Chemical–Gravity–Thermal Diffusion Equilibrium in Two-Phase Non-isothermal Petroleum Reservoirs
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- 作者：Mohammad Hossain Nikpoor ; Morteza Dejam ; Zhangxin Chen ; Matthew Clarke
- 刊名：Energy & Fuels
- 出版年：2016
- 出版时间：March 17, 2016
- 年：2016
- 卷：30
- 期：3
- 页码：2021-2034
- 全文大小：786K
- ISSN：1520-5029
文摘
The initial state of hydrocarbon mixtures in petroleum reservoirs is the result of equilibrium among several forces, the most important of which are the chemical forces arising from chemical potential gradients of the molecular species in the petroleum accumulation, the gravitational force arising from the gravitational acceleration, and the thermal diffusion forces arising from temperature gradients. The equilibrium among these forces determines the state of pressure and a compositional gradient and the creation of a gas–oil contact (GOC) in a stationary reservoir along with the changes in other physical properties. Accurate modeling of these changes in the development of a proper stationary model for the reservoir simulation initialization leads to more realistic predictions of the future behavior of petroleum reservoirs. This is important especially when phase behavior is important in designing, modeling, and predicting the performance of the processes used to maximize the oil recovery, such as in dealing with a gas condensate reservoir or when miscible displacement is to be done in the enhanced oil recovery (EOR) stage of the reservoir life. In this study, we consider the equilibrium among chemical, gravitational and thermal diffusion forces to predict the changes in reservoir fluid composition and pressure and also to predict the location of a possible GOC in a reservoir. Additionally, we develop a simple model to predict the change of the plus-fraction molecular weight (MW) in a non-isothermal reservoir using continuous thermodynamics and the theory of irreversible processes. We propose a method not only to tune the equation of state (EOS) versus the measured PVT lab data for one fluid sample but also to accurately model the depths of the GOC and other fluid samples and their PVT lab data in order to determine which sample is representative of the reservoir fluid and also to develop an EOS model that can work for every fluid in the reservoir, not just a single point. In two case studies, we validate our calculation procedure for the general compositional gradient, GOC detection, and the plus-fraction MW change in the reservoir against two data sets from the literature. The computational results show that the model developed works satisfactorily to predict the fluid changes in these two reservoirs. Subsequently, we also report the results of a series of sensitivity analysis tests to show the factors affecting the compositional gradient calculations and present examples of abnormal fluid distributions in a hydrocarbon fluid column where the fluid becomes denser toward the top of the column or the changes in fluid properties are highly nonlinear with respect to depth in the reservoir.