{"id":"W4220794821","doi":"10.1016/j.petlm.2022.03.003","title":"Prediction of permeability from well logs using a new hybrid machine learning algorithm","year":2022,"lang":"en","type":"article","venue":"Petroleum","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Particle swarm optimization; Algorithm; Computer science; Permeability (electromagnetism); Hybrid algorithm (constraint satisfaction); Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001624396,0.0001070719,0.0001996657,0.0001265319,0.0001155681,0.00001361433,0.0001181398,0.00002262551,0.00135653],"category_scores_gemma":[0.00001638338,0.0001152411,0.0001157271,0.0001926391,0.00001668657,0.00008723655,0.0000664921,0.0003055332,0.0000112327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001347189,"about_ca_system_score_gemma":0.00002757886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001072498,"about_ca_topic_score_gemma":0.00002605807,"domain_scores_codex":[0.9990626,0.00008583081,0.0002548545,0.0001691112,0.0002804536,0.0001471801],"domain_scores_gemma":[0.9996461,0.00002522249,0.00004988334,0.0001868314,0.00001636442,0.00007554331],"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":[0.000007610933,0.00002868134,0.00773848,0.00001037076,0.00008691956,0.000003941977,0.0002481621,0.9826984,0.00747793,0.000001867892,0.0002307456,0.001466911],"study_design_scores_gemma":[0.0002961147,0.00003445702,0.0006275607,0.000003430066,0.0000525093,0.000003488641,0.0001875301,0.9864009,0.001668119,0.00007739788,0.0105625,0.00008596528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.790897,0.0007315891,0.2062387,0.00007675709,0.000362303,0.00006269517,0.0002291794,0.0002902127,0.001111578],"genre_scores_gemma":[0.9977509,0.00003571587,0.001416502,0.00001066472,0.0001112229,0.000006744127,0.0002527326,0.00002411276,0.0003914482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2068539,"threshold_uncertainty_score":0.9995564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666091930832735,"score_gpt":0.2051529484483832,"score_spread":0.1884920291400558,"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."}}