Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

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. Abstract 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 stochastic nature. 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.