3W Dataset 2.0.0: a realistic and public dataset with rare undesirable real events in oil wells

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Authors: Ricardo Emanuel Vaz Vargas, Afrânio José de Melo Junior, Cláudio Benevenuto de Campos Lima, Jean Carlos Dias de Araújo, Mateus de Araujo Fernandes, Rogério Leite Alves Pinto, Jader Riso Barbosa Jr., Celso J. Munaro, Patrick Marques Ciarelli, Flávio Miguel Varejão, Felipe Muntzberg Barrocas, J. N. E. Carneiro
+9 more Matheus Lima Scramignon, Rodrigo Castello Branco, Lucas Pereira de Gouveia, Eduardo Toledo de Lima Júnior, Igor Carvalho Lima Oliveira, Lucas Gouveia Omena Lopes, Lucas Pereira de Gouveia, Guilherme Fidelis Peixer, Jaime Andrés Lozano
Cited by: 2
References: 34

Abstract

In the oil industry, undesirable events in oil wells can cause economic losses, environmental accidents, and human casualties. In 2019, recognizing the importance and the lack of public datasets related to undesirable events in oil wells, Petrobras developed and publicly released the first version of the 3W Dataset, which is essentially a set of Multivariate Time Series labeled by experts. Since then, the 3W Dataset has been developed collaboratively and has become a foundational reference for numerous works in the field. This data article describes the current publicly available version of the 3W Dataset, which contains additional instances, more variables, and a new label. Furthermore, a new data structure has been developed to make data access more robust and efficient. The detailed description we provide encourages and supports the 3W community and new 3W users to improve previous published results and to develop new robust methodologies, digital products and services capable of detecting undesirable events in oil wells with enough anticipation to enable corrective or mitigating actions.

Fields of Study & Subjects

Oil and Gas Production Techniques Anomaly Detection Techniques and Applications Reservoir Engineering and Simulation Methods

Publication details

Journal / source
Scientific Data
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
Publication date
24 April 2026
Language
EN