Application of a particle swarm optimization algorithm for determining optimum well location and type

Computational Geosciences

Volumn 14, Issue 1, 2010, Pages 183-198

  Onwunalu, Jérôme E ^a   Durlofsky, Louis J ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Advanced well; Nonconventional well; Oil reservoir simulation; Particle swarm optimization; Production optimization; PSO; Subsurface flow

Indexed keywords

GAS FIELD; GENETIC ALGORITHM; HYDROCARBON RESERVOIR; OIL FIELD; SIMULATION; STOCHASTICITY; SUBSURFACE FLOW; WELL TECHNOLOGY;

EID: 77952880976     PISSN: 14200597     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10596-009-9142-1     Document Type: Article

References (41)

1. Alander, J. T.: On optimal population size of genetic algorithms. In: Proceedings of CompEuro '92 Computer Systems and Software Engineering, pp. 65-70 (1992).
2. Artus, V., Durlofsky, L. J., Onwunalu, J., Aziz, K.: Optimization of nonconventional wells under uncertainty using statistical proxies. Comput. Geosci. 10(4), 389-404 (2006).
3. Bangerth, W., Klie, H., Wheeler, M. F., Stoffa, P. L., Sen, M. K.: On optimization algorithms for the reservoir oil well placement problem. Comput. Geosci. 10, 303-319 (2006).
4. Bittencourt, A. C., Horne, R. N.: Reservoir development and design optimization. Paper SPE 38895 presented at the 1997 SPE Annual Technical Conference and Exhibition, San Antonio, 5-8 October 1997.
5. Braendler, D., Hendtlass, T.: The suitability of particle swarm optimisation for training neural hardware. In: International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE, pp. 190-199. Springer, New York (2002).
6. Brandstatter, B., Baumgartner, U.: Particle swarm optimization-mass-spring system analogon. IEEE Trans. Magn. 38, 97-1000 (2002).
7. Cao, H.: Development of techniques for general purpose research simulator. Ph. D. Thesis, Stanford University (2002).
8. Carlisle, A., Dozier, G.: An off-the-shelf PSO. In: Proceedings of Workshop on Particle Swarm Optimization, pp. 1-6, Indianapolis, 6-7 April 2001.
9. Chakrabarti, R., Chattopadhyay, P. K., Basu, M., Panigrahi, C. K.: Particle swarm optimization technique for dynamic economic dispatch. J. Inst. Eng. India 87, 48-54 (2006).
10. Clerc, M.: Back to random topology. Technical report, March 2007. http://www. clerc. maurice. fr/pso/ (2007).
11. Clerc, M.: Stagnation analysis in particle swarm optimisation or what happens when nothing happens. hal-00122031, version 1-23 Dec 2006. http://hal. archives-ouvertes. fr/hal-00122031/ (2006).
12. Clerc, M.: Particle Swarm Optimization. iSTE, London (2006).
13. Clerc, M.: Standard PSO 2007. http://www. particleswarm. info (2007).
14. Deb, K.: Multi-objective optimization using evolutionary algorithms, 1st edn. Wiley, West Sussex (2001).
15. Eberhardt, R. C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micromachine and Human Science, pp. 39-43 (1995).
16. Schlumberger Eclipse: 2004. Schedule User Guide 2004A, Chapter 6: Technical description.
17. Engelbrecht, A. P.: Fundamentals of Computational Swarm Intelligence. Wiley, West Sussex (2005).
18. Farshi, M. M.: Improving genetic algorithms for optimum well placement. Master's Thesis, Stanford University (2008).
19. Franstrom, K. L., Litvak, M. L.: Automatic simulation algorithm for appraisal of future infill development potential of Prudhoe Bay. Paper SPE 59374 presented at the 2000 SPE/DOE Improved Oil Recovery Symposium, Tulsa, 3-5 April 2000.
20. Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (2004).
