A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

CIMCA 2006: International Conference on Computational Intelligence for Modelling, Control and Automation, Jointly with IAWTIC 2006: International Conference on Intelligent Agents Web Technologies ...

Volumn , Issue , 2006, Pages

  Fonseca, Tiago C ^a   Mendes, José Ricardo P ^a   Serapião, Adriane B S ^b   Guilherme, Ivan R ^b  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; MATHEMATICAL MODELS; OIL WELL DRILLING; PROBLEM SOLVING; ROBUST CONTROL;

PERFORMANCE EVALUATORS; RATE OF PENETRATION (ROP);

ADAPTIVE CONTROL SYSTEMS;

EID: 38849162361     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIMCA.2006.8     Document Type: Conference Paper

References (24)

1. C. Siqueira, Antes tarde do que sem contratos. (In Portuguese) Brasil Energia, 298, 2005, 26-28.
2. T. Unneland, M. Häuser, Real-Time Asset Management: From Vision to Engagement-An Operator's Experience, Proc. SPE Annual Technical Conference and Exhibition, Dallas, USA, 2005.
3. F.P. Iversen, E. Cayeux, E.W. Dvergsnes, J.E. Gravdal, E.H. Vefring, B. Mykletun, A. Torsvoll, S. Omdal, A. Merlo, Monitoring and Control of Drilling Utilizing Continuously Updated Process Models, Proc. IADC/SPE Drilling Conference, Miami, USA, 2005.
4. F.E. Dupriest, J.W. Witt, S.M. Remmert, Maximizing ROP With Real-Time Analysis of Digital Data and MSE, SPE 10607-MS, Proc. International Petroleum Technology Conference, Doha, Qatar, 2005.
5. S. Lapierre, G. Courville, J. Song, Achieving Technical Limits: Expanded Application of Real-Time Pressure-While-Drilling Data Helps Optimize ROP and Hole Cleaning in LargeDiameter, Directional Intervals, SPE 99142-MS, Proc. IADC/SPE Drilling Conference, Miami, USA, 2006.
6. A.T. Bourgoyne Jr., F.S. Young Jr., A multiple regression approach to optimal drilling and abnormal detection, Society of Petroleum Engineers Journal, 14(4), 1974, 371-384.
7. R.V. Barragan, Otimização dos parâmetros mecânicos nas brocas para obter o custo mínimo de uma fase de um poço, (In Portuguese) Masters Dissertation, Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas. Campinas, Brazil, 1995.
8. S. Mohaghegh, R. Arefi, S. Ameri, K. Aminiand, R. Nutter, Petroleum reservoir characterization with the aid of artificial neural networks, Journal of Petroleum Sciences and Engineering, 16(4), 1996, 263- 274.
9. D.K. Coelho, M. Roisenberg, P.J. Freitas Filho, C.M.C Jacinto, Risk Assessment of Drilling and Completion Operations in Petroleum Wells Using a Monte Carlo and a Neural Network Approach, Proc. Winter Simulation Conference, Orlando, USA, 2005.
10. 1999 D. Dashevskiy, V. Dubinsky, J.D. Macpherson, Application of neural networks for predictive control in drilling dynamics. Proc. SPE Annual Technical Conference and Exhibition, Houston, USA, 1999.
11. M. Norgaard, O. Ravn, N.K. Poulsen, L.K. Hansen, Neural networks for modeling and control of dynamic systems: a practitioner's handbook (London, UK: Springer, 2000).
12. H.I. Bilgesu, L.T. Tetrick, U. Altmis, S. Mohaghegh, S. Ameri, A new approach for the prediction of rate of penetration (ROP) values, Proc. SPE Eastern Regional Meeting, Lexington, USA, 1997.
13. Application of ARX Neural Networks to Model the Rate of Penetration of Petroleum Wells Drilling, submitted for presentation at The LASTED International Conference on Computational Intelligence, San Francisco, USA, 2006.
14. Y. Li, Y. Li, G. Liu, Oil-Pumping System Control Using Nonlinear Homotopy BP Neural Network and Genetic Algorithm, Proc. 2005 IEEE Conference on Control Applications, Toronto, Canada, 2005.
15. th Asiolmar Conference on Signals, Systems and Computer, Pacific Grove, USA, 1990.
16. R. Dimeo, K.Y. Lee, Boiler-Turbine Control System Design Using a Genetic Algorithm, LEEE Transactions on Energy Conversion, 10(4), 1995, 752-759.
17. Y. Malachi, S. Singer, A Genetic Algorithm for the Corrective Control of Voltage and Reactive Power, IEEE Transactions on Power Systems, 21(1), 2006, 295-300.
18. X. Sun, K.K. Mohanty, Estimation of Flow Functions During Drainage Using Genetic Algorithm, SPE 84548-MS, Proc. SPE Annual Technical Conference and Exhibition, Denver, USA, 2003.
19. M. Tavakkolian, F. Jalali F., M.A. Emadi, Production Optimization using Genetic Algorithm Approach, SPE 88901-MS, Proc. Nigeria Annual International Conference and Exhibition, Abuja, Nigeria, 2004.
20. S. Haykin, Neural networks: a comprehensive foundation (Upper Saddle River, USA: Prentice-Hall, 1999).
21. D. Marquardt, An algorithm for least-squares estimations of nonlinear parameters, SIAM Journal on Applied Mathematics, 11(2), 1963, 431-441.
22. K.J. Astrom, B. Wittenmark, Adaptive Control (Reading, USA: Addison-Wesley, 1989).
23. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Reading, USA: Addison-Wesley, 1989).
24. A.F. Siegel, Statistics and Data Analysis (New York, USA: John Wiley & Sons, 1988).