Maximizing volumetric sweep efficiency in waterfloods with hydrocarbon F–Φ curves

Waterflooding is by far the most commonly used method to improve oil recovery. Success of a waterflood depends on its ability to sweep mobile oil efficiently. Incorrect or insufficient design may lead to increases in cost associated with water cycling and poor sweep. Most waterflood management is restricted to classical methods (e.g., surveillance or pattern balancing) or sensitivity studies centered on finite difference simulation. Most of the time, conventional methods fail to account for subsurface heterogeneity. Optimizing sweep via numerical modeling is time consuming in waterfloods with large number of wells or a relatively high-resolution numerical grid. pose a practical and efficient approach for rapid and full-field optimization of waterfloods. Our method focuses on optimizing volumetric sweep efficiency using streamlines. We introduce two new concepts: the hydrocarbon F–Φ curve and Hydrocarbon Lorenz Coefficient (LC-HC). We show that these concepts can be easily derived from streamline simulation and can be used for optimum waterflood management. LC-HC serves as a unique measure of the flow – or dynamic – heterogeneity. We show that minimizing LC-HC results in maximum volumetric sweep efficiency. The method is straightforward: we evaluate the sensitivity of LC-HC to variations in operating conditions in a design of experiment (DoE) study, describe the LC-HC as a function of those operating conditions using response surface methods, then select the operating conditions that minimize LC-HC (maximize sweep efficiency). The main advantages of our method are its speed, flexibility to start optimizing at any arbitrary time regardless of the history, and ability to handle large problems. The new approach requires running a streamline simulator only a few time steps, so multi-million cell models are simulated in minutes. ified our approach with several synthetic examples. An example shows that a 100,000 cell, complex reservoir with 13 wells, and 29 completions can be optimized in less than 4 h, leading to significant increase in recovery efficiency and reduced water cycling. We then apply the method to a model of the Brugge field, the SPE comparative problem on recovery optimization. Improved production response illustrates the power of the new method.