{"id":"W1975369753","doi":"10.2118/150197-ms","title":"Permanent Distributed Temperature Sensing (DTS) Technology Applied In Mature Fields: A Forties Field Case Study","year":2012,"lang":"en","type":"article","venue":"SPE Intelligent Energy International","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Apache (Canada)","funders":"","keywords":"Workflow; Software deployment; Gas lift; Petroleum engineering; Computer science; Optical fiber; Lift (data mining); Process engineering; Engineering; Real-time computing; Telecommunications; Data mining","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":[0.00008547169,0.0002008143,0.0001674214,0.0002953991,0.00003444078,0.00003320647,0.0001568463,0.0001962228,0.00007260439],"category_scores_gemma":[0.0000227601,0.0001989869,0.00004932733,0.0002104704,0.00001329546,0.00008659431,0.00005874095,0.0003638845,0.00001316554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001259616,"about_ca_system_score_gemma":0.000006480625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001279908,"about_ca_topic_score_gemma":0.0002684961,"domain_scores_codex":[0.9990649,0.00000793694,0.0002879161,0.0001624435,0.0001653075,0.0003115094],"domain_scores_gemma":[0.9996486,0.00004521042,0.0000260186,0.0001816818,0.00003633384,0.00006211613],"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":[0.00003713073,0.0003079078,0.006227182,0.00005223981,0.0003883221,0.001370072,0.003221276,0.9470889,0.002492245,0.007041141,0.002433651,0.02933996],"study_design_scores_gemma":[0.00220709,0.0002921286,0.0008584378,0.0005187197,0.0001484172,0.007583297,0.03516836,0.5628412,0.2640496,0.002826482,0.1204715,0.003034853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9164232,0.0006846826,0.07620496,0.0002581881,0.002934419,0.0001644817,0.00003081698,0.0005057729,0.002793473],"genre_scores_gemma":[0.9989267,0.0000751014,0.0003424119,0.00005985193,0.0003466658,0.00001858337,0.0000476012,0.00002914579,0.0001539222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3842477,"threshold_uncertainty_score":0.8114449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006716164259742816,"score_gpt":0.218676829245536,"score_spread":0.2119606649857932,"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."}}