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. Introduction The need for a well placement optimization tool cannot be overemphasized because reservoir performance is highly dependent on well locations. Determination of optimal well locations cannot be based on intuitive judgment because engineering and geologic variables affecting reservoir performance are often nonlinearly correlated. Because the performance of a reservoir is time and process dependent, well placement decisions cannot be based on static properties alone. One way of solving this problem is direct optimization with the simulator. Numerical models are able to evaluate the complex interactions of various variables affecting development decisions, such as reservoir and fluid properties, well and surface equipment specifications, and economic factors. Even with these models, the current practice is still the ad-hoc, single-well-configuration-at-a-time approach when infill prospects are sought. In each trial, well configuration is selected based on the intuition of the reservoir engineer. For a single-well case, this one-well-at-a-time approach may lead to suboptimal decisions. The problem definitely compounds when multiple producers and injectors are involved in a field development scenario. The use of the Hybrid Genetic Algorithm (HGA) offers a way out. Güyagüler1 and Güyagüler et al.2 reported development and application of HGA in determining optimal well locations for vertical wells. In this study, we extended Güyagüler's work to include horizontal wells. Thereafter, the tool was tested with a gas-injection project involving a synthetic reservoir, and two actual field development scenarios. Application of infill prospects in a developed field is also shown. In all cases, comparisons with independent intuitive solutions are discussed. Background Over the years, a lot of research has been done in the field of optimization. The focus of a significant number of them has been optimal determination of well locations using numerical simulation, coupled with an automated optimization algorithm. Detailed descriptions of these methods abound in the literature. This work uses the Hybrid Genetic Algorithm (HGA), a member of stochastic optimization algorithms based on nature's way of finding the individual fittest to its environment. Genetic Algorithm (GA), the core of the HGA and a member of these evolutionary techniques, was introduced in 1975 by Holland.3 Genetic Algorithms employ ideas from the principles of genetics as found in the biological sciences, as well as in Darwin's theory of natural selection.