{"id":"W4213285643","doi":"10.1007/s10596-022-10134-w","title":"Huff-n-Puff (HNP) design for shale reservoirs using local dual-porosity, dual-permeability compositional simulation","year":2022,"lang":"en","type":"article","venue":"Computational Geosciences","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Porosity; Hydraulic fracturing; Monte Carlo method; Petroleum engineering; Permeability (electromagnetism); Parametric statistics; Surrogate model; Oil shale; Probabilistic logic; Markov chain Monte Carlo; Computer science; Environmental science; Geology; Mathematical optimization; Mathematics; Geotechnical engineering; Chemistry; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004355143,0.0003004897,0.0005528155,0.0003435305,0.0004717622,0.0005753104,0.0008933903,0.0008793066,0.001918329],"category_scores_gemma":[0.00105782,0.000320605,0.0003720405,0.0002097259,0.0004970907,0.0006553709,0.000808908,0.0004604937,0.0002077008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005573189,"about_ca_system_score_gemma":0.001073453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003002093,"about_ca_topic_score_gemma":0.00394775,"domain_scores_codex":[0.9998738,0.00003331661,0.000005674709,0.00002324491,0.00004165399,0.00002230609],"domain_scores_gemma":[0.9997067,0.0001172873,0.00003963027,0.00002858351,0.0000755854,0.00003222151],"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":[0.00004656126,0.00002893148,0.0003854163,0.000023402,0.00000816594,0.00003614322,0.00002266029,0.9840031,0.003360851,0.003141167,0.0001460632,0.008797481],"study_design_scores_gemma":[0.000003203952,0.00001047621,0.00002358198,0.000001220696,0.00000171753,0.000003636324,0.000002855754,0.9989398,0.0005279049,0.0003839112,0.0001002028,0.000001498967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1236154,0.0001465137,0.8668692,0.000139488,0.00003324733,0.00005721283,0.00007126604,0.0004007941,0.008666769],"genre_scores_gemma":[0.9245417,0.00006184814,0.07317428,0.00004113358,0.00000683504,0.0000677035,0.00003534653,0.00004720178,0.002023931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003002093,"threshold_uncertainty_score":0.006417394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07266001496923755,"score_gpt":0.3286107879372286,"score_spread":0.2559507729679911,"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."}}