{"id":"W2092670755","doi":"10.1615/specialtopicsrevporousmedia.v4.i4.30","title":"IMPLEMENTING ARTIFICIAL NEURAL NETWORK FOR PREDICTING CAPILLARY PRESSURE IN RESERVOIR ROCKS","year":2013,"lang":"en","type":"article","venue":"Special Topics & Reviews in Porous Media An International Journal","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petroleum Technology Research Centre; University of Regina","funders":"","keywords":"Capillary pressure; Capillary action; Porosity; Geology; Permeability (electromagnetism); Saturation (graph theory); Petroleum reservoir; Relative permeability; Petroleum engineering; Artificial neural network; Mineralogy; Geotechnical engineering; Porous medium; Materials science; Chemistry; Artificial intelligence; Mathematics; Composite material","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":[0.0009435308,0.0001918564,0.0003272815,0.0001782795,0.00007799709,0.0001815364,0.000438045,0.00009872397,0.0001954505],"category_scores_gemma":[0.0002739549,0.0001896506,0.000103927,0.0001344737,0.00001308059,0.0004675794,0.00005727302,0.0005468024,0.000009058093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001294662,"about_ca_system_score_gemma":0.000025191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007478664,"about_ca_topic_score_gemma":0.0009746373,"domain_scores_codex":[0.9979528,0.00006070471,0.0009528852,0.0001827344,0.000298327,0.0005525427],"domain_scores_gemma":[0.9994213,0.00008876778,0.0001330163,0.0001336709,0.0001053409,0.000117928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001640045,0.00003783343,0.02957295,0.00009514613,0.00005679251,0.0000522645,0.001619629,0.5412242,0.0001049382,0.0005648947,0.005029406,0.4216255],"study_design_scores_gemma":[0.0008176109,0.00006152583,0.009569477,0.0007381454,0.00002862725,0.0001189575,0.000267483,0.4459789,0.0001347913,0.007317318,0.534463,0.0005041626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9311612,0.01433895,0.01059273,0.0007448145,0.03468287,0.001701845,0.00003517718,0.0002044686,0.006537971],"genre_scores_gemma":[0.7888713,0.01360691,0.01417684,0.0002927571,0.1820744,0.0003031798,0.0001757942,0.0001775617,0.0003212913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5294337,"threshold_uncertainty_score":0.7733729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377932937927509,"score_gpt":0.2773851871133248,"score_spread":0.2536058577340498,"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."}}