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TECHNICAL PAPERS

Optimization of Well Placement

[+] Author and Article Information
Baris Guyaguler, Roland Horne

Department of Petroleum Engineering, Stanford University, 65 Green Earth Sciences Building, Stanford, CA 74305

J. Energy Resour. Technol 122(2), 64-70 (Oct 28, 1999) (7 pages) doi:10.1115/1.483164 History: Received October 28, 1999
Copyright © 2000 by ASME
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References

Rogers,  L. L., and Dowla,  F. U., 1994, “Optimal Groundwater Remediation Using Artificial Neural Networks With Parallel Solute Transport,” Water Resour. Res., 30, No. 2, pp. 458–481.
Centilmen, A., Ertekin, T., and Gradér, A. S., 1999, “Applications of Neural Networks in Multiwell Field Development,” SPE 56433, presented at SPE Annual Technical Conference and Exhibition, Houston, TX, October 3–6.
Bittencourt, A. C., and Horne, R. N., 1997, “Reservoir Development and Design Optimization,” SPE 38895 presented at SPE Annual Technical Conference and Exhibition, San Antonio, TX, October 5–8.
Pan, Y., and Horne, R. N., 1998, “Improved Methods for Multivariate Optimization of Field Development Scheduling and Well Placement Design,” SPE 49055 presented at the 1998 SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 27–30; also, J. P. Technol. Dec.
Goldberg, D. E., 1989, Genetic Algorithm in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, PA.
Michalewicz, Z., 1992, Genetic Algorithms+Data Structures=Evolution Programs, Springer-Verlag, Berlin Heidelberg, Germany.
Gill, P. E., Murray, W., and Wright, M. H., 1981, Practical Optimization, Academic, San Diego, CA.
Nelder,  J. A., and Mead,  R., 1965, “A Simplex Method for Function Minimization,” Comput. J. (UK), 7, pp. 308–313.
Wackernagel, H., 1998, Multivariate Geostatistics, Springer-Verlag, Berlin Heidelberg, Germany.
Chiles, J. P., and Delfiner, P., 1999, Geostatistics, Modeling Spatial Uncertainty, Wiley, New York, NY.

Figures

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A polytope in two-dimensional space; triangles are the data points, circles are generated by the polytope algorithm
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A two-dimensional parameter space with irregularly spaced data (black) and a location where value is to be estimated (white)
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Cumulative oil production surface for the single-production well placement problem, obtained by exhaustive simulation (Run 4)
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Cumulative oil production surface for the single injection well placement problem, obtained by exhaustive simulation (Run 5)
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Permeability distribution for Run 3; highly permeable regions have 100 md permeability (white), background has 20 md permeability (gray)
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32×32 model permeability distribution for Runs 4 and 5
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Effect of population size on algorithm performance for the production well placement problem (Run 4)
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Effect of population size on algorithm performance for the injection well placement problem (Run 5)

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