{"id":"W4318320789","doi":"10.1016/j.geoen.2023.211495","title":"Predicting long-term production dynamics in tight/shale gas reservoirs with dual-stage attention-based TEN-Seq2Seq model: A case study in Duvernay formation","year":2023,"lang":"en","type":"article","venue":"Geoenergy Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Virtual Materials Group (Canada); University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; ENCODE; Robustness (evolution); Artificial intelligence; Oil shale; Encoder; Data mining; Algorithm; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007852977,0.0008199668,0.0008556426,0.0003308576,0.0005300157,0.0007367725,0.001363423,0.001560774,0.001598221],"category_scores_gemma":[0.001565884,0.0005897954,0.0008419701,0.0004992009,0.000588468,0.001005989,0.0007204644,0.001162949,0.0002292609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146956,"about_ca_system_score_gemma":0.001527413,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1047441,"about_ca_topic_score_gemma":0.1002781,"domain_scores_codex":[0.9998438,0.00003984597,0.000008345593,0.00005360338,0.00001432757,0.00004004124],"domain_scores_gemma":[0.9992055,0.0005458955,0.00004131186,0.00003181996,0.0001055965,0.00006978279],"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":[0.00009316862,0.00007231462,0.005758864,0.00002271385,0.00003332189,0.00009827053,0.00002804455,0.9899946,0.0005955094,0.0002867093,0.000379819,0.002636555],"study_design_scores_gemma":[0.000004449633,0.00000971689,0.000502939,9.236201e-7,0.000004360063,0.00000300013,0.000009110501,0.9991928,0.0001148896,0.0001204327,0.00003433618,0.000003017245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823058,0.0003346606,0.01415467,0.0005592162,0.00005448553,0.00001770842,0.0009376502,0.0003841985,0.001251613],"genre_scores_gemma":[0.994768,0.0000614488,0.003223787,0.00006790187,0.00001408673,0.00001726723,0.0008114793,0.00002498713,0.001011109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8952559,"threshold_uncertainty_score":0.2082689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906315947670789,"score_gpt":0.2590486082552537,"score_spread":0.2399854487785458,"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."}}