Bangerth, W., Klie, H., Wheeler, M.F., Stoffa, P.L. and Sen, M.K. (2006) On Optimization Algorithms for the Reservoir Oil Well Placement Problem. Computational Geosciences, 10, 303-319. - References

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Bangerth, W., Klie, H., Wheeler, M.F., Stoffa, P.L. and Sen, M.K. (2006) On Optimization Algorithms for the Reservoir Oil Well Placement Problem. Computational Geosciences, 10, 303-319. http://dx.doi.org/10.1007/s10596-006-9025-7

has been cited by the following article:

TITLE: A Computational Comparison between Optimization Techniques for Wells Placement Problem: Mathematical Formulations, Genetic Algorithms and Very Fast Simulated Annealing

AUTHORS: Ghazi D. AlQahtani, Ahmed Alzahabi, Timothy Spinner, Mohamed Y. Soliman

KEYWORDS: Wells Placement Optimization Integer Programming Simulated Annealing Genetic Algorithm

JOURNAL NAME: Journal of Materials Science and Chemical Engineering, Vol.2 No.10, October 28, 2014

ABSTRACT: This study considers several computational techniques for solving one formulation of the wells placement problem (WPP). Usually the wells placement problem is tackled through the combined efforts of many teams using conventional approaches, which include gathering seismic data, conducting real-time surveys, and performing production interpretations in order to define the sweet spots. This work considers one formulation of the wells placement problem in heterogeneous reservoirs with constraints on inter-well spacing. The performance of three different types of algorithms for optimizing the well placement problem is compared. These three techniques are: genetic algorithm, simulated annealing, and mixed integer programming (IP). Example case studies show that integer programming is the best approach in terms of reaching the global optimum. However, in many cases, the other approaches can often reach a close to optimal solution with much more computational efficiency.