{"id":"W2087909103","doi":"10.1080/10916460903058061","title":"Pre-Post Frac Test Data Analysis for Hydraulically Fractured Vertical Tight Gas Well-Field Case Study","year":2009,"lang":"en","type":"article","venue":"Petroleum Science and Technology","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Erciyes Üniversitesi; Ryerson University","keywords":"Hydraulic fracturing; Petroleum engineering; Petrophysics; Tight gas; Permeability (electromagnetism); Natural gas; Geology; Well stimulation; Fracture (geology); Natural gas field; Fracturing fluid; Geotechnical engineering; Reservoir engineering; Engineering; Porosity; Petroleum; Chemistry; Waste management","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":[],"consensus_categories":[],"category_scores_codex":[0.0006130155,0.000224805,0.0004268281,0.001143327,0.0003921009,0.0001328999,0.001063607,0.0002129137,0.00003399263],"category_scores_gemma":[0.0009964552,0.0001787259,0.00006832708,0.002441424,0.000281161,0.0003044487,0.0002126362,0.0003899017,0.00001018841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004623468,"about_ca_system_score_gemma":0.00005961598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001398718,"about_ca_topic_score_gemma":0.000650912,"domain_scores_codex":[0.997964,0.00001264417,0.0003271938,0.000742252,0.0003762814,0.0005776077],"domain_scores_gemma":[0.9980749,0.0002353533,0.00003317315,0.001318932,0.0001527644,0.0001848319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002697569,0.006294487,0.3608097,0.0002457452,0.005219082,0.008655916,0.003326516,0.2630868,0.07240828,0.0004738754,0.008954878,0.2702549],"study_design_scores_gemma":[0.0006813626,0.001179971,0.01072306,0.000009527299,0.0009886909,0.0003037713,0.0007503802,0.9733784,0.005615311,0.0003033114,0.005612134,0.0004541455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620366,0.000281733,0.03075396,0.005556421,0.00006746028,0.0002086997,0.00001572633,0.0004604415,0.0006190304],"genre_scores_gemma":[0.9987419,0.0000375791,0.0008386936,0.0002350686,0.00005511634,0.00002264686,0.00001222972,0.00001172396,0.00004502028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7102915,"threshold_uncertainty_score":0.7288231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008496965352028788,"score_gpt":0.2612213880399974,"score_spread":0.2527244226879686,"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."}}