Please use this identifier to cite or link to this item:
http://acervodigital.unesp.br/handle/11449/71234- Title:
- Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm
- Universidade Estadual Paulista (UNESP)
- Universidade Estadual de Campinas (UNICAMP)
- 0302-9743
- 1611-3349
- This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.
- 9-Nov-2009
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5579 LNAI, p. 301-310.
- 301-310
- Bio-inspired
- Colony algorithms
- Data sets
- Decision-tree algorithm
- Hybrid particles
- Rule induction
- Data mining
- Decision trees
- Intelligent systems
- Mud logging
- Oil wells
- Petroleum industry
- Well drilling
- http://dx.doi.org/10.1007/978-3-642-02568-6_31
- Acesso restrito
- outro
- http://repositorio.unesp.br/handle/11449/71234
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.