Unsupervised Methods to Classify Real Data from Offshore Wells

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American Journal of Operations Research

ISSN Print: 2160-8830
ISSN Online: 2160-8849
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"Unsupervised Methods to Classify Real Data from Offshore Wells"
written by Antônio Orestes De Salvo Castro, Mayara De Jesus Rocha Santos, Fabiana Rodrigues Leta, Cláudio Benevenuto C. Lima, Gilson Brito Alves Lima,
published by American Journal of Operations Research, Vol.11 No.5, 2021

has been cited by the following article(s):

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[1]	Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells
	… Intelligence and Soft …, 2022

[2]	Machine learning prediction of bottomhole flowing pressure as a time series in the volve field
	2022

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[1]	Anomaly detection in oil-producing wells: a comparative study of one-class classifiers in a multivariate time series dataset Journal of Petroleum Exploration and Production Technology, 2024 DOI:10.1007/s13202-023-01710-6

[2]	Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells Applied Computational Intelligence and Soft Computing, 2022 DOI:10.1155/2022/1558381