Data from seismic to production is integrated to build models to provide estimations of parameters such as petroleum volumetrics, pressure behavior, and production performance (Fig. 1). The level of confidence of these models depends on the representativeness of the data. The quality of the generated models is based on the data interpreted and integrated aimed to build computational realizations of petroleum reservoirs.

Reservoir dynamic simulation is the most applied process that integrates all reservoir data, where an Equation of State (EOS) is coupled with the objective to estimate the fluid thermodynamic state at each computational step. The simulation consists of iterative mathematical computations in which the reservoir-defined conditions at the previous time step is an input to determine the properties at the next and subsequent time steps. The calculated pressure is a fundamental variable in each time step, which means that a representative and high level of confidence Pressure Volume Temperature (PVT) model is required to avoid scale-up of errors resulting from fluid pressure estimation.

A PVT modeling includes three main stages:

  1. Fluid sample and data acquisition

  2. Laboratory analysis and fluid characterization

  3. The EOS model.

The emphasis in this work is on the EOS model, which is the fluid model used for the simulation process. The objective of this work is to analyze the main uncertainties associated with typical EOS modeling and defining the level of confidence of these EOS approaches. In this work, some of the most-used approaches for EOS modeling are reviewed. An assessment of these methods is also provided based on their application to actual petroleum fluids with the objective of defining their statistical level of confidence.

First, the study analyzes the sources of critical uncertainties in a PVT EOS model. Second, a statistical number of PVT laboratory studies of petroleum fluids is used to determine the level of confidence of four approaches that are based on the two well-known Peng-Robinson and Soave-Redlich-Kwong EOS. Third, statistical analysis is performed to determine the level of confidence of the different methods. Fourth, a correlation to determine the optimal number of pseudo-components is defined. These steps include:

  • Characterization of fluid and heavy components

  • Tuning

  • Lumping.

As a result of this study, one can conclude:

  • The level of confidence of the four analyzed approaches

  • The significance of the difference between the analyzed methods

  • A correlation to determine the optimal number of pseudo-components.

In this work, a statistical analysis over some of the most-used EOS modeling approaches and on a set of petroleum fluid PVTs was performed to determine the level of confidence of four EOS modeling methods. In addition, a correlation was introduced for a priori determination of the optimal number of pseudo-components in a PVT fluid.

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