An intelligent system for petroleum well drilling cutting analysis

Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems, ICAIS 2009

Volumn , Issue , 2009, Pages 37-42

  Marana, Aparecido N ^a   Chiachia, Giovani ^a   Guilherme, Ivan R ^a   Papa, João P ^a   Miura, Kazuo ^b   Ferreira, Marcus V D ^b   Torres, Francisco ^b  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^b PETROBRAS   (Brazil)

Author keywords

Cutting analysis; Optimum path forest; Petroleum well drilling monitoring

Indexed keywords

BAYESIAN CLASSIFIER; BOREHOLE WALL; CUTTING ANALYSIS; DATA ANALYSIS; MULTI-LAYER PERCEPTRONS; OPTIMUM-PATH FOREST; SHALE SHAKERS; SUPERVISED CLASSIFIERS;

ACOUSTIC GENERATORS; CLASSIFIERS; FEATURE EXTRACTION; IMAGE PROCESSING; INTELLIGENT SYSTEMS; MUD LOGGING; MULTILAYER NEURAL NETWORKS; OIL WELLS; PATTERN RECOGNITION SYSTEMS; PETROLEUM ANALYSIS; PETROLEUM INDUSTRY; SHALE; SUPPORT VECTOR MACHINES;

WELL DRILLING;

EID: 74549132870     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAIS.2009.16     Document Type: Conference Paper

References (27)

1. C. Allène, J. Y. Audibert, M. Couprie, J. Cousty, and R. Keriven, "Some links between min-cuts, optimal spanning forests and watersheds," in Mathematical Morphology and its Applications to Image and Signal Processing. 2007, pp. 253-264, MCT/INPE.
2. P. E. Hart nad D. G. Stork R. O. Duda, Pattern Classification (second edition), Wiley-Interscience, 2001.
3. D.K. Coelho, M. Roisenberg, P.J.F. Filho and C.M.C. Jacinto. Risk assessment of drilling and completion operations in petroleum wells using a monte carlo and a neural network approach, Proceedings of the 37th Conference on Winter simulation, pp. 1892-1897. ACM, 2005.
4. R. Collobert and S. Bengio. Links between perceptrons, mlps and svms. In Proceedings of the 21th International Conference on Machine learning, page 23, New York, NY, USA, 2004. ACM Press.
5. T. Cormen, C. Leiserson, and R. Rivest, Introduction to Algorithms, MIT, 1990.
6. K. Duan and S. S. Keerthi. Which is the best multiclass svm method? an empirical study. Multiple Classifier Systems, pages 278-285, 2005.
7. A.X. Falcão, J. Stolfi, and R.A. Lotufo, "The image foresting transform: Theory, algorithms, and applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 19-29, Jan 2004.
8. K. Frantiek, F. Jozef and L. Milan, Monitoring of drilling process with the application of acoustic signal, Acta Montanistica Slovaca, vol. 5, no. 3, pp. 237-240. Technical University of Kosice, Slovakia, 2000.
9. T.C. Fonseca, J.R.P. Mendes, A.B.S. Serapião and I.R. Guilherme, A Genetic Neuro-Model Reference Adaptive Controller for Petroleum Wells Drilling Operations, International Conference on Computational Intelligence for Modelling, pp. 3-3. IEEE Press, 2006.
10. A.B.S. Serapião, R.M. Tavares, J.R.P. Mendes and I.R. Guilherme, Classification of Petroleum Well Drilling Operations Using Support Vector Machine, International Conference on Computational Intelligence for Modelling, pp. 145-145. IEEE Press, 2006.
11. R.M. Haralick, Statistical and structural approaches to texture, Proceedings of IEEE, vol. 67, no. 5, pp. 786-804. IEEE Press, 1979.
12. M.T. Hayajneh, Monitoring Hole Quality in a Drilling Process Using a Fuzzy Subtractive Clustering-basedqa System Identification Method, Journal of Testing and Evaluation, vol. 35, no. 3, 2007.
13. S. Haykin. Neural networks: a comprehensive foundation. Prentice Hall, 1994.
14. L. I. Kuncheva. Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, 2004.
15. João Paulo Papa, Celso Tetsuo Nagase Suzuki, and Alexandre Xavier Falcão, LibOPF: A library for the design of optimum-path forest classifiers, 2008, Software version 1.0 available at http://www.ic.unicamp. br/∼afalcao/LibOPF.
16. C. C. Chang and C. J. Lin, LIBSVM: A Library for Support Vector Machines, 2001, Software available at url http://www.csie.ntu.edu.tw/ ̃cjlin/libsvm.
17. A. Rocha P.A.V. Miranda, A.X. Falcão and F.P.G. Bergo, "Object delineation by κ-connected components," EURASIP Journal on Advances in Signal Processing, vol. 2008, pp. 1-15, 2008.
18. H.O. Mcleod and M.J. Minarovic, Monitoring and analysis of cutting-packing procedures to explain well performance, Journal of petroleum technology, vol. 46, no. 10, pp. 878-883. Society of Petroleum Technology, Richardson, TX, ETATSUNIS, 1994.
19. S. Nissen, Implementation of a Fast Artificial Neural Network Library (FANN), 2003, Department of Computer Science University of Copenhagen (DIKU). Software available at http://leenissen.dk/fann/.
20. N. Panda, E. Y. Chang, and G. Wu. Concept boundary detection for speeding up svms. In Proceedings of the 23th International Conference on Machine learning, pages 681-688, New York, NY, USA, 2006. ACM Press.
21. J. Papa, A. Falcão, C. Suzuki, and N. Mascarenhas. A discrete approach for supervised pattern recognition. In 12th International Workshop on Combinatorial Image Analysis, volume 4958, pages 136-147. LNCS Springer Berlin/Heidelberg, 2008.
22. L. Reyzin and R. E. Schapire. How boosting the margin can also boost classifier complexity. In Proceedings of the 23th International Conference on Machine learning, pages 753-760, New York, NY, USA, 2006. ACM Press.
23. A.B.S. Serapião, J.R.P. Mendes and K. Miura, Artificial Immune Systems for Classification of Petroleum Well Drilling Operations, 6th International Conference on Artificial Immune Systems, vol. 4628, pp. 47-58. Springer, 2007.
24. B. Tang and D. Mazzoni. Multiclass Reduced-set Support Vector Machines. In Proceedings of the 23th International Conference on Machine learning, pages 921-928, New York, NY, USA, 2006. ACM Press.
25. R.M. Tavares, Petroleum Well Drilling Data Interpretation and Analysis, MSc. Dissertation - School of Mechanical Engineering, University of Campinas, avaliable in portuguese at http://libdigi.unicamp.br/document/? code=000434401, 2006.
26. B. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Workshop on Computational Learning Theory, pages 144-152, New York, NY, USA, 1992. ACM Press.
27. S. Yilmaz, C. Demircioglu and S. Akin, Application of artificial neural networks to optimum bit selection, Computers & Geosciences, vol. 28, no. 2, pp. 261-269. Elsevier, 2002.