{"id":"W2027496621","doi":"10.1016/j.petrol.2012.06.007","title":"Estimation of equivalent fracture network permeability using fractal and statistical network properties","year":2012,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":140,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fractal; Fracture (geology); Artificial neural network; Permeability (electromagnetism); Network model; Geology; Computer science; Geotechnical engineering; Mathematics; Artificial intelligence; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005308861,0.0002444245,0.0002710388,0.001715959,0.0002192511,0.0003954658,0.0003479996,0.0004385029,0.0003663869],"category_scores_gemma":[0.004417172,0.0002405289,0.000297244,0.0007210994,0.0003192751,0.001279177,0.000338552,0.000219341,0.00007522231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003844458,"about_ca_system_score_gemma":0.0001977354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001813984,"about_ca_topic_score_gemma":0.00191737,"domain_scores_codex":[0.9998367,0.00005485072,0.000009222207,0.0000336223,0.00004921543,0.00001645881],"domain_scores_gemma":[0.9975847,0.001605605,0.0002584652,0.0001970236,0.0002930395,0.00006126616],"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":[0.0005002234,0.0002074074,0.07927444,0.0001067346,0.0001182575,0.0003058099,0.0001228328,0.744077,0.05559724,0.005904717,0.0003117769,0.1134735],"study_design_scores_gemma":[0.000004820299,0.00001580521,0.009018126,0.000001585844,0.000008862285,0.00006408845,0.000008721411,0.9873956,0.002652508,0.0007680919,0.00005398816,0.00000780617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8060666,0.00008291164,0.1924868,0.00003964283,0.000005413256,0.0000148518,0.0001241347,0.0002781498,0.0009015268],"genre_scores_gemma":[0.9872074,0.00002236323,0.01262651,0.000002036363,0.00000316938,0.000005139439,0.00005971997,0.000009168176,0.00006452343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001813984,"threshold_uncertainty_score":0.003606915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213681650614169,"score_gpt":0.2301336229759988,"score_spread":0.2179968064698571,"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."}}