{"id":"W2342689740","doi":"10.2118/180416-ms","title":"Evaluation and Prediction of Hydraulic Fractured Well Performance in Montney Formations Using a Data-Driven Approach","year":2016,"lang":"en","type":"article","venue":"SPE Western Regional Meeting","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydraulic fracturing; Geology; Well stimulation; Petroleum engineering; Cluster analysis; Well logging; Computer science; Reservoir engineering; Petroleum; Artificial intelligence","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":[0.0003759724,0.0007561391,0.0003533059,0.001068846,0.0002489017,0.0005022479,0.0005725837,0.0004921295,0.0003164396],"category_scores_gemma":[0.0008590103,0.0002399978,0.0006065062,0.0006139632,0.0002152031,0.0003391228,0.0002383176,0.0002992489,0.00006901777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001483544,"about_ca_system_score_gemma":0.001104932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08730809,"about_ca_topic_score_gemma":0.09199826,"domain_scores_codex":[0.9998507,0.00001817064,0.00001286151,0.0000438058,0.00004949048,0.00002493355],"domain_scores_gemma":[0.9995087,0.0001617644,0.00008440917,0.00003067992,0.0001776858,0.00003680421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001183599,0.0002002285,0.05862205,0.00004471104,0.00006866951,0.0001402275,0.00003945081,0.9040101,0.009261252,0.000186743,0.0002567484,0.02705154],"study_design_scores_gemma":[0.000001440071,0.00002105974,0.008076835,0.000001662488,0.000004497196,0.000005730478,0.00001580075,0.9902093,0.001580449,0.00003732652,0.00004132225,0.000004644176],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687477,0.00006292498,0.02982046,0.00005626563,0.000006788118,0.00003327295,0.0005517888,0.0002843587,0.0004364574],"genre_scores_gemma":[0.9916612,0.00001917581,0.007500188,0.000006298018,0.000001483092,0.00002077059,0.0005825834,0.000003772295,0.0002045127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08730809,"threshold_uncertainty_score":0.1735998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04813699462381375,"score_gpt":0.2626508925652341,"score_spread":0.2145138979414203,"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."}}