Coupling Memory-Based Diffusivity Model with Energy Balance Equation to Estimate Temperature Distributions During Thermal EOR Process

Abstract

Abstract At the end of the primary recovery phase, enhanced oil recovery (EOR) process such as hot water flooding becomes more attractive to reduce the residual oil saturation and to increase the oil production. It is well known that during hot water injection, the heat transfers from the injected fluid to the reservoir matrix, and this transfer depends mostly on the rheological and thermal properties of the formation rock and fluids. Moreover, the injection time and fluid velocity play significant roles on the temperature distribution. Therefore, it is important to investigate the effects of fluid velocity on the temperature distribution during thermal operations. The temperature profile in a reservoir formation is an important indicator of various reservoir conditions, such as type of fluid encroachment, the state of water or gas influx, etc. This information is necessary for better reservoir management. Previous studies assumed constant fluid velocity throughout the reservoir to calculate the temperature variations. However, this work studies the temperature distributions of reservoir rock and fluid during thermal operations by employing the velocity profiles. The temperature distributions are estimated by coupling a proposed memory-based diffusivity model with energy balance equation. The variations of fluid velocity are produced by solving the memory-based diffusivity equation, and then the temperature distributions are estimated based on the fluid velocity profiles. The proposed energy balanced equation is solved by a newly developed numerical technique. This technique is used to couple the energy balance equation with the memory-based diffusivity model. The model equations are solved simultaneously together by Matlab programming language. The temperature profiles are analyzed for unequal rock and fluid temperatures throughout the reservoir. Results show that, coupling of the pressure model and temperature model can lead to more reasonable temperature distributions during thermal flooding and then to better design of such operations. In addition, it is found that temperature distributions are affected mainly by the pressure variations and the fluid velocity. The finding of this research will help to improve the performance/efficiency of thermal flooding operations.