CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data

by Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 2
Year of Publication: 2013
Authors: Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta
10.5120/12466-8834

Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta . Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data. International Journal of Computer Applications. 72, 2 ( June 2013), 23-35. DOI=10.5120/12466-8834

@article{ 10.5120/12466-8834,
author = { Chithra Chakra N C, Ki-young Song, Deoki N Saraf, Madan M Gupta },
title = { Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 2 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number2/12466-8834/ },
doi = { 10.5120/12466-8834 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:51.016674+05:30
%A Chithra Chakra N C
%A Ki-young Song
%A Deoki N Saraf
%A Madan M Gupta
%T Production Forecasting of Petroleum Reservoir applying Higher-Order Neural Networks (HONN) with Limited Reservoir Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 2
%P 23-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurate and reliable production forecasting is certainly a significant step for the management and planning of the petroleum reservoirs. This paper presents a new neural approach called higher-order neural network (HONN) to forecast the oil production of a petroleum reservoir. In HONN, the neural input variables are correlated linearly as well as nonlinearly, which overcomes the limitation of the conventional neural network. Hence, HONN is a promising technique for petroleum reservoir production forecasting without sufficient network training data. A sandstone reservoir located in Gujarat, India was chosen for simulation studies, to prove the efficiency of HONNs in oil production forecasting with insufficient data available. In order to reduce noise in the measured data from the oil field a pre-processing procedure that consists of a low pass filter was used. Also an autocorrelation function (ACF) and cross-correlation function (CCF) was employed for selecting the optimal input variables. The results from these simulation studies show that the HONN models have enhanced forecasting capability with higher accuracy in the prediction of oil production.

References
  1. D. Tamhane, P. M. Wong, F. Aminzadeh, and M. Nikravesh, "Soft Computing for Intelligent Reservoir Characterization," in SPE Asia Pacific Conference on Integrated Modelling for Asset Management Yokohama, Japan, 2000, pp. 1-11.
  2. S. D. Mohaghegh, "Recent Developments in Application of Artificial Intelligence in Petroleum Engineering," Journal of Petroleum Technology, vol. 57, pp. 86-91, 2005.
  3. W. W. Weiss, R. S. Balch, and B. A. Stubbs, "How Artificial Intelligence Methods Can Forecast Oil Production," in SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma: Society of Petroleum Engineers, 2002, pp. 1-16.
  4. F. Aminzadeh, J. Barhen, C. W. Glover, and N. B. Toomarian, "Reservoir Parameter Estimation Using a Hybrid Neural Network," Comput. Geosci. , vol. 26, pp. 869-875, 2000.
  5. A. U. Al-Kaabi and W. J. Lee, "Using Artificial Neural Networks To Identify the Well Test Interpretation Model " SPE Formation Evaluation, vol. 8, pp. 233-240, 1993.
  6. C. Maschio, C. P. V. de Carvalho, and D. J. Schiozer, "A New Methodology to Reduce Uncertainties in Reservoir Simulation Models Using Observed Data and Sampling Techniques," Journal of Petroleum Science and Engineering, vol. 72, pp. 110-119, 2010.
  7. W. A. Habiballah, R. A. Startzman, and M. A. Barrufet, "Use of Neural Networks for Prediction of Vapor/Liquid Equilibrium K-Values for Light-Hydrocarbon Mixtures," SPE Reservoir Engineering, vol. 11, pp. 121-126, 1996.
  8. S. M. Al-Fattah and R. A. Startzman, "Predicting Natural Gas Production Using Artificial Neural Network," in SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, Texas: Society of Petroleum Engineers, 2001, pp. 1-11.
  9. Y. C. Lee, G. Doolen, H. H. Chen, G. Z. Sun, T. Maxwell, H. Y. Lee, and C. L. Giles, "Machine Learning Using a Higher Order Correlation Network," Phys. D, vol. 2, pp. 276 - 306, 1986.
  10. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, MA: The MIT Press, 1986.
  11. C. L. Giles and T. Maxwell, "Learning, Invariance, and Generalization in High-Order Neural Networks," Applied Optics, vol. 26, pp. 4972-4978. , 1987.
  12. J. Gosh and Y. Shin, "Effcient Higher-Order Neural Networks For Classification And Function Approximation," International Journal of Neural Systems, vol. 3, pp. 323-350. , 1992.
  13. N. Homma and M. M. Gupta, "Superimposing Learning for Backpropagation Neural Networks," Bulletin of College of Medical Sciences, Tohoku University, vol. 11, pp. 253-259, 2002.
  14. M. M. Gupta, L. Jin, and N. Homma, Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, 1 ed. : Wiley - IEEE Press, 2003.
  15. S. K. Redlapalli, "Development of Neural Units with Higher-order Synaptic Operations and Their Applications to Logic Circuits and Control Poblems," in Department of Mechanical Engineering: University of Saskatchewan, Canada, 2004.
  16. K. -Y. Song, M. M. Gupta, and D. Jena, "Design of An Error-Based Robust Adaptive Controller," in Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on, 2009, pp. 2386-2390.
  17. M. K. Tiwari, K. Y. Song, C. Chatterjee, and M. M. Gupta, "River-flow Forecasting Using Higher-order Neural Networks," Journal of Hydrologic Engineering, vol. 17, pp. 1-12, 2012.
  18. Z. -G. Hou, K. -Y. Song, M. M. Gupta, and M. Tan, "Neural Units with Higher-Order Synaptic Operations for Robotic Image Processing Applications," Soft Comput. , vol. 11, pp. 221-228, 2006.
  19. M. M. Gupta, N. Homma, Z. -G. Hou, A. M. G. Solo, and I. Bukovsky, "Higher Order Neural Networks: Fundamentals Theroy and Appplications," in Artificial Higher-Order Neural Networks for Computer Science and Engineeing: Trends for emerging Applications, M. Zhang, Ed. : Information Science Reference, 2010, pp. 397-422.
  20. M. M. Gupta, "Correlative Type Higher-Order Neural Units with Applications," in Automation and Logistics, 2008. ICAL 2008. IEEE International Conference, 2008, pp. 715-718.
  21. M. M. Gupta and D. H. Rao, Neuro-Control Systems- Theory and Applications: IEEE Neural Networks Council, 1993.
  22. A. Azadeh, S. F. . Ghaderi, and S. Sohrabkhani, "Forcasting Electrical Consumption by Integration of Neural Network, Time Series and ANOVA," Applied Mathematics and Computation, 2006.
  23. S. W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing, 1997.
Index Terms

Computer Science
Information Sciences

Keywords

Production forecasting reservoir performance higher-order neural network higher-order synaptic operation

Powered by PhDFocusTM