Simulation of Gas Condensate Reservoir Performance

Abstract

This paper presents a generalized equation of state (EOS) that represents several widely used cubic EOS's. The generalized form is obtained by manipulation of Martin's EOS 1 and is applied in this smdy. A component pseudoization procedure that preserves densities and viscosities of the pseudocomponents and the original mixture as functions of pressure and temperature is described. This procedure is appiied with material balance requirements in generation of two-component, blackoil properties for gas condensates. Agreement between resulting black-oil and fully compositional simulations of gas condensate reservoir depletion is demonstrated for a very rich, near-critical condensate. Also, agreement between EOS compositional results and laboratory expansion data is shown. The fully compositional simulation necessary for belowdewpoint cycling is performed for the neaT-critical condensate with a wide range of component pseudoizations. Results show the well-known necessity of splitting the C 7+ fraction and indicate a minimal set of about six total components necessary for acceptable accuracy. L,t;::oduction Gas condensate reservoirs are simulated frequently with fully compositional models. This paper presents a pseudoization procedure that reduces the multicomponent condensate fluid to a pseudo two-component mixture of surface gas and oil. T4is aliows the use of a simpier, less expensive, modified black-oil model that accounts for both gas dissolved in oil and oil vapor in the gas. A major question in the use of the black-oil model is whether the two-component description can represent adequately the compositional phenomena active during the depletion or the cycling of gas condensate reservoirs. This question is especially pertinent to near-critical or very rich gas condensates. This paper, therefore, includes a comparison of black-oil and compositional simulations for depletion and below-dewpoint cycling of a naturally occurring ng, rich condensate only 15°F [8.3 0c] above its critical temperature. Like a number Qf unreported cases for leaner condensates, the two models give very similar results for depletion. In addition, the two models give identical results for cycling above dewpoint provided that certaln conditions are satisfied. However, the black-oil model is not applicable to cycling below dewpoint, so results of the compositional model are compared for different multi-component "Now with Scientific Software-Intercomp.

Overview and Computational Approach for Studying the Physicochemical Characterization of High-Boiling-Point Petroleum Fractions (350°C+)

“…Coats (1985) made an important contribution to connected computer simulation by creating a grouping of dummy components. This approach is known as pseudoization, and it aims to reduce the number of elements necessary to represent a fluid, reducing the computational effort.…”

Development and Optimization of Gas-Assisted Gravity Drainage (GAGD) Process for Improved Light Oil Recovery

“…The weakness of EOS towards calculation of some specific properties, the reliability of data and the target for the fluid properties study affect the values of these weight factors. Coats and Smart (1986), Coats (1988) and Bahbahaninia (2001) recommended a universal set of weight factors for experimental data to ensure proper tuning of EOS, which are shown in Table 2.41.…”

“…However, if the input parameters of EOS were adjusted widely by assigning weight factors other than those suggested by Coats and Smart (1986), Coats (1988), Bahbahaninia (2001) to match the experimental data, it would lead to unrealistic results. This is known as over tuning of EOS.…”

“…The best set of tuning parameter (binary interaction coefficient) obtained in this study that matched the saturation pressures for both the reservoir crude oils perfectly, was used to predict the laboratory PVT data as a reality check. The weight factors proposed by Coats and Smart (1986), Coats (1988) and Bahbahaninia (2001) were used to improve the PR-EOS predictive capabilities. These predictions were then compared against the actual laboratory PVT measurements of reservoir crude oil samples.…”

Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

“…Whitson and Torp (1983), formed a set of steps to estimate the MBO properties according to the constant volume depletion (CVD) test data for the gas condensate. Coats, 1985, introduced a different method for gas condensate reservoir using commercial software of EOS PVT and a regression package to fit the PVT data obtained in the laboratory. McVay (1994), expanded Coats method for the volatile oil reservoir.…”