":"Numerical approach on production optimization of high water-cut well via advanced completion management using flow control valves","year":2023,"lang":"en","type":"article","venue":"Journal of Petroleum Exploration and Production Technology","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy; Korea Institute of Geoscience and Mineral Resources; National Research Foundation","keywords":"Completion (oil and gas wells); Production (economics); Oil field; Well control; Water injection (oil production); Water flow; Petroleum engineering; Engineering; Environmental science; Computer science; Process engineering; Environmental engineering; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005681463,0.0006049227,0.0007354734,0.0005726203,0.0005632738,0.001190271,0.0006221698,0.001217332,0.003082278],"category_scores_gemma":[0.001233485,0.0003961042,0.0008468783,0.0003567269,0.0006028271,0.0004345804,0.0006038579,0.000757565,0.0001311638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009269287,"about_ca_system_score_gemma":0.001521618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02310303,"about_ca_topic_score_gemma":0.01200591,"domain_scores_codex":[0.9998412,0.00003665988,0.000006495695,0.00002822786,0.00004036442,0.00004707643],"domain_scores_gemma":[0.9995124,0.0002956282,0.00005803542,0.00001357614,0.00008956819,0.00003072794],"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":[0.00001780048,0.00001636714,0.0004048053,0.00003026439,0.000005819792,0.00005838301,0.0000105096,0.9961678,0.0007763298,0.001203432,0.00009653153,0.001212038],"study_design_scores_gemma":[0.000005149639,0.00001211441,0.00007225745,0.000002568119,0.000002350076,0.000002634298,0.000007629917,0.9994531,0.0001283046,0.0001914213,0.0001206726,0.000001826708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4399071,0.001473774,0.506192,0.0009861946,0.0002437563,0.0003333754,0.000430937,0.0003908862,0.050042],"genre_scores_gemma":[0.9713889,0.0002153021,0.02473846,0.00004172619,0.00001227048,0.00010252,0.00006518685,0.00002243531,0.00341322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02310303,"threshold_uncertainty_score":0.04593712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786724899189046,"score_gpt":0.2469630632691923,"score_spread":0.2290958142773018,"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."}}