{"id":"W2072105628","doi":"10.2118/161965-ms","title":"Integrated Microseismic Monitoring for Field Optimization in the Marcellus Shale - A Case Study","year":2012,"lang":"en","type":"article","venue":"SPE Canadian Unconventional Resources Conference","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsemi (Canada)","funders":"","keywords":"Geophone; Microseism; Geology; Borehole; Hydraulic fracturing; Fracture (geology); Seismology; Passive seismic; Economic geology; Environmental geology; Oil shale; Petroleum engineering; Engineering geology; Geotechnical engineering; Tectonics; Volcanism","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":[0.0007878409,0.0004113002,0.0003971533,0.0009534072,0.000342642,0.0005033665,0.0006101354,0.0004754277,0.0004803247],"category_scores_gemma":[0.00100351,0.0001373727,0.0002628782,0.0006951195,0.0002791678,0.0002933127,0.0003994508,0.0001975222,0.00007981621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008847092,"about_ca_system_score_gemma":0.0007056234,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01591622,"about_ca_topic_score_gemma":0.03473222,"domain_scores_codex":[0.9995703,0.0000916561,0.00002250362,0.00009621962,0.0001443856,0.00007486463],"domain_scores_gemma":[0.9992917,0.0002307752,0.0001210232,0.00006793785,0.0002110297,0.00007750034],"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":[0.001803407,0.001320886,0.2256671,0.0003554916,0.0001656227,0.003536361,0.001005487,0.4026069,0.1134668,0.001311347,0.0007631757,0.2479974],"study_design_scores_gemma":[0.00007316659,0.001570289,0.2539428,0.00002790215,0.00009023965,0.0002930005,0.00133696,0.6969697,0.04279156,0.0005554835,0.002289165,0.00005964312],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911675,0.00005970459,0.007600577,0.00003238346,0.000002142261,0.00007295165,0.0001209443,0.00007892439,0.0008648393],"genre_scores_gemma":[0.9927667,0.0000259332,0.006541062,0.000003385702,0.000002688244,0.00002395207,0.0000970227,0.00000544213,0.0005337292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9840838,"threshold_uncertainty_score":0.03164715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02494598039554711,"score_gpt":0.2457099500989131,"score_spread":0.220763969703366,"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."}}