Cupiagua: Modeling of a Complex Fractured Reservoir Using Compositional Upscaling
- P.R. Ballin (BP America) | P.J. Clifford (BP Exploration) | M.A. Christie (Heriot-Watt U.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2002
- Document Type
- Journal Paper
- 488 - 498
- 2002. Society of Petroleum Engineers
- 4.6 Natural Gas, 5.5.8 History Matching, 5.2.2 Fluid Modeling, Equations of State, 5.8.6 Naturally Fractured Reservoir, 3.3.1 Production Logging, 3.3.2 Borehole Imaging and Wellbore Seismic, 5.8.8 Gas-condensate reservoirs, 5.5 Reservoir Simulation, 4.1.5 Processing Equipment, 5.5.3 Scaling Methods, 5.3.2 Multiphase Flow, 5.1.5 Geologic Modeling, 5.4.3 Gas Cycling, 4.3.4 Scale, 5.6.5 Tracers, 5.2 Reservoir Fluid Dynamics, 5.2.1 Phase Behavior and PVT Measurements
- 4 in the last 30 days
- 553 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
The first application of compositional upscaling to the routine modeling of a major reservoir is described. The reservoir - Cupiagua, in Colombia - is a rich gas condensate field. Given that Cupiagua falls rapidly below dewpoint and produced gas is recycled, the main recovery process is the vaporization of liquid components into the gas phase, in which they are transported to producers. Therefore, the process is compositional, but flow is dominantly in one phase.
Cupiagua is a heterogeneous reservoir dominated by natural fracture corridors that provide more than 80% of the permeability in some areas. It is not possible to represent these features explicitly within the full-field model (FFM), nor do they fit a conventional dual-porosity representation. Therefore, an upscaling process is required.
The process described is the use of alpha-factor compositional upscaling functions, which modify the velocities of individual pseudocomponents. We show how fine-grid cross-section modeling, with a range of sensitivities, may be used to generate an appropriate set of alpha-factor functions, which are validated against detailed sector models and may then be used as the principal history-match parameter in the FFM.
Compositional upscaling is a technique that has been developed at a theoretical level for a number of years. A set of tools to generate compositional upscaling functions for realistic reservoir and fluid descriptions now exists, and some commercial simulators have been adapted to apply these functions in full-field compositional simulation.
There arguably has been some delay in appreciating the importance of the technique, given that many of the potential applications (such as miscible injection processes) involve a whole set of complexities (such as three-phase flow description and upscaling) in which compositional upscaling is just one more factor.
Cupiagua, however, is a good candidate for compositional upscaling, with a production rate that cannot otherwise be simulated even approximately in an FFM.
It is a good candidate because almost all reservoir flow occurs in the gas phase