{"id":"W3094117021","doi":"10.2118/201267-ms","title":"Comprehensive Analysis for Production Prediction of Hydraulic Fractured Shale Reservoirs Using Proxy Model Based on Deep Neural Network","year":2020,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data pre-processing; Overfitting; Artificial neural network; Data mining; Workflow; Outlier; Random forest; Artificial intelligence; Machine learning; Database","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004928319,0.0005927439,0.0004448351,0.0007287515,0.00029394,0.0006696532,0.000693208,0.0004886723,0.0009561509],"category_scores_gemma":[0.0008291222,0.0002911693,0.0005866577,0.0005616519,0.0002238547,0.0006458631,0.0003715829,0.0004553287,0.000101635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009162299,"about_ca_system_score_gemma":0.001147913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04474957,"about_ca_topic_score_gemma":0.03103234,"domain_scores_codex":[0.999867,0.00002556835,0.000009109496,0.00003666732,0.00003537495,0.00002632242],"domain_scores_gemma":[0.9997327,0.0001084701,0.00003191085,0.00001439713,0.00009193767,0.00002064096],"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":[0.00003168807,0.00003743573,0.00968316,0.00002670307,0.00003102891,0.00007164688,0.0000111465,0.9810318,0.0007797012,0.0004172837,0.0002395387,0.007638738],"study_design_scores_gemma":[8.658232e-7,0.000003349032,0.0005753707,0.000001112918,0.000002482046,0.000001550951,0.000002502281,0.9991898,0.0001172519,0.00008373955,0.00002050721,0.00000145237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.823598,0.0004709575,0.1714307,0.0003355847,0.00004150226,0.00004230238,0.000791938,0.0007554873,0.002533473],"genre_scores_gemma":[0.9956898,0.00006568629,0.003494169,0.00001070766,0.000005038319,0.00001600303,0.0002211523,0.000009011632,0.0004885356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04474957,"threshold_uncertainty_score":0.08897817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03597076108827488,"score_gpt":0.2573732487742111,"score_spread":0.2214024876859362,"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."}}