Because of increased speed and accuracy, 3D streamline simulation now can be used in a wide range of reservoirs. Large reservoirs with hundreds or thousands of wells, several hundred thousand grid blocks and an extensive production history always have been a challenge for finite-difference simulation. The size and complexity of these reservoirs generally have limited simulation to sections or patterns. The streamline technique enables simulation of these reservoirs by reducing the 3D domain to a series of 1D streamlines along which the fluid flow computations are performed, offering computational benefits orders of magnitude greater. Additionally, increased accuracy is achieved by maintaining the sharp flood fronts from the displacement processes and reducing grid orientation effects. The streamline simulation results substantially have more value as a reservoir management tool when used in conjunction with traditional reservoir engineering techniques such as standard finite-difference simulators. Several case studies that highlight a variety of situations where streamline methods proved highly beneficial are presented in this paper. These studies will help the practicing reservoir engineer decide whether to apply streamline methods and the optimal timing for the application. Introduction Streamline and streamtube methods have been used in fluid flow computations for many years31,48. Early applications for hydrocarbon reservoir simulation were reported by Fay and Pratts22 in the 1950's, Higgins et al32–34 in the early 1960's, and Pitts and Crawford53, LeBlanc and Caudle41 and Martin and Wegner et al43,44 in the 1970's. Streamline/streamtube methods numerically solve the complex fluid flow models for multiphase flow in porous media with a reasonable balance between the computational efficiency and the physics modeled. As computers became more powerful, attention turned toward developing simulators based on finite-difference (FD) methods60, including more physical effects. However, computer models of reservoirs have grown in complexity and geological models with tens of millions of grid cells can now be created. Conventional finite-difference methods suffer from two drawbacks, numerical smearing and computational efficiency for models with a large numbers of grid cells. For models with a large number of wells, the number of cells required to achieve acceptable accuracy between wells can be prohibitive. Also, accurate modeling of geological heterogeneities can require a very large number of cells. A finite-difference method based on an IMPES approach suffers from the time-step length limiting Courant-Friedrichs-Lewy (CFL) condition. As the number of cells grows higher, the maximum time-step length gets shorter for a given model. For a very large number of cells, the shortness of the time step can render the total CPU time impractical for a simulation. The advantage of the fully implicit approach is stability of the solutions. The time-step length is only limited by the nonlinearities; however, these can be strong and, in practice, keep the time-step length relatively short. A disadvantage of the fully implicit approach is the tendency to smear the solution (numerical dispersion) even more than the IMPES approach.