{"id":"W1964717999","doi":"10.2118/149010-ms","title":"Design of Solvent-Assisted SAGD Processes in Heterogeneous Reservoirs Using Hybrid Optimization Techniques","year":2011,"lang":"en","type":"article","venue":"Canadian Unconventional Resources Conference","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Saudi Aramco","keywords":"Simulated annealing; Genetic algorithm; Taguchi methods; Computer science; Solvent; Process engineering; Petroleum engineering; Mathematical optimization; Materials science; Environmental science; Algorithm; Mathematics; Engineering; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0002780021,0.0001577996,0.0001999227,0.0004696592,0.00004535614,0.00003018414,0.0002327092,0.00008836573,0.0002950772],"category_scores_gemma":[0.0001324462,0.0001807514,0.00004122386,0.0003661663,0.00005531987,0.0001371362,0.00001466027,0.0001231619,0.000001812894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001718463,"about_ca_system_score_gemma":0.0002952036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004174464,"about_ca_topic_score_gemma":0.003667725,"domain_scores_codex":[0.9989279,0.00007938966,0.0003648293,0.0001803729,0.0001730436,0.0002744945],"domain_scores_gemma":[0.9992982,0.00007231819,0.0000608594,0.0001820056,0.0002068839,0.0001797601],"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":[0.00001351536,0.00001084132,0.0006500584,0.0001336248,0.00001911886,0.00002424376,0.0002761445,0.9977638,0.0006551062,0.00004904105,0.00001148081,0.0003930071],"study_design_scores_gemma":[0.0001954823,0.00003591372,0.0004205242,0.00023501,0.00001022909,0.00001995338,0.00004927535,0.9863573,0.01174009,0.0003568024,0.0003655173,0.0002138802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3815442,0.0003210802,0.6162268,0.000007609031,0.00008516266,0.0002514117,0.00003002322,0.0001144547,0.0014193],"genre_scores_gemma":[0.9029752,0.00004146193,0.09683985,0.000007270214,0.00001759913,0.00002312983,0.00002171855,0.00002785956,0.00004589757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.521431,"threshold_uncertainty_score":0.7370827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08299787666489482,"score_gpt":0.259681119581798,"score_spread":0.1766832429169031,"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."}}