Optimization of Nonconventional Well Type, Location, and Trajectory

Abstract

Summary The determination of the optimal type, location, and trajectory of a nonconventional well is extremely challenging. The problem is more complicated than other well optimization problems because of the wide variety of possible well types (i.e., number, location, and orientation of laterals) that must be considered. In this paper, a general methodology for the optimization of nonconventional wells is presented. The optimization procedure entails a Genetic Algorithm (GA) applied in conjunction with several acceleration routines that include an artificial neural network, a hill climber, and a near-well upscaling technique. The overall methodology is then applied to a number of problems involving different reservoir types and fluid systems. It is shown that the objective function (cumulative oil produced or net present value of the project) is always increased relative to its value in the first generation of the optimization, in some cases by 30% or more. The optimal well type is found to vary depending on the reservoir model and objective function. The effects of reservoir uncertainty are also included in some of the optimizations. It is shown that the optimal type of well can differ, depending on whether single or multiple realizations of the reservoir geology are considered.