{"id":"W3007054062","doi":"10.1016/j.geothermics.2020.101831","title":"An embedded 3D fracture modeling approach for simulating fracture-dominated fluid flow and heat transfer in geothermal reservoirs","year":2020,"lang":"en","type":"article","venue":"Geothermics","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Office of Energy Efficiency and Renewable Energy; CMG Reservoir Simulation Foundation; National Renewable Energy Laboratory; Geothermal Technologies Office; U.S. Department of Energy","keywords":"Discretization; Fluid dynamics; Fracture (geology); Geothermal gradient; Grid; Geology; Matrix (chemical analysis); Computer science; Jacobian matrix and determinant; Algorithm; Computational science; Mechanics; Geotechnical engineering; Mathematics; Applied mathematics; Geophysics; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002598309,0.0003680561,0.0005055874,0.0001324994,0.0001253812,0.00009309399,0.0002699206,0.0003437656,0.00005429238],"category_scores_gemma":[0.00004558934,0.000336528,0.0001329618,0.0002652985,0.00003415597,0.0002618829,0.00001882186,0.0005151631,0.000002467712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004923664,"about_ca_system_score_gemma":0.00002065765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001496378,"about_ca_topic_score_gemma":0.0000364982,"domain_scores_codex":[0.9982785,0.00006316118,0.0004316006,0.0004793697,0.0002077964,0.0005395895],"domain_scores_gemma":[0.9992864,0.000085404,0.00001259125,0.0003289483,0.00004657459,0.0002401538],"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":[0.0001019196,0.00003517047,0.00007188767,0.0002342408,0.00008274376,0.00000657549,0.004504319,0.9894577,0.004224678,7.822624e-7,0.000031962,0.001247974],"study_design_scores_gemma":[0.001185023,0.00005298434,0.00002936838,0.00003157472,0.0000546389,0.000002575916,0.0005480175,0.9950185,0.002055725,0.00002344906,0.000589548,0.0004085811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4686188,0.000380289,0.5300258,0.0002510291,0.00002787271,0.0002994781,0.00002077208,0.0002181003,0.0001578112],"genre_scores_gemma":[0.989778,0.00005606351,0.008673332,0.0009081385,0.0002707784,0.00003905848,0.0001156861,0.0001457266,0.00001323937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5213525,"threshold_uncertainty_score":0.9999087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323292122408494,"score_gpt":0.2292834461349607,"score_spread":0.2160505249108758,"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."}}