Fluid Behaviors Around a Well in Gas-Condensate Reservoirs
Authors: Takeda Tsuyoshi , Fujinaga Yoshinobu , Fujita Kazuo 
Published: December 2007

This paper presents fluid condensation behaviors around a well in a gas-condensate reservoir which are computed using a 2-Dimensional, 2-Phase compositional simulator of radial geometry. We discuss retrograde condensation behavior focusing on near a producing well. Effects of interfacial tension to relative permeabilities and effects of the gravity forces are taken account for in this study. The calculation program of phase behavior based on Soave Redlich-Kwong (SRK-EOS) was composed and was tested to match published laboratory data. Using real reservoir rock and fluid properties obtained from a reservoir offshore Sarawak in Malaysia, we demonstrates some predicted results of presumable retrograde condensation behaviors around a well.

Introduction

When we develop gas condensate reservoir it is important to predict condensate dropout around a wellbore because productivity of well stream gas is highly dependent upon the liquid saturation changes. It is more exaggerated especially for a rich gas reservoir which contains heavier components such as propane, butane, etc. Particularly in gas condensate reservoirs, the condensated liquid starts to accumulate around a production well when the flowing bottom hole pressure falls below the dew point of the fluid. The mobility of the fluid which consists of two phases as gas and condensated liquid is seriously decreased, and most of condensated liquid is left inpore space of the reservoir. This is the reason that the productivity is severely reduced.

In this study we use SRK EOS which is one of the equations of state in the type of van der Waals to analyze p-v-T relationship. In this equation we use the method written by J. Richard Elliott et al. to obtain binary interaction coefficient. This parameter is used for mixing rule of SRK EOS parameter a, b.

And to compute relative permeabilities, we use the method proposed by K.H. Coats. In this method we obtain relative permeability as a function of interfacial tension and saturation. The next chapter describes formulation for multicomponents, numerical simulator. The simulator is two-phase, two-dimensional radial model which represents vertical profiles around a wellbore including the effects of gravitational forces. The purpose of this study is to simulate the fluid behaviors around a production well for a condensate reservoir.

Description of this model

Kazemi et al. give detailed descriptions of compositional models for three phase, three dimensional flow in porous media. The method described by Kazemi is modified to the 2-D radial compositional model used in this study. The calculation of this model is carried out as following three steps.

Step 1. At first properties of the reservoir (pressure, temperature, porosity, permeability, thickness, well radius, and effective radius, etc.), of the fluid (compositions, critical pressure, critical temperature, molecular weight and specific gravity, etc.) are given. The reservoir is supposed to be radial geometry with a well being at the center of it. The reservoir fluid flow in this model occur in the radial and vertical directions (Fig. 1). The compositions of the fluid are assumed to contain 11 components which are 9 hydrocarbons, C1, C2, C3, i-C4,.. C7+ and CO2, N2. Properties of the critical point for C7+ is given by Standing's Method.

Step 2. In next step we solve phase behavior from input data. In this calculation SRK-EOS is used to compute vapor/liquid equilibrium and volumetric behavior of fluid.

(1)

Here is correction factor which is a function of accentric factor and a, b are parameter in SRK-EOS. Applying this equation for the mixture fluid, parameters a, b need to be corrected by binary interaction coefficients determined by J. Richard Elliott et al. Density and fugacity are given with compressibility factor which is calculated by SRK-EOS. Because the density is the weak point for this type of cubic equations, Peneloux et al. introduced a volume correction parameter for improving the volumetric prediction. We use the following method to improve density prediction.

(2)

where

(3)

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