{"id":"W3094170823","doi":"10.2118/201698-ms","title":"Finding a Trend Out of Chaos, A Machine Learning Approach for Well Spacing Optimization","year":2020,"lang":"en","type":"article","venue":"SPE Annual Technical Conference and Exhibition","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Feature engineering; Computer science; Artificial intelligence; Machine learning; Workflow; Robustness (evolution); Deep learning; Field (mathematics); Big data; Data modeling; Data mining; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0001695032,0.0001178839,0.0002023318,0.0000666849,0.00004467615,0.00002597562,0.00005501476,0.0001053145,0.0000231278],"category_scores_gemma":[0.0001082922,0.0001177609,0.00004897849,0.0001307117,0.00002356427,0.0001504516,0.00002234363,0.0001762519,8.555366e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001204435,"about_ca_system_score_gemma":0.000005756564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002668863,"about_ca_topic_score_gemma":0.000001328885,"domain_scores_codex":[0.9993529,0.00002285356,0.0002189946,0.0001654775,0.00009538255,0.0001444411],"domain_scores_gemma":[0.9997187,0.00005725936,0.00003708579,0.00006554891,0.00004490208,0.00007651878],"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":[0.00002470624,0.000007703917,0.00006429372,0.0001948661,0.000008114182,3.297893e-7,0.0004205569,0.9887753,0.008456742,0.0005639003,0.00008562235,0.001397875],"study_design_scores_gemma":[0.0003911248,0.0001256486,0.0000344926,0.0000395794,0.00001417374,0.000001157807,0.0001481183,0.9952253,0.003065065,0.0001050874,0.0007179056,0.000132329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01289834,0.000107223,0.9842897,0.0001080606,0.00002876933,0.0001696406,0.00003278255,0.0002682064,0.002097284],"genre_scores_gemma":[0.8877693,0.0001198869,0.1118341,0.00001356798,0.00006638184,0.0000152752,0.0001356031,0.00001866887,0.0000271472],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.874871,"threshold_uncertainty_score":0.4802148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04433658567922221,"score_gpt":0.2804115645730584,"score_spread":0.2360749788938362,"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."}}