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Abstract

Summary

Reservoir engineers are in need of information about fluid properties of reservoir fluid before calculating reserves or production scenarios. Mature fields may require reviews of older data sets that are sometimes scarce. The so-called PVT properties (black oil or compositional) are generated in either an in-house or an external lab. Prior to their use, these data sets need to be checked for their correctness and consistency. Modelling with correlations for estimating some of the properties or equations of state (EOS) provides only limited insight. First, they are not applicable for each reservoir fluid. Due to the variety of chemical composition every fluid is unique. Secondly, the correlations are purely numerical, lack non-dimensionality and consider physics only to a limited extent. Black oils separate below saturation pressure into a vapour and a liquid phase. The gas phase, consisting predominantly of the lighter compo-nents, increases with decreasing pressure. In other words, the higher the pressure the more gas is in solution. It influences other quantities, like formation volume factor Bo, oil com-pressibility Cpo and oil viscosity µo. This paper analyzes how the components of the gas phase contribute to the PVT-properties mentioned. It is assumed that the light components assume a certain volume in the liquid phase which is dependent on temperature and pressure. Additionally, the shape of the heavier components plays a role. As the light and heavy molecules in the mixture try to assume a minimal volume, the conversion factor from the va-pour to the liquid volume of the light components varies to some degree. The parameters (conversion factors) necessary to model Bo, Cpo and µo are extracted from experimental data. Mathematically, it is a minimization problem where the variables need to be positive. The solution is sought with a simplex algorithm. Once the parameters are determined, an estimate of Bo, Cpo and µo can be calculated, and plausibility and consistency of lab PVT-data can be carried out. This approach provides a valuable tool for the reservoir engineer in assessing the quality of PVT-data.

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/content/papers/10.3997/2214-4609.20141793
2014-09-08
2020-01-19
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