论文信息 - Classification of Petroleum Well Drilling Operations with a Hybrid Particle Swarm/Ant Colony Algorithm

Classification of Petroleum Well Drilling Operations with a Hybrid Particle Swarm/Ant Colony Algorithm

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.

José Ricardo Pelaquim Mendes | Adriane Beatriz de Souza Serapião

[1] Alex Alves Freitas,et al. Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[2] Alex Alves Freitas,et al. A hybrid PSO/ACO algorithm for classification , 2007, GECCO '07.

[3] José Ricardo Pelaquim Mendes,et al. Artificial Immune Systems for Classification of Petroleum Well Drilling Operations , 2007, ICARIS.

[4] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[5] Celso Kazuyuki Morooka,et al. AUTOMATED CLASSIFICATION SYSTEM FOR PETROLEUM WELL DRILLING USING MUD-LOGGING DATA , 2005 .

[6] Alex Alves Freitas,et al. A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[7] Alex A. Freitas,et al. HIERARCHICAL CLASSIFICATION OF G-PROTEIN-COUPLED RECEPTORS WITH A PSO/ACO ALGORITHM , 2006 .

[8] Tiago Ferra de Sousa,et al. Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..

[9] Age Kyllingstad,et al. Factors Limiting the Quantitative Use of Mud-Logging Data , 1993 .

[10] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .