An improved ant colony algorithm-based ANN for bottom hole pressure prediction in underbalanced drilling

Petroleum Science and Technology

Volumn 30, Issue 13, 2012, Pages 1307-1316

  Nasimi, R ^a   Irani, R ^a   Moradi, B ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b Iranian Central Oil Fields Company   (Iran)

Author keywords

ANN; ant colony algorithm; back propagation; bottom hole circulating pressure; two phase fluid; underbalanced drilling

Indexed keywords

ANN; ANT COLONY ALGORITHMS; CIRCULATING PRESSURE; TWO PHASE FLUID; UNDER-BALANCED DRILLING;

BACKPROPAGATION ALGORITHMS; BOTTOM HOLE PRESSURE; NEURAL NETWORKS; OPTIMAL SYSTEMS; OPTIMIZATION;

FORECASTING;

EID: 84861555070     PISSN: 10916466     EISSN: 15322459     Source Type: Journal    
DOI: 10.1080/10916466.2010.497790     Document Type: Article

References (20)

1. Bhadeshia, H. K. D. H. (1999). Neural networks in materials science. ISIJ Int. 39:966.
2. Colorni, A., Dorigo, M., and Maniezzo, V. (1992). Distributed optimization by ant colonies. Proc. First European Conf. on Artificial Life 34-142.
3. Coulibaly, P., and Baldwin, C. K. (2005). Nonstationary hydrological time series forecasting using nonlinear dynamic methods. J. Hydrol. 307:164-174. (Pubitemid 40743124)
4. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst., Man, Cybern. B 26:29-41. (Pubitemid 126780573)
5. Dreo, J., and Siarry, P. (2002). A new ant colony algorithm using the heterarchical concept aimed at optimization of multi-minima continuous functions. Proc. Third Int. Workshop on Ant Algorithms 216-221.
6. Gambardella, L. M., and Dorigo, M. (1996). Solving symmetric and asymmetric TSPs by ant colonies. Proc. IEEE Conf. on Evolutionary Computation 622-627.
7. Gutjahr, W. J. (2000). A graph-based ant system and its convergence. Fut. Gen. Comput. Sys. 16:837-888.
8. Hassibi, B., and Stork, D. G. (1993). Second derivatives for network pruning: Optimal brain surgeon. In: Advances in Neural Information Processing Systems. Proceedings of the 1992 Conference, Hanson, S. J., Cowan, J. D., and Giles, C. L. (Eds.). Cambridge, MA: MIT Press, pp. 164-171.
9. Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2:359-366. (Pubitemid 20609008)
10. James, M., and Marcus, R. (2002). Anti-pheromone as a tool for better exploration of search space. Proc. Third Int. Workshop on Ant Algorithms 100-110.
11. Lin, Y. C., Zhang, J., and Zhong, J. (2008). Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel. Comput. Mater. Sci. 43:752-758.
12. Lockhart, R.W. and Martinelli, R. C. (1949). Proposed correlation of data for isothermal two-phase, two-component flow in pipes. Chem. Eng. Prog. 45:39-48.
13. Mandal, S., Sivaprasad, P. V., and Dube, R. K. (2007). Modeling microstructural evolution during dynamic recrystallization of alloy D9 using artificial neural network. J. Mater. Eng. Perform. 16:672-679. (Pubitemid 47587421)
14. Mohammadi, M. (2006). A comprehensive neural network model for predicting two-phase liquid holdup under various angles of pipe inclinations. Canadian International Petroleum Confer- ence, Calgary, Alberta, Canada, June 13-15.
15. Nash, J. E., and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models I: A discussion of principles. J. Hydrol. 10:282-290.
16. Ozbayoglu, E. M., Miska, S. Z., Reed, T., and Takach, N. (2002). Analysis of bed height in horizontal and highly-inclined wellbores by using artificial neural networks, SPE International Thermal Operations and Heavy Oil Symposium and International Horizontal Well Technology Conference, Calgary, Alberta, Canada, November 4-7.
17. Ozbayoglu, E. M., and Ozbayoglu, M. A. (2007). Flow pattern and frictional-pressure-loss estimation using neural networks for UBD operations. IADC/SPE Managed Pressure Drilling & Underbalanced Operations, Galveston, TX, March 28-29.
18. Ross, C. R. (1995). A simplified computer model for volumetric flow requirements of aerated muds in vertical wellbores. M.S. Thesis, Tulsa, OK: University of Tulsa.
19. Silpngarmlers, N., Guler, B., and Ertekin, T. (2002). Development and testing of two-phase relative permeability predictors using artificial neural networks. SPE J. 7:299-308.
20. Wallis, G. B. (1969). One-dimensional two-phase flow. New York: McGraw-Hill.