{"id":"W4407041270","doi":"10.3390/en18030657","title":"Boosting Reservoir Prediction Accuracy: A Hybrid Methodology Combining Traditional Reservoir Simulation and Modern Machine Learning Approaches","year":2025,"lang":"en","type":"article","venue":"Energies","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Boosting (machine learning); Reservoir simulation; Reservoir modeling; Reservoir computing; Computer science; Machine learning; Artificial intelligence; Petroleum engineering; Engineering; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001036317,0.0002338162,0.0003041871,0.0003333725,0.0002628156,0.0000982335,0.0001463987,0.0001264091,0.00001107584],"category_scores_gemma":[0.001413362,0.0002485233,0.00006402043,0.0002817161,0.00005433728,0.0003704191,0.00007971143,0.0004660081,0.00000109664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000699108,"about_ca_system_score_gemma":0.00002227374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002271765,"about_ca_topic_score_gemma":0.000004766759,"domain_scores_codex":[0.9984667,0.000349103,0.0003943774,0.0003044392,0.00019868,0.0002867421],"domain_scores_gemma":[0.9967553,0.002852005,0.00005486725,0.0002195108,0.00005306317,0.00006527238],"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":[0.00003111077,0.000009557572,0.00163439,0.00014362,0.00007423246,0.000002582537,0.0004096521,0.9904473,0.001115417,0.001474911,0.00008077695,0.004576399],"study_design_scores_gemma":[0.0005620803,0.00002948831,0.002817747,0.00008181847,0.00002658024,0.000005783016,0.0001136817,0.9859734,0.001589003,0.006544198,0.002069836,0.0001863849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6018456,0.001577758,0.3937575,0.00009835517,0.0002865469,0.00009888023,0.00001462234,0.0006632051,0.001657596],"genre_scores_gemma":[0.9461415,0.0001043182,0.05297384,0.00001419303,0.0001557856,0.00004238026,0.0001351593,0.00004136822,0.0003914066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3442959,"threshold_uncertainty_score":0.9999967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1405211773107272,"score_gpt":0.3107075264611042,"score_spread":0.170186349150377,"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."}}