{"id":"W4317641843","doi":"10.1306/11192121014","title":"A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data","year":2022,"lang":"en","type":"article","venue":"AAPG Bulletin","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Geology; Oil shale; Petroleum engineering; Transient (computer programming); Reservoir modeling; Transient analysis; Petrology; Transient response; Computer science; Paleontology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000622893,0.00007662018,0.00009901476,0.00003258025,0.0005549417,0.00005989101,0.0001498403,0.00001621991,0.0009514388],"category_scores_gemma":[0.00003067373,0.00007398015,0.00001195639,0.00005312116,0.00003889773,0.000086195,0.00006221282,0.0001294759,0.000002707024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001736962,"about_ca_system_score_gemma":0.00001198102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006040593,"about_ca_topic_score_gemma":0.000004474627,"domain_scores_codex":[0.9992032,0.0001388552,0.0001108417,0.0002742755,0.0000944353,0.0001784047],"domain_scores_gemma":[0.9996458,0.00008939705,0.00005954514,0.0001444023,0.0000144482,0.00004643132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001262483,0.00009253158,0.1827742,0.0005308968,0.0002279407,0.00008258418,0.003180401,0.05742386,0.00375007,0.0001064697,0.3120939,0.4384746],"study_design_scores_gemma":[0.0001275343,0.00006266568,0.004990668,0.000009547378,0.00002004146,0.00001915932,0.00004313269,0.3952747,0.00004844609,0.00005556534,0.5992751,0.00007346748],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769568,0.003863762,0.01206508,0.005058965,0.0003128097,0.0005679715,0.0005787778,0.0001600598,0.0004357487],"genre_scores_gemma":[0.9905397,0.0002425239,0.004430363,0.001686015,0.0001308271,0.000004811614,0.002028258,0.000007972099,0.0009295455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4384012,"threshold_uncertainty_score":0.9999619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02460473955033314,"score_gpt":0.2204145249273531,"score_spread":0.1958097853770199,"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."}}