Optimal model-based reservoir management with model parameter uncertainty updates

2011 International Symposium on Advanced Control of Industrial Processes, ADCONIP 2011

Volumn , Issue , 2011, Pages 439-444

  Chen, Yingying ^a   Hoo, Karlene A ^b  

^a Department of Electrical and Computer Engineering   (United States)

^b TEXAS TECH UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX RESERVOIRS; MARKOV CHAIN MONTE CARLO METHOD; MODEL PARAMETERS; MODEL-BASED CONTROL; OIL PRODUCTION; OPTIMAL CONTROLS; PARTIAL LEAST SQUARE REGRESSION; REDUCED ORDER MODELS; SYSTEM STATE; UNCERTAIN PARAMETERS;

CHAOTIC SYSTEMS; COMPUTER SIMULATION; MARKOV PROCESSES; MONTE CARLO METHODS; OPTIMIZATION; PETROLEUM RESERVOIR ENGINEERING; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; RESERVOIRS (WATER); UNCERTAINTY ANALYSIS;

RESERVOIR MANAGEMENT;

EID: 79960687156     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. P. Sarma, K. Aziz, and L. J. Durlofsky, "Implementation of adjoint solution for optimal control of smart wells," in Proc. Reservoir Symposium. Soc. Pet. Eng., 2005, Houston, TX, SPE 92864.
2. G. Nævdal, D. R. Brouwer, and J.-D. Jansen, "Waterflooding using closed-loop control," Computational Geosciences, vol. 10, pp. 37-60, 2006.
3. J. F. M. van Doren, R. Markovinović, and J.-D. Jansen, "Reduced-order optimal control of water flooding using proper orthogonal decomposition," Computational Geosciences, vol. 10, pp. 137-158, 2006.
4. P. Sarma, L. J. Durlofsky, and K. Aziz, "Efficient closed-loop production optimization under uncertainty," in Proc. 14th European Biennial Conference. Society of Petoleum Engineers, 2005, madrid, Spain, SPE 94241.
5. P. Sarma, L. Durlofsky, K. Aziz, andW. Chen, "Efficient real-time reservoir management using adjoint-based optimal control and model updating," Computational Geosciences, vol. 10, pp. 3-36, 2006.
6. D. R. Brouwer and J. D. Jansen, "Dynamic optimization of water flooding with smart wells using optimal control theory," in Proc. 13th European Petroleum Conference. Society of Petoleum Engineers, 2002, aberdeen, Scotland, U.K., SPE 78278.
7. H. Haario, E. Saksman, and J. Tamminen, "An adaptive metropolis algorithm," Bernoulli, vol. 7, pp. 223-242, 2001.
8. Y. Chen and K. Hoo, "Uncertainty propagation for effective reduced-order model generation," Computers and Chemical Engineering, vol. 34, pp. 1597-1605, 2010.
9. L. Marshall, D. Nott, and A. Sharma, "A comparative study of Markov chain Mote Carlo methods for conceptual rainfallrunoff modeling," Water Resources Research, vol. 40, 2004, w02501, doi:10.1029/2003WR002378.
10. A. Gelman, G. O. Roberts, and W. R. Gilks, "Efficient Metropolis jumping rules," Bayesian Statistics, vol. 5, pp. 599-607, 1996.
11. H. Abdi, Partial Least Squares (PLS) Regression. Thousand Oaks (CA): Sage, 2003, pp. 792-795.
12. Y. Chen and K. Hoo, "Uncertainty propagation for efficient model-based control solutions," in American Control Conference, Baltimore, Maryland, June & July 2010.
13. A. J. Newman, "Model reduction via the Karhunen-Lòeve expansion Part I: An exposition technical report t.r. 96-32," University of Maryland, College Park,MD, Tech. Rep., 1996.
14. M. Latil, Enhanced Oil Recovery, editions technip ed. Paris, French: Gulf Publishing Co, 1980.
15. B. Hancock and B. J. Skinner, The Oxford Companion to the Earth. Permeability and porosity. Oxford University Press, 2000.