Reservoir management using streamline simulation

Abstract

Geostatistical techniques can generate fine-scale description of reservoir properties that honor a variety of available data. The differences among multiple geostatistical realizations indicate the presence of uncertainty due to the lack of information and sparsity of data. Quantifying this uncertainty in terms of reservoir performance forecast poses a major reservoir management challenge. One solution to this problem is flow simulation of a large number of these plausible reservoir descriptions. However, this approach is not feasible in practice because of the computational costs associated with multiple detailed flow simulations. Other major reservoir management challenges include the determination of the swept and unswept areas at a particular time of interest in the life of a reservoir. Until now, sweep efficiency correlations have generally been limited to homogeneous 2-D cases. Calculating volumetric sweep efficiency in a 3-D heterogeneous reservoir is difficult due to the inherent complexity of multiple layers and arbitrary well configurations. Identifying the swept and unswept areas is primarily important for making a decision on the infill locations. Most of the mature reservoirs all over the world are under waterflood. Managing a waterflood requires an understanding of how injection wells displace oil to producing wells. By quantifying the fluid movements, the displacement process can be actively managed. Areas that are not being swept can be developed, and inefficiencies, such as water cycling, can be removed. Conventional simulation provides general answers to almost all of these problems, however time constraint prohibits using a detailed model to capture complexities for each well. Three dimensional streamline simulation can meet most of these reservoir management challenges. Moreover use of fast streamline-based simulation technique offers significant potential in terms of computational efficiency. Its high performance simulation speed makes it well suited for describing flow characteristics for high resolution reservoir models and can be used on a routine basis to make effective and efficient reservoir management decisions. In this research, we extend the capability of streamline simulation as an efficient tool for reservoir management purposes. We show its application in terms of swept volume calculations, ranking of stochastic reservoir models, pattern rate allocation and reservoir performance forecasting under uncertainty.