{"id":"W4387665412","doi":"10.2118/215056-ms","title":"Shale Gas Production Forecasting with Well Interference Based on Spatial-Temporal Graph Convolutional Network","year":2023,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Graph; Data mining; Deep learning; Cache; Artificial intelligence; Theoretical computer science; Parallel computing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003056122,0.0006467995,0.0004306258,0.0005866275,0.0002429105,0.0004685538,0.0009129699,0.0006939122,0.000855255],"category_scores_gemma":[0.0009474082,0.000337742,0.0005800395,0.0006567063,0.0003290743,0.0007548026,0.0004003763,0.0007792769,0.0001475202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001166822,"about_ca_system_score_gemma":0.0008951293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05049092,"about_ca_topic_score_gemma":0.04596574,"domain_scores_codex":[0.9998815,0.0000158445,0.000005475718,0.00004390045,0.00002365823,0.00002969156],"domain_scores_gemma":[0.9997386,0.0001054437,0.00003774638,0.00002088073,0.00007175365,0.0000255627],"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":[0.00006632898,0.00003305998,0.003567454,0.0000146102,0.00002975967,0.00005142707,0.00001440031,0.975319,0.0009450644,0.0007359619,0.0005900848,0.01863279],"study_design_scores_gemma":[7.511853e-7,0.000002215304,0.0001729224,5.725092e-7,0.000001798758,0.000001346925,0.000001015861,0.9995639,0.00008770152,0.0001446652,0.00002229188,8.789233e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7267464,0.001184852,0.2642301,0.00099065,0.0001416388,0.00003873208,0.0009542705,0.001671644,0.004041798],"genre_scores_gemma":[0.9914909,0.0001136242,0.00696325,0.00005312749,0.00001670679,0.00001204146,0.0004288441,0.0000137018,0.0009076829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05049092,"threshold_uncertainty_score":0.1003941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02587575568319151,"score_gpt":0.2270544421490248,"score_spread":0.2011786864658333,"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."}}