Understanding the phase behavior of the various fluids present in a petroleum reservoir is essential for achieving optimal design and cost-effective operations in a petroleum development. Computer-based modeling techniques have been developed for estimating the properties and/or phase behavior of petroleum fluid in a reservoir of interest. The calculation of phase behavior in a reservoir model can be made in one of the two following ways: 1) using a “black-oil” approach based on simple interpolation of pressure, volume and temperature (PVT) properties as a function of pressure, or 2) using a “compositional” approach based on a thermodynamically-consistent model such as a cubic equation of state (EOS).
The main difference between these two methods is that a black oil simulator does not consider changes in composition of the hydrocarbons as the field is produced, whereas the compositional model fits the PVT properties of oil and gas phases to an equation of state (EOS) as a mixture of components. The compositional simulator then uses the tuned EOS model to dynamically track the movement of both phases and components in field. Thus, depletion of e.g. gas-condensate reservoirs and volatile-oil reservoirs can be accounted for in the compositional model.
Phase behavior of a fluid with known composition consists of defining the number of phases, phase amounts, phase compositions, phase properties (molecular weight, density, and viscosity), and the interfacial tension (IFT) between phases. In addition to defining the phase behavior of a fluid at a specific reservoir pressure, knowing the derivatives of all phase properties with respect to pressure, temperature and composition is important in reservoir simulation. Thus, compositional modeling is much more complex and difficult than the more simplified black oil techniques.
Typically, compositional modeling of phase behaviors employs an equation of state (EOS) model that represents the phase behavior of the petroleum fluid in the reservoir. Once the EOS model is defined, it can be used to compute a wide array of properties of the petroleum fluid of the reservoir, such as gas-oil ratio (GOR) or condensate-gas ratio (CGR) (one is the inverse of the other), density of each phase, volumetric factors and compressibility, and saturation pressure (bubble or dew point). Transport properties, such as viscosity, can also be obtained using the EOS model and standard viscosity correlations, such as Lohrenz-Bray-Clark (LBC) correlations. Furthermore, the EOS model can be extended with other reservoir evaluation techniques for compositional simulation of flow and production behavior of the petroleum fluid of the reservoir, as is well known in the art.
For example, compositional simulations can be helpful in studying (1) depletion of a volatile oil or gas condensate reservoir where phase compositions and properties vary significantly with pressure below bubble or dew point pressures, (2) injection of non-equilibrium gas (dry or enriched) into a black oil reservoir to mobilize oil by vaporization into a more mobile gas phase or by condensation through an outright (single-contact) or dynamic (multiple-contact) irascibility, and (3) injection of CO2 into an oil reservoir to mobilize oil by miscible displacement and by oil viscosity reduction and oil swelling.
In the recent years, exploration, drilling and production activities have ramped up significantly in unconventional resource plays such as Eagle Ford, Bakken, Permian, Barnett, and others around the world. In the past, fluid homogeneity in a hydrocarbon reservoir has been assumed. There is now a growing awareness that fluids are often heterogeneous or compartmentalized in the reservoir, but still very little detailed understanding of the variation in petroleum fluid quality both vertically and laterally in a play. As relatively unexplored resource plays are being developed, the capability of understanding and predicting petroleum fluid variation and phase behavior would be extremely useful in resource mapping and planning development strategies.
However, the complexities arising from petroleum fluid heterogeneity along the several thousands of feet lateral wellbore in unconventional shale reservoirs adds additional uncertainties to the compositional model. Thus, there exists a need for improved modeling abilities for full reservoir petroleum fluid phase behavior properties and predictions for large shale reservoirs. Ideally, this method can utilize data collected during drilling to dynamically monitor the system.