{"id":"W2995440822","doi":"10.1016/j.petrol.2019.106805","title":"Determination of an infill well placement using a data-driven multi-modal convolutional neural network","year":2019,"lang":"en","type":"article","venue":"Journal of Petroleum Science and Engineering","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Korea Institute of Geoscience and Mineral Resources; Ministry of Trade, Industry and Energy; Ewha Womans University; Computer Modelling Group; National Research Foundation of Korea; Korea Gas Corporation; Ministry of Science and Technology","keywords":"Convolutional neural network; Computer science; Artificial neural network; Modal; Feed forward; Infill; Feature (linguistics); Artificial intelligence; Algorithm; Pattern recognition (psychology); Engineering; Structural engineering; Control engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007611995,0.0001368937,0.0002310672,0.0002814043,0.00004571205,0.00005263599,0.0003559987,0.00004295035,0.000007011144],"category_scores_gemma":[0.00003690894,0.0001320896,0.00003287999,0.0002717882,0.0000471659,0.001070159,0.00007340669,0.0002011452,0.00000126106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001039802,"about_ca_system_score_gemma":0.0000591208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004426208,"about_ca_topic_score_gemma":0.000001158525,"domain_scores_codex":[0.9987528,0.000006772258,0.0003735065,0.0001441147,0.0004254392,0.0002973297],"domain_scores_gemma":[0.9994003,0.00003682603,0.000100217,0.0001993663,0.0001226049,0.0001406954],"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":[0.000005127379,0.00000909299,0.0007508627,0.0000596111,0.00001030244,0.000003796163,0.00005724128,0.9391105,0.05949575,0.00002012685,0.000005099585,0.0004724558],"study_design_scores_gemma":[0.0004163873,0.00009815503,0.002542743,0.0001146839,0.00001863138,0.0001043607,0.00004560735,0.9954998,0.0007864251,0.000001926947,0.0002319802,0.0001392648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8864182,0.0002522093,0.1123113,0.000004443581,0.00089639,0.00004072976,0.000004983502,0.00003184844,0.00003991151],"genre_scores_gemma":[0.9636489,0.00003067094,0.03609795,0.000004774125,0.0001920804,3.958719e-7,0.000002277276,0.0000175486,0.000005374805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07723078,"threshold_uncertainty_score":0.5386459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01766097858687922,"score_gpt":0.2398608259998425,"score_spread":0.2221998474129633,"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."}}