21. Gong, T., Tuson, A. L.: Particle swarm optimization for quadratic assignment problems-a forma analysis approach. Int. J. Comput. Intell. Res. 4, 177-185 (2008).
22. Guyaguler, B., Horne, R. N.: Uncertainty assessment of well placement optimization. SPE Reservoir Evaluation & Engineering, pp. 24-32 (2004).
23. Haupt, R. L., Haupt, S. E.: Practical Genetic Algorithms, 2nd edn. Wiley, New York (2004).
24. Helwig, S., Wanka, R.: Theoretical analysis of initial particle swarm behavior. In: Proceedings of the 10th International Conference on Parallel Problem Solving from Nature (PPSN08), pp. 889-898. Springer, Dortmund (2008).
25. Jiang, Y.: Techniques for modelling complex reservoirs and advanced wells. Ph. D. Thesis, Stanford University (2007).
26. Kennedy, J., Mendes, R.: Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms. In: Proceedings of the IEEE International Workshop on Soft Computing in Industrial Applications, pp. 45-50. IEEE, New York (2003).
27. Kennedy, J., Eberhardt, R. C.: Particle swarm optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1942-1947. IEEE, Piscataway (1995).
28. Litvak, M., Gane, B., Williams, G., Mansfield, M., Angert, P., Macdonald, C., McMurray, L., Skinner, R., Walker, G. J.: Field development optimization technology. Paper SPE 106426 presented at the 2007 SPE Reservoir Simulation Symposium, Houston, 26-28 February 2007.
29. Mattot, L. S., Rabideau, A. J., Craig J. R.: Pump-and-treat optimization using analytic element method flow models. Adv. Water Resour. 29, 760-775 (2006).
30. Miranda, V., Keko, H., Duque, A. J.: Stochastic star communication topology in evolutionary particle swarms (epso). Int. J. Comput. Intell. Res. 4(2), 105-116 (2008).
31. Onwunalu, J.: Optimization of nonconventional well placement using genetic algorithms and statistical proxy. Master's Thesis, Stanford University (2006).
32. Sarma, P., Chen, W. H.: Efficient well placement optimization with gradient-based algorithms and adjoint models. Paper SPE 112257 presented at the 2008 SPE Intelligent Energy Conference and Exhibition, Amsterdam, 25-27 February 2008.
33. Shi, Y., Eberhardt, R. C.: A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 69-73. IEEE, New York (1998).
34. Spall, J. C.: An overview of the simultaneous perturbation method for efficient optimization. John Hopkins APL Tech. Dig. 19(4), 482-492 (1998).
35. Tupac, Y. J., Faletti, L., Pacheco, M. A. C., Vellasco, M. M. B. R.: Evolutionary optimization of oil field development. Paper SPE 107552 presented at the 2007 SPE Digital Energy Conference and Exhibition, Houston, 11-12 April 2007.
36. Wang, C., Li, G., Reynolds, A. C.: Optimal well placement for production optimization. Paper SPE 111154 presented at the 2007 SPE Eastern Regional Meeting, Lexington, 11-14 October 2007.
37. Williams, G. J. J., Mansfield, M., Macdonald, D. G., Bush, M. D.: Top-down reservoir modeling. Paper SPE 89974 presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, 26-29 September 2004.
38. Yeten, B.: Optimum deployment of nonconventional wells. Ph. D. thesis, Stanford University (2003).
39. Yeten, B., Durlofsky, L. J., Aziz, K.: Optimization of nonconventional well type, location and trajectory. SPE J. 8, 200-210 (2003).
40. Zandvliet, M. J., Handels, M., van Essen, G. M., Brouwer, D. R., Jansen, J. D.: Adjoint-based well-placement optimization under production constraints. SPE J. 13(4), 392-399 (2008).
41. Zhang, W. J., Xie, X. F., Bi, D. C.: Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: IEEE Congress on Evolutionary Computation, vol. 2, pp. 2307-2311. IEEE, Piscataway (2004).