Well Placement Optimization in Field Development

Abstract

Abstract Determination of optimal well locations is a challenging task because engineering and geologic variables affecting reservoir performance are uncertain and they are often correlated in a nonlinear fashion. This study presents an approach where a hybrid optimization technique based on genetic algorithm, with polytope algorithm as the helper method, was used in determining optimal well locations. The Hybrid Genetic Algorithm (HGA) has been shown to work on synthetic and field examples alike. The HGA was used to optimize both horizontal and vertical wells for both a gas injection and water injection projects with net present value (NPV) maximization as the objective. Comparison of results was made between locations proposed by the HGA and those selected by engineering judgment. Results showed that horizontal wells performed better than vertical wells from the recovery standpoint for the synthetic reservoir. For a real reservoir, however, horizontal wells performed only marginally better than the vertical wells owing to low-kv/kh ratio. We also observed that optimal well locations are a strong function of the anticipated project life. A method of integrating the HGA with Experimental Design (ED) was also investigated. For this purpose, a synthetic reservoir was used and exhaustive runs were made with increasing well count. In this particular case study, we observed that the uncertainties in the variables affecting recovery did not affect the optimal number of wells required to develop this reservoir. Thus, forehand knowledge of the well count eliminates the need for the inclusion of this process variable in the ED matrix.