{"id":"W2042443675","doi":"10.2118/163819-ms","title":"Characterizing Hydraulic Fractures in Shale Gas Reservoirs Using Transient Pressure Tests","year":2013,"lang":"en","type":"article","venue":"SPE Hydraulic Fracturing Technology Conference","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"IFP Energies Nouvelles; CMG Reservoir Simulation Foundation; Colorado School of Mines","keywords":"Hydraulic fracturing; Petroleum engineering; Tight gas; Unconventional oil; Geology; Oil shale; Permeability (electromagnetism); Fracture (geology); Transient (computer programming); Directional drilling; Shale gas; Geotechnical engineering; Well stimulation; Natural gas; Drilling; Reservoir engineering; Engineering; Petroleum; Computer science; Mechanical engineering","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002961744,0.0008388988,0.001015679,0.001615907,0.000249132,0.0002547963,0.00138638,0.001018792,0.00109845],"category_scores_gemma":[0.0001928177,0.0008080802,0.0002323096,0.00130975,0.00034265,0.0008231279,0.0002637904,0.002046288,0.0003147701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002197369,"about_ca_system_score_gemma":0.00009259388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001431168,"about_ca_topic_score_gemma":0.0003821564,"domain_scores_codex":[0.9958351,0.00009571679,0.0009916876,0.001011534,0.0005428378,0.001523176],"domain_scores_gemma":[0.9977736,0.0001375687,0.0002051385,0.001430586,0.0001514094,0.0003017071],"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":[0.00003958564,0.0003127639,0.007651214,0.0006466784,0.0006315067,0.0003709501,0.002551779,0.8413358,0.1056574,0.00008328286,0.001359717,0.03935927],"study_design_scores_gemma":[0.001761081,0.0001339555,0.07440002,0.001209704,0.0002481788,0.0002244694,0.0008728643,0.7126591,0.1641327,0.005956073,0.03581202,0.002589925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805382,0.001683523,0.00439015,0.004812281,0.0003832739,0.0007416415,0.00001118796,0.001785155,0.005654539],"genre_scores_gemma":[0.9961755,0.0004243539,0.002355317,0.0003128088,0.000183811,0.0001753978,0.00002607144,0.0001477501,0.000199028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1286768,"threshold_uncertainty_score":0.9998147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329394320735046,"score_gpt":0.2347235843761949,"score_spread":0.2214296411688444,"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."}}