{"id":"W4388289388","doi":"10.1016/j.jgsce.2023.205161","title":"Deep neural network model for estimating montney shale gas production using reservoir, geomechanics, and hydraulic fracture treatment parameters","year":2023,"lang":"en","type":"article","venue":"Gas Science and Engineering","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Korea Institute of Energy Technology Evaluation and Planning; Ministry of Trade, Industry and Energy","keywords":"Hydraulic fracturing; Geomechanics; Petroleum engineering; Oil shale; Reservoir simulation; Tight gas; Shale gas; Directional drilling; Unconventional oil; Artificial neural network; Geology; Drilling; Engineering; Geotechnical engineering; Computer science; Machine learning","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":[0.00025884,0.0005572007,0.0003632464,0.0003121084,0.0002345807,0.0003522683,0.0006430016,0.0007219219,0.001642218],"category_scores_gemma":[0.0005956046,0.0003051237,0.0003784302,0.0003947143,0.0002219666,0.0005219093,0.0003642632,0.0007972969,0.0002666812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007614296,"about_ca_system_score_gemma":0.001278833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05487409,"about_ca_topic_score_gemma":0.07175703,"domain_scores_codex":[0.9999415,0.000008332498,0.000003279556,0.00001957475,0.00001412784,0.00001307194],"domain_scores_gemma":[0.9998354,0.00006945796,0.00001654314,0.00001110461,0.00005679346,0.00001068422],"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":[0.0000753188,0.00004659156,0.002133792,0.00001468555,0.00003634182,0.00003737619,0.00001062188,0.9629189,0.00200869,0.0006808161,0.0007858511,0.0312509],"study_design_scores_gemma":[9.203087e-7,0.000002962647,0.0001979619,6.652788e-7,0.000001580038,0.000001216389,8.325079e-7,0.9994862,0.0001670303,0.0001076862,0.00003174865,0.000001148077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5301786,0.0008878866,0.4604974,0.0006060748,0.0001254487,0.00003888417,0.001369239,0.001708495,0.00458793],"genre_scores_gemma":[0.9724467,0.0001388165,0.02184573,0.00006475691,0.00002072402,0.00003607085,0.0007991212,0.00002596629,0.004622196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05487409,"threshold_uncertainty_score":0.1091094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0229920306491279,"score_gpt":0.2390871679340169,"score_spread":0.216095137284889,"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."}}