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PVT considerations are important in setting up the proper parameters when undergoing reservoir simulation. Phase behavior of a mixture 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 mixtures at a specific reservoir pressure, knowing the derivatives of all phase properties with respect to pressure and composition is important in reservoir simulation. With either approach, the PVT quantities required by a reservoir simulator are essentially the same. Modern reservoir simulators are usually written with a general compositional formulation, whereas black-oil PVT properties are converted internally to a two-component "compositional" model; the two components are surface gas and surface oil. A reservoir simulator keeps track of overall composition in each computational grid cell as a function of time. The phase fluxes and component movement within the reservoir are greatly affected by phase behavior (e.g., the mobility of each phase and which components are carried in each phase).
Upscaling, or homogenization, is substituting a heterogeneous property region consisting of fine grid cells with an equivalent homogeneous region made up of a single coarse-grid cell with an effective property value. Upscaling is performed for each of the cells in the coarse grid and for each of the grid properties needed in the reservoir flow-simulation model. Therefore, the upscaling process is essentially an averaging procedure in which the static and dynamic characteristics of a fine-scale model are to be approximated by that of a coarse-scale model. A conceptual illustration of the upscaling process is shown in Figure 1. Typically, 3D geological models contain detailed descriptions of the reservoir that can be hard to capture properly with a significantly coarser model. Therefore, it would be preferable if upscaling could be avoided. Currently, an average-sized flow simulation model consists of approximately 100,000 active grid cells. This is to ensure that the CPU consumption of a simulation run will be reasonable (i.e., within practical limits).
Geostatistical reservoir-modeling technologies depart from traditional deterministic modeling methods through consideration of spatial statistics and uncertainties. Geostatistical models typically examine closely the numerous solutions that satisfy the constraints imposed by the data. Using these tools, we can assess the uncertainty in the models, the unknown that inevitably results from never having enough data. Reservoir characterization encompasses all techniques and methods that improve our understanding of the geologic, geochemical, and petrophysical controls of fluid flow. It is a continuous process that begins with the field discovery and all the way through to the last phases of production and abandonment.
The linear equation solver is an important component in a reservoir simulator. It is used in the Newton step to solve the discretized nonlinear partial differential equations. These equations describe mass balances on the individual components treated in the model. For nonisothermal problems, an energy balance is added to the system. The matrix problem involves solving Ax b, where A is typically a large sparse matrix, b is the right-side vector, and x is the vector of unknowns.
The aim of gridding in reservoir simulation is to turn the geological model of the field into a discrete system on which the fluid flow equations can be solved. The basic structure of an oil reservoir is a set of geological horizons representing bedding planes. The reservoir may contain faults, at which the strata are displaced. It is usually possible to identify many more layers in the geological model than it is practical to include in reservoir flow simulation, so some upscaling of rock properties will normally be carried out. Even after this process, the geology to be represented is rarely homogeneous at the scale of the simulation grid.
Miscible injection is a proven, economically viable process that significantly increases oil recovery from many different types of reservoirs. Most miscible flooding projects use CO2 or nitrogen as solvents to increase oil recovery, but other injectants are sometimes used. This page provides an overview of the fundamental concepts of miscible displacement. Also provided are links to additional pages about designing a miscible flood, predicting the benefits of miscible injection, and a summary of field applications. Fieldwide projects have been implemented in fields around the world, with most of these projects being onshore North American fields.
The objective of scaleup is to take the behavior predicted from detailed, fine-grid reference models that at best represent only a few wells and a tiny part of the reservoir and transfer it to a model that attempts to represent many wells and the integrated behavior of the entire compositionally enhanced solvent flood (or at least a significant portion of it). Jerauld is a good example of the application of this method. Several reference models describe different areas of the field. Water/oil, solvent/oil, and solvent/water pseudorelative permeability relations are developed, along with pseudotrapped-solvent and solvent-flood residual oil values, so that the relevant behavior of the reference models is reproduced by corresponding models that have the same coarse grids as the full-field model. The coarse-grid models, of course, represent the same parts of the full-field model that the reference models represent.
In most compositionally enhanced solvent displacements, some of the solvent will be trapped permanently in the reservoir and will not be produced. This happens when water is used to drive a solvent slug and the oil displaced by the solvent. Solvent is trapped by advancing water much like oil is left as a residual in a waterflood. Solvent also can be trapped by oil that crossflows into a previously solvent-swept zone. In water-alternating-gas (WAG) flooding, solvent trapping can affect saturation through the mechanism of relative permeability hysteresis.
In practice, vapor/liquid reservoir phase behavior is calculated by an equation of state (EOS). The two most common EOSs that have been used for oil-recovery solvent-injection processes are the Peng-Robinson EOS and the Soave-Redlick-Kwong EOS. Of the two, the Peng-Robinson EOS seems to be the one most often cited in the literature and is the one discussed in some detail. The Soave-Redlick-Kwong EOS is used in a similar manner to predict solvent/oil phase behavior. For heavier components, where ω 0.49, the following equation is recommended: The constants in Eqs. 2 and 4 are often designated Ωa and Ωb. Eq. 1 represents continuous fluid behavior from the solvent to liquid state, and it can be rewritten as Jhaveri and Youngren adapted a procedure used by Peneloux et al. and modified the original Eq. 1 to include a third parameter to allow more-accurate volumetric predictions, which is recommended for solvent/oil simulations.