Improved Methods for Multivariate Optimization of Field Development Scheduling and Well Placement Design

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 Optimization of reservoir development requires many evaluations of the possible combinations of the decision variables, such as well locations and production scheduling parameters, to obtain the best economic strategies. Running a simulator for such a large number of evaluations may be infeasible due to the computation time involved. This study investigated two multivariate interpolation algorithms, Least Squares and Kriging, to generate new realizations from a limited number of simulations in order to predict the optimal strategies in a field development scheduling project and a waterflood project. The result was a significant reduction in the simulation effort required. The recommended solutions were obtained by searching for the optimal objective function values among the interpolation realizations. Additional simulation runs were performed to refine the search for the final optimal solution in the vicinity of the intermediate optimal region. The net present value was used as the objective function in both projects. The field development scheduling simulation was achieved by an economic model taking account of all the costs and profits during the time period being studied. In the waterflooding application, movement of the waterfront was tracked and the oil and water production at each production well was calculated, after which the optimum well placement strategy was determined. The results obtained using the interpolation methods showed that the algorithms are able to reduce the number of simulation runs, to provide a global sketch of the objective function surface, to avoid possible failure at local optima, and to reach the absolute optimum by refining the …