{"id":"W4291017571","doi":"10.1016/j.jngse.2022.104725","title":"Prediction of maximum slug length considering impact of well trajectories in British Columbia shale gas fields using machine learning","year":2022,"lang":"en","type":"article","venue":"Journal of Natural Gas Science and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Korea Institute of Geoscience and Mineral Resources; Ministry of Trade, Industry and Energy","keywords":"Slugging; Trajectory; Azimuth; Cartesian coordinate system; Simulation; Centroid; Artificial intelligence; Geometry; Computer science; Algorithm; Engineering; Flow (mathematics); Mathematics; Geology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071554,0.00009737341,0.0003040418,0.0003403008,0.00009139023,0.00007029196,0.0001652777,0.00004047663,0.00002772133],"category_scores_gemma":[0.0002763176,0.0001268694,0.00008767066,0.0007435955,0.00005169651,0.0004412572,0.00005521043,0.0006185537,2.311864e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000205586,"about_ca_system_score_gemma":0.00007029086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007180008,"about_ca_topic_score_gemma":0.00007263551,"domain_scores_codex":[0.9987283,0.00002635625,0.0004753825,0.0001039159,0.0004295132,0.0002365628],"domain_scores_gemma":[0.9995011,0.0001022077,0.0001156805,0.00007562174,0.0001276974,0.00007770742],"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":[0.000006267996,0.000007535452,0.02529213,0.00007715442,0.00001864689,0.00001466746,0.0002532221,0.9088577,0.06456147,9.212339e-7,0.000006971458,0.0009033116],"study_design_scores_gemma":[0.0004211964,0.0001473696,0.03003304,0.0001201391,0.00001104881,0.0003310102,0.0001405015,0.9667234,0.001897613,0.00002397549,0.00004844831,0.0001023051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948799,0.001745415,0.002759181,0.000004100319,0.0005025676,0.0000543999,0.000007063962,0.00002795404,0.00001942478],"genre_scores_gemma":[0.993214,0.0001743131,0.006538928,0.000001017026,0.00004570749,9.346552e-7,8.201949e-7,0.00001568913,0.000008628515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06266386,"threshold_uncertainty_score":0.5173586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655771968101429,"score_gpt":0.2471471149042641,"score_spread":0.2305893952232498,"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."}}