Optimization Algorithms Used in Reservoir History Matching

Article Preview

Abstract:

Numerical reservoir models are constructed from limited available static and dynamic data, and history matching is a process of changing model parameters to find a set of values that will yield a reservoir simulation prediction of data that matches the observed historical production data. To minimize the objective function involved in the history matching procedure, we need to apply the optimization algorithms. This paper is based on the optimization algorithms used in automatic history matching. Several optimization algorithms will be compared in this paper.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

614-618

Citation:

Online since:

August 2013

Export:

Price:

[1] Baosheng Liang, "A Singular Evolutive Interpolated Kalman Filter for Rapid Uncertainty Quantification," SPE Reservoir Simulation Symposium, 26-28 February 2007.

DOI: 10.2118/106170-ms

Google Scholar

[2] Roger Fletcher. "Practical Methods of Optimization," John Wiley& Sons. New York, second editon. 1987.

Google Scholar

[3] Y. Abacioglu, D. S. Oliver. And A.C. Reynolds. "Efficient history-matching using subspace vectors," In TUPREP Research Report 17 (May 22, 2000). Pages 69-90.

Google Scholar

[4] Fengjun Zhang, Jan Arild Skjervheim, D.S. Oliver, "Automatic History Matching in a Bayesian Framework, Example Applications," SPE Reservoir Evaluation & Engineering. June 2005, pp.214-223.

DOI: 10.2118/84461-pa

Google Scholar

[5] Owe Axelsson. "Iterative Solution Methods," Cambridge University Press. 40 West 20th Street. New York. NY 10011-4211, USA,1996.

Google Scholar

[6] Shuguang Wang, Guozhong Zhao, Luobin Xu. "Optimization for Automatic History Matching," Internaion Journal of Numerical Analysis and Modeling, volume 2, 2005, pages 131-137.

Google Scholar

[7] Wu, Z., Reynolds, A.C., and Oliver, D.S. "Conditioning Geostatistical Models to Two-Phase Production Data," Soc. Petrol. Eng. J., 3(2), 142-155, 1999.

DOI: 10.2118/56855-pa

Google Scholar

[8] Bi, Z., Oliver, D.S. and Reynolds, A.C. "Conditioning 3D Stochastic Channels to Pressure Data," Soc. Petrol. Eng. J., 5(4), 474-484, 2000.

DOI: 10.2118/67954-pa

Google Scholar

[9] Cullick, A.S., Johnson, D., and Shi, G. "Improved and More Rapid History Matching with a Nonlinear Proxy and Global Optimization," paper SPE 101933 presented at the SPE Annual Technical Conference and Exhibition, San Antonio, TX, 24-27 September, 2006.

DOI: 10.2118/101933-ms

Google Scholar

[10] Forsythe, G.E., Malcolm, M.A., and Moler, C.B. "Computer Methods for Mathematical Computations, " Prentice-Hall Inc., Englewood Cliffs, NJ, 1977.

DOI: 10.1177/058310247901100907

Google Scholar

[11] Jutila, H.A. and Goodwin, N.H. "Schedule Optimization to Complement Assisted History Matching and Prediction under Uncertainty," paper SPE 100253 presented at the SPE Europec/EAGE Annual Conference and Exhibition, Vienna, Austria, 12-15 June, 2006.

DOI: 10.2118/100253-ms

Google Scholar

[12] Makhlouf, E.M., Chen, W.H, Wasserman, M.L., and Seinfeld, J.H.: "A General History Matching Algorithm for Three-Phase, Three-Dimensional Petroleum Reservoirs," SPE Advanced Technology Series, 1(2), 83-92, 1993.

DOI: 10.2118/20383-pa

Google Scholar