{"id":"W2038957041","doi":"10.2523/iptc-12165-ms","title":"Integrated Modeling and Statistical Analysis of 3-D Fracture Network of the Midale Field","year":2008,"lang":"en","type":"article","venue":"International Petroleum Technology Conference","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fracture (geology); Reservoir modeling; Sensitivity (control systems); Computer science; Field (mathematics); Matrix (chemical analysis); Oil field; Drawdown (hydrology); Petroleum engineering; Well stimulation; Estimator; Fractional factorial design; Geology; Factorial experiment; Geotechnical engineering; Reservoir engineering; Engineering; Mathematics; Materials science; Statistics; Aquifer; Machine learning; Petroleum; Electronic engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003252141,0.0002791537,0.00025175,0.0005240693,0.000301908,0.0003960719,0.000616464,0.0005958178,0.0007211022],"category_scores_gemma":[0.0007448746,0.0001875922,0.0004836637,0.0002769096,0.0003542911,0.000292116,0.0002487926,0.0003068024,0.00005530008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009136896,"about_ca_system_score_gemma":0.0006190092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06974263,"about_ca_topic_score_gemma":0.04089522,"domain_scores_codex":[0.9999056,0.00001617937,0.000004660334,0.00003011247,0.00002620367,0.00001721993],"domain_scores_gemma":[0.9995909,0.000224719,0.00004617586,0.00003657856,0.00008293879,0.00001865875],"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":[0.00001665698,0.00002083013,0.003858094,0.000007904912,0.00000625574,0.00004164685,0.00001154192,0.9920692,0.001949647,0.0004189812,0.00003925238,0.001559938],"study_design_scores_gemma":[7.435147e-7,0.000004163918,0.001136609,5.019969e-7,9.984951e-7,0.000003468142,0.000003657363,0.9985469,0.0002200574,0.00006160375,0.00001944951,0.000001734386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546384,0.00004639561,0.0432828,0.00006934148,0.000005870873,0.00002876509,0.0003485557,0.0001477424,0.001432119],"genre_scores_gemma":[0.9960765,0.00001680062,0.003471131,0.000004712601,0.000001247344,0.00001211999,0.00008751806,0.00000479427,0.0003250208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06974263,"threshold_uncertainty_score":0.1386734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009087512923772259,"score_gpt":0.2191020034682176,"score_spread":0.2100144905444454,"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."}}