Copyright 2000, Society of Petroleum Engineers Inc.
This paper was prepared for presentation at the 2000 SPE Annual Technical Conference and
Exhibition held in Dallas, Texas, 1–4 October 2000.
This paper was selected for presentation by an SPE Program Committee following review of
information contained in an abstract submitted by the author(s). Contents of the paper, as
presented, have not been reviewed by the Society of Petroleum Engineers and are subject to
correction by the author(s). The material, as presented, does not necessarily reflect any
position of the Society of Petroleum Engineers, its officers, or members. Papers presented at
SPE meetings are subject to publication review by Editorial Committees of the Society of
Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper
for commercial purposes without the written consent of the Society of Petroleum Engineers is
prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300
words; illustrations may not be copied. The abstract must contain conspicuous
acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.
Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.
Determination of the location of new wells is a complex
problem that depends on reservoir and fluid properties, well
and surface equipment specifications, and economic criteria.
Various approaches have been proposed for this problem.
Among those, direct optimization using the simulator as the
evaluation function, although accurate, is in most cases
infeasible due to the number of simulations required.
In this study a hybrid optimization technique based on the
genetic algorithm (GA), polytope algorithm, kriging algorithm
and neural networks is proposed. Hybridization of the GA
with these helper methods introduces hill-climbing into the
stochastic search and also makes use of proxies created on the
fly. Performance of the technique was investigated on a set of
exhaustive simulations for the single well placement problem
and it was observed that the number of simulations required
was reduced significantly. This reduction in the number of
simulations reduced the computation time, enabling the use of
full-scale simulation for optimization even for this full-scale
field problem. It was also seen that the optimization technique
was able to avoid convergence to local maxima due to its
Optimal placement of up to four water injection wells was
studied for Pompano, an offshore field in the Gulf of Mexico.
Injection rate was also optimized. The net present value of the
waterflooding project was used as the objective function.
Profits and costs during the time period of the project were
taken into consideration.