Well Optimization Strategies in Conventional Reservoirs

Abstract

AbstractWell optimization is an important factor in field development strategies targeted to maximizing the hydrocarbon recovery, and economic feasibility of new field development projects. Particularly, in view of shortage in new oil field discoveries, maximizing oil production and net present value (NPV) have become critical factors (hereinafter called the "critical factors") in reservoir engineering. As a result, well optimization research has become a separate field in its own merit. Recent attempts by academic and industrial researchers converged on the goal to create efficient well optimization models that can predict strategies for managing the existing oil and gas fields and developing new fields with potential for maximizing the critical factors.Important elements in field development optimization include well type, well placement and scheduling. In the last decade, significant amount of work has been done in the area of well optimization for which both gradient based and gradient-free optimization methods were used. In gradient-based well optimization methods, the derivative of the objective function with respect to the decision variables is sought. In gradient-free optimization, a family of algorithms classified as global or "stochastic" algorithms - such as the genetic algorithm, simulated annealing, and particle swarm optimization - can be employed. Other algorithms such as local or "deterministic" algorithms (e.g. Generalized Pattern Search, and Hook Jeeves Direct Search,) are also useful in these studies. These optimization strategies can be applied individually or as an ensemble of optimization methods to maximize the critical factors in reservoir simulation.In this paper, we review several of the current optimization techniques, and their application to maximize the critical factors. In the process, we address the significance of different methods and highlight their limitations. We discuss as well the challenges associated in extending these methods, and their potential in the future.