{"id":"W2888669078","doi":"10.1016/j.jngse.2018.08.016","title":"Determination of stimulated reservoir volume and anisotropic permeability using analytical modelling of microseismic and hydraulic fracturing parameters","year":2018,"lang":"en","type":"article","venue":"Journal of Natural Gas Science and Engineering","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Universiti Teknologi Petronas","keywords":"Microseism; Anisotropy; Hydraulic fracturing; Permeability (electromagnetism); Geology; Thermal diffusivity; Mechanics; Petroleum engineering; Physics; Chemistry; Thermodynamics; Seismology; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005841689,0.0001343418,0.0003265357,0.0004299991,0.0000822856,0.00005063535,0.0001415683,0.0000673718,0.000001332685],"category_scores_gemma":[0.0001863941,0.0001104466,0.00005293237,0.0004123699,0.0003523282,0.0005079243,0.00005497756,0.0002558741,6.485066e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007262138,"about_ca_system_score_gemma":0.0000306035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004043107,"about_ca_topic_score_gemma":0.000001251835,"domain_scores_codex":[0.9988582,0.00001281057,0.0004253605,0.0001504486,0.0003258352,0.0002273497],"domain_scores_gemma":[0.9992911,0.00007097884,0.0001221584,0.0001131299,0.0002740527,0.0001285293],"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":[0.00001284665,0.000006203243,0.0009846208,0.0001504673,0.00002553759,0.000003761693,0.0004426684,0.9332754,0.06333227,9.587901e-7,0.000001383033,0.001763897],"study_design_scores_gemma":[0.0001949648,0.0000825799,0.005984948,0.0001651508,0.00005805299,0.00007862093,0.0000717809,0.9740401,0.01917226,0.0000312566,0.00001444756,0.0001058272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669024,0.001044984,0.03185086,0.0000351219,0.0001110249,0.00003726346,8.519977e-7,0.00001200858,0.000005437875],"genre_scores_gemma":[0.9858037,0.0001689086,0.01396451,0.000005716005,0.00004474172,1.409394e-7,1.58209e-7,0.000009862797,0.00000232947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04416001,"threshold_uncertainty_score":0.4503883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636384162231797,"score_gpt":0.2415488486621098,"score_spread":0.2251850070397919,"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."}}