research-article

Use of retrospective optimization for placement of oil wells under uncertainty

Authors:

Honggang Wang,

David Echeverría Ciaurri,

Louis J. DurlofskyAuthors Info & Claims

WSC '10: Proceedings of the Winter Simulation Conference

Pages 1750 - 1757

Published: 05 December 2010 Publication History

Get Access

Abstract

Determining well locations in oil reservoirs under geological uncertainty remains a challenging problem in field development. Well placement problems are integer optimization problems because a reservoir is discretized into grid blocks and the well locations are defined by block indices (i, j, k) in the discrete model. Reservoir simulators are used to evaluate reservoir production given a well placement. In the presence of reservoir uncertainty, we simulate multiple model realizations to estimate the expected field performance for a certain well placement. Most existing methods for well placement optimization problems are random-search based algorithms.

We present a retrospective optimization (RO) algorithm that uses Hooke-Jeeves search for well location optimization under uncertainty. The RO framework generates a sequence of sample-path problems with increasing sample sizes. Embedded in RO, the Hooke-Jeeves search solves each sample-path problem for a local optimizer given a discrete neighborhood definition. The numerical results show that the RO algorithm efficiently finds a solution yielding a 70% increase (compared to a solution suggested from heuristics) in the expected net present value (NPV) over 30 years of reservoir production for the problem considered.

References

[1]

Bangerth, W., H. Klie, M. F. Wheeler, P. L. Stoffa, and M. K. Sen. 2006. On optimization algorithms for the reservoir oil well placement problem. Computational Geosciences 10:303--319.

Crossref

Google Scholar

[2]

Chen, H., and B. W. Schmeiser. 2001. Stochastic root finding via retrospective approximation. IIE Transactions 33:259--275.

Crossref

Google Scholar

[3]

Hooke, R, and T. A. Jeeves. 1961. "Direct search" solution of numerical and statistical problems. Journal of the ACM 8:212--229.

Digital Library

Google Scholar

[4]

Jin, J., and B. W. Schmeiser. 2003. Simulation-based retrospective optimization of stochastic systems. In Proceedings of the 2003 Winter Simulation Conference. New Orleans, Louisiana.

Digital Library

Google Scholar

[5]

Kleywegt, A. J., A. Shapiro, and T. H. de Mello. 2001. The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization 12:479--502.

Digital Library

Google Scholar

[6]

Onwunalu, J. E., and L. J. Durlofsky. 2010. Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences 14:183--198.

Crossref

Google Scholar

[7]

Peters, E., R. J. Arts, G. K. Brouwer, and C. R. Geel. 2009. Results of the Brugge benchmark study for flooding optimization and history matching. In Paper SPE 119094 presented at the SPE Reservoir Simulation Symposium. The Woodlands, Texas.

Google Scholar

[8]

Sarma, P., and W. H. Chen. 2008. Efficient well placement optimization with gradient-based algorithms and adjoint models. In Paper SPE 112257 presented at the SPE Intelligent Energy Conference and Exhibition. Amsterdam, The Netherlands.

Google Scholar

[9]

Wang, H., and B. W. Schmeiser. 2008. Discrete stochastic optimization using linear interpolation. In Proceedings of the 2008 Winter Simulation Conference, ed. S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, and J. W. Fowler. Washington D. C., USA.

Digital Library

Google Scholar

[10]

Yeten, B. 2003. Optimum deployment of nonconventional wells. Ph.D. thesis, Stanford University.

Google Scholar

Cited By

View all

Wang HGong B(2015)Hierarchical stochastic modeling and optimization for petroleum field development under geological uncertaintyComputers and Industrial Engineering10.1016/j.cie.2014.11.00780:C(23-32)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.cie.2014.11.007
Wang HHill RKuhl M(2013)Mixed integer simulation optimization for petroleum field development under geological uncertaintyProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676117(1057-1067)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2676117

Index Terms

Use of retrospective optimization for placement of oil wells under uncertainty

Recommendations

Oil Well Placement Optimization Using Niche Particle Swarm Optimization
CIS '12: Proceedings of the 2012 Eighth International Conference on Computational Intelligence and Security

A challenging problem in oil field development project is the optimization of multi-wells placement because of the increasing in optimization variable quantities and searching space size. To overcome the limitation of traditional optimization with ...
Optimization of placement solutions for routability
DAC '13: Proceedings of the 50th Annual Design Automation Conference

Routability has become a critical issue in VLSI design flow. To avoid producing an unroutable design, many placers [4-7] invoke global routers to get a congestion map and then move cells to reduce congestion based on this map. However, as cells move, ...
Well placement optimization under geological statistical uncertainty
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

To control fluid flow in underground geologic formations, the placement of injection/production wells needs to be optimized taking geological characteristics of the reservoir into account. However, the optimum solution might not perform beneficially in ...

Comments

Information & Contributors

Information

Published In

WSC '10: Proceedings of the Winter Simulation Conference

December 2010

3519 pages

ISBN:9781424498642

Publisher

Winter Simulation Conference

Publication History

Published: 05 December 2010

Check for updates

Qualifiers

Research-article

Conference

WSC10

Sponsor:

IIE
INFORMS-SIM
SCS
SIGSIM

WSC10: Winter Simulation Conference

December 5 - 8, 2010

Maryland, Baltimore

Acceptance Rates

WSC '10 Paper Acceptance Rate 184 of 281 submissions, 65%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
42
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wang HGong B(2015)Hierarchical stochastic modeling and optimization for petroleum field development under geological uncertaintyComputers and Industrial Engineering10.1016/j.cie.2014.11.00780:C(23-32)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.cie.2014.11.007
Wang HHill RKuhl M(2013)Mixed integer simulation optimization for petroleum field development under geological uncertaintyProceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World10.5555/2675983.2676117(1057-1067)Online publication date: 8-Dec-2013
https://dl.acm.org/doi/10.5555/2675983.2676117

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Oil Well Placement Optimization Using Niche Particle Swarm Optimization

Optimization of placement solutions for routability

Well placement optimization under geological statistical uncertainty

Comments

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Oil Well Placement Optimization Using Niche Particle Swarm Optimization

Optimization of placement solutions for routability

Well placement optimization under geological statistical uncertainty

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations