Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Engineering Applications of Artificial Intelligence

Volumn 24, Issue 1, 2011, Pages 201-207

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

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^b PETROBRAS   (Brazil)

Author keywords

Applied artificial intelligence; Artificial Neural Networks; Optimum path forest; Petroleum well drilling; Support vector machines

Indexed keywords

APPLIED ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; OPTIMUM-PATH FOREST; PETROLEUM WELL DRILLING; SUPPORT VECTOR;

COMPUTER VISION; CUTTING; IMAGE ANALYSIS; NEURAL NETWORKS; OIL WELLS; PETROLEUM ANALYSIS; PETROLEUM INDUSTRY; SUPPORT VECTOR MACHINES;

WELL DRILLING;

EID: 78649648348     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2010.04.002     Document Type: Article

References (32)

1. Allne, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R., 2007. Some links between min-cuts, optimal spanning forests and watersheds. In: Mathematical Morphology and its Applications to Image and Signal Processing, pp. 253264.
2. Boser, B.E., Guyon, I.M., Vapnik, V.N., 1992. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Workshop on Computational Learning Theory. ACM Press, New York, NY, USA, pp. 144152.
3. Chang, C.C., Lin, C.J., 2001. LIBSVM: a library for support vector machines. Software available at url: 〈 http://www.csie.ntu.edu.tw/ ∼cjlin/libsvm 〉.
4. Coelho, D.K., Roisenberg, M., DeFreitasFilho, P.J., Jacinto, C.M.C., 2005. Risk assessment of drilling and completion operations in petroleum wells using a monte carlo and a neural network approach. In: WSC'05: Proceedings of the 37th Conference on Winter simulation, Winter Simulation Conference, pp.1892-1897.
5. Collobert, R., Bengio, S., 2004. Links between perceptrons, mlps and svms. In: Proceedings of the 21th International Conference on Machine Learning. ACM Press, New York, NY, USA
6. T. Cormen, C. Leiserson, and R. Rivest Introduction to Algorithms 1990 MIT, Cambridge, UK
7. C. Cortes, and V. Vapnik Support vector networks Machine Learning 1995 Kluwer Academic Publishers, Hingham, MA, USA 273 297
8. K. Duan, and S.S. Keerthi Which is the best multiclass SVM method? An empirical study Multiple Classifier Systems 2005 278 285
9. A.X. Falco, J. Stolfi, and R.A. Lotufo The image foresting transform: theory, algorithms, and applications IEEE Transactions on PAMI 26 1 2004 19 29
10. Fonseca, T.C., Mendes, J.R.P., Serapiao, A.B.S., Guilherme, I.R., 2006. A genetic neuro-model reference adaptive controller for petroleum wells drilling operations. In: CIMCA '06: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce. IEEE Computer Society, Washington, DC, USA, p. 3.
11. K. Frantiek, F. Jozef, and L. Milan Monitoring of drilling process with the application of acoustic signal Acta Montanistica Slovaca 5 3 2000 237 240
12. Grigorescu, S.D., Cepisca, C., Ghita, O.M., Potarniche, I., Grigorescu, E., 2008. Intelligent control system for monitoring drilling process. In: CIMMACS'08: Proceedings of the Seventh WSEAS International Conference on Computational Intelligence, ManMachine Systems and Cybernetics, Stevens Point, Wisconsin, USA, 2008. World Scientific and Engineering Academy and Society (WSEAS), Cairo, Egypt, pp. 8893.
13. R.M. Haralick Statistical and structural approaches to texture Proceedings of IEEE 67 5 1979 786 804
14. M.T. Hayajneh Monitoring hole quality in a drilling process using a fuzzy subtractive clustering-based system identification method Journal of Testing and Evaluation 35 3 2007
15. S. Haykin Neural Networks: A Comprehensive Foundation 1994 Prentice-Hall, Upper Saddle River, NJ, USA
16. L.I. Kuncheva Combining Pattern Classifiers: Methods and Algorithms 2004 Wiley-Interscience, Hoboken, NJ, USA
17. Marana, A.N., Chiachia, G., Guilherme, I.R., Papa, J.P., Miura, K., Ferreira, M.V.D., Torres, F., 2009. An intelligent system for petroleum well drilling cutting analysis. In: Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems. IEEE Computer Society, Los Alamitos, CA, USA, pp. 3742.
18. H.O. Mcleod, and M.J. Minarovic Monitoring and analysis of cutting-packing procedures to explain well performance Journal of Petroleum Technology 46 10 1994 878 883
19. Nissen, S., 2003. Implementation of a fast artificial neural network library (FANN). Department of Computer Science University of Copenhagen (DIKU). Software available at: 〈 http://leenissen.dk/fann/ 〉.
20. Panda, N., Chang, E.Y., Wu, G., 2006. Concept boundary detection for speeding up svms. In: Proceedings of the 23th International Conference on Machine Learning. ACM Press, New York, NY, USA, pp. 681688.
21. Papa, J.P., Suzuki, C.T.N., Falco, A.X., 2009a. LibOPF: a library for the design of optimum-path forest classifiers. Software version 2.0 available at: 〈 http://www.ic.unicamp.br/∼afalcao/libopf 〉.
22. J.P. Papa, A.X. Falco, and C.T.N. Suzuki Supervised pattern classification based on optimum-path forest International Journal of Imaging Systems and Technology 19 2009 120 131
23. Reyzin, L., Schapire, R.E., 2006. How boosting the margin can also boost classifier complexity. In: Proceedings of the 23th International Conference on Machine Learning. ACM Press, New York, NY, USA, pp. 753760.
24. A. Rocha, P.A.V. Miranda, A.X. Falco, and F.P.G. Bergo Object delineation by κ -connected components EURASIP Journal on Advances in Signal Processing 2008 2008 1 14
25. B. Schlkopf, and A.J. Smola Learning with Kernels 2002 MIT Press Cambridge, MA
26. Serapiao, A.B.S., Tavares, R.M., Mendes, J.R.P., Guilherme, I.R., 2006. Classification of petroleum well drilling operations using support vector machine (svm). In: CIMCA '06: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce. IEEE Computer Society, Washington, DC, USA, p. 145.
27. Serapio, A.B.S., Mendes, J.R.P., Miura, K., 2007. Artificial immune systems for classification of petroleum well drilling operations. In: Sixth International Conference on Artificial Immune Systems. Lecture Notes in Computer Science, vol. 4628, Springer, Santos, Sp, Brazil, pp. 4758.
28. Serapio, A.B.S., Mendes, J.R.P., 2009. Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm. In: IEA/AIE '09: Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer-Verlag, Berlin, Heidelberg, pp. 301310.
29. Tang, B., Mazzoni, D., 2006. Multiclass reduced-set support vector machines. In: Proceedings of the 23th International Conference on Machine Learning. ACM Press, Pittsburgh, PA, USA, pp. 921928.
30. Tavares, R.M., 2006. Petroleum well drilling data interpretation and analysis. M.Sc. DissertationSchool of Mechanical Engineering, University of Campinas. Available in portuguese at: 〈 http://libdigi.unicamp.br/document/ ?code=000434401 〉.
31. V.N. Vapnik An overview of statistical learning theory IEEE Transactions on Neural Networks 10 5 1999 988 999
32. S. Yilmaz, C. Demircioglu, and S. Akin Application of artificial neural networks to optimum bit selection Computers & Geosciences 28 2 2002 261 269