Gas condensates and volatile oil reservoirs are simulated using compositional simulation or modified black-oil (MBO) approaches to account for composition changes in the reservoir1. The current approach in the industry is to use a tuned EOS model to represent the fluid phase behavior and volumetric properties. However; there is no consensus on how to tune an EOS model, how many pseudo-components to use, or what weight factors to apply to experimental data when tuning an EOS model for use in compositional and MBO reservoir simulation studies.
Regarding the EOS tuning parameters, for example, some investigators prefer to regress on the binary interaction parameters (BIP) and the (Ω's) parameters of the EOS. Others have chosen the volume shift, BIP's, and critical parameters as matching parameters. Also, there is a newer approach where some investigators have chosen to vary the plus fraction molecular weight followed by the volume shift parameters.
In this study, we have constructed an EOS model program to perform the five commonly used tuning approaches. We have investigated the different tuning approaches using a large data base of PVT samples spanning the range of black-oils through volatile oils and gas condensates (including rich gas condensates and lean wet gases). We have compared the different tuning approaches based on two criteria: (1) the quality of match after applying the tuning procedure, as indicated by the absolute average error (AAE), and (2) the percentage deviation in the tuning parameters from their original values.
The tuning of many reservoir fluids using the different approaches has shown that some approaches were more superior to others and usually yielded excellent matches. While the other tuning approaches gave moderate to good matches. Based on this work, we were also able to give practical recommendations on tuning approaches that yield consistent EOS models.