Computational techniques for closed-loop reservoir modeling with application to a realistic reservoir

Petroleum Science and Technology

Volumn 26, Issue 10-11, 2008, Pages 1120-1140

  Sarma, P ^a,b,c   Durlofsky, L J ^a   Aziz, K ^a  

^a STANFORD UNIVERSITY   (United States)

^b CHEVRON ENERGY TECHNOLOGY COMPANY   (United States)

^c NONE   (United States)

Author keywords

Adjoint; E fields; Field of the future; History matching; I fields; Intelligent wells; Karhunen Loeve; Kernel principal component analysis; Optimization; Reservoir simulation; Smart fields; Uncertainty propagation

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; EARTH SCIENCES; FINANCIAL DATA PROCESSING; GEOLOGIC MODELS; HISTORY; INJECTION (OIL WELLS); OFFSHORE OIL WELL PRODUCTION; OPTIMIZATION; PETROLEUM DEPOSITS; PETROLEUM RESERVOIRS; PRINCIPAL COMPONENT ANALYSIS; PUBLIC KEY CRYPTOGRAPHY; RESERVOIR MANAGEMENT; RESERVOIRS (WATER); WELL FLOODING; WELL STIMULATION; WELLS;

(E ,3E) PROCESS; ADJOINT BASED OPTIMIZATION; APPLIED (CO); BOTTOM-HOLE; CLOSED LOOPS; CLOSED-LOOP RESERVOIR MODELING; COMPUTATIONAL PROCEDURES; COMPUTATIONAL TECHNIQUES; GENERAL (CO); GEOLOGICAL MODELING; GEOSTATISTICAL MODELLING; GRADIENT-BASED; GULF OF MEXICO (GOM); HISTORY MATCHING (HM); INJECTION RATES; INJECTION WELLS; KERNEL PRINCIPAL COMPONENT ANALYSIS (KPCA); MULTI POINTS; NET PRESENT VALUE (NPV); OPEN LOOPS; OPTIMIZATION ALGORITHMS; PATH CONSTRAINTS; PRODUCTION RATES; PRODUCTION WELLS; REAL-TIME MODELING; RESERVOIR PERFORMANCE; TIME STEPPING; WATER CUTS;

WATER INJECTION;

EID: 46149105719     PISSN: 10916466     EISSN: 15322459     Source Type: Journal    
DOI: 10.1080/10916460701829580     Document Type: Article

References (22)

1. Brouwer, D. R., and Jansen, J. D. (2004). Dynamic optimization of water flooding with smart wells using optimal control theory. SPE J. 9:391-402.
2. Bryson, A. E., and Ho, Y. C. (1975). Applied Optimal Control: Optimization, Estimation, & Control. New York: Taylor & Francis.
3. Faithi, Z., and Ramirez, W. F. (1987). Optimization of an enhanced oil recovery process with boundary controls - A large scale nonlinear maximization. Automation 23:301-310.
4. Gavalas, G. R., Shah, P. C., and Seinfeld, J. H. (1976). Reservoir history matching by Bayesian estimation. SPE J. 16:337-350.
5. Golub, G. H., and van Loan, C. F. (1996). Matrix Computations. Baltimore, MD: The Johns Hopkins Press.
6. Jansen, J. D., Brouwer, D. R., Naevdal, G., and van Kruijsdiik, C. P. J. W. (2005). Closed-loop reservoir management. First Break 23:43-48.
7. Liu, S. Q., Shi, J., Dong, J., and Wang, S. A. (2004). Modified penalty function method for inequality constraints minimization. Retrieved from http://www.mmm.muroran-it.ac.jp/shi/
8. Liu, W., and Ramirez, W. F. (1994). Optimal control of three-dimensional steam flooding process. J. Pet. Sci. Eng. 11:137-154.
9. Loeve, M. (1977). Probability Theory I and II. Fourth Edition. New York: Springer-Verlag.
10. Mehos, G., and Ramirez, W. F. (1989). Use of optimal control theory to optimize carbon dioxide miscible flooding enhanced oil recovery. J. Pet. Sci. Eng. 2:247-260.
11. Oliver, D. S. (1996). Multiple realizations of the permeability field from well test data. SPE J. 1:145-154.
12. Rao, S. S. 1978. Optimization: Theory and Applications. New York: John Wiley and Sons.
13. Reynolds, A. C., He, N., Chu, L., and Oliver, D. S. (1996). Reparameterization techniques for generating reservoir descriptions conditioned to variograms and well-test pressure data. SPE J. 1:413-426.
14. Sarma, P., Chen, W. H., Durlofsky, L. J., and Aziz, K. (2006a). Production optimization with adjoint models under nonlinear control-state path in-equality constraints. SPE Paper 99959. Presented at the SPE Intelligent Energy Conference and Exhibition, Amsterdam, April 11-13.
15. Sarma, P., Durlofsky, L. J., Aziz, K., and Chen, W. H. (2006b). Efficient real-time reservoir management using adjoint-based optimal control and model updating. Comput. Geosci. 10:3-36.
16. Sarma, P., Durlofsky, L. J., and Aziz, K. (2005). Efficient closed-loop production optimization under uncertainty. SPE Paper 94241. Presented at the SPE Europec/EAGE Annual Conference, Madrid, June 13-16.
17. Sarma, P., Durlofsky, L. J., and Aziz, K. (2008). Kernel principal component analysis for an efficient, differentiable parameterization of multi-point geostatistics. Math. Geo. Sci. 40:3-32.
18. Scholkopf, B., Smola, A., and Muller, K. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10:1299-1319.
19. Severns, W. (2006). Can technology turn the tide on decline? Keynote presentation at the SPE Intelligent Energy Conference and Exhibition, Amsterdam, April 11-13.
20. Strebelle, S. (2002). Conditional simulation of complex geological structures using multiple-point statistics. Math. Geol. 34:1-22.
21. Zakirov, I. S., Aanonsen, S. I., Zakirov, I. S., and Palatnik, B. M. (1996). Optimization of reservoir performance by automatic allocation of well rates. Presented at the 5th European Conference on the Mathematics of Oil Recovery. Leoben, Austria, September 3-5.
22. Zhang, T. (2006). Multiple point geostatistics: filter-based local training pattern classification for spatial pattern simulation, PhD thesis, Stanford University, Stanford, CA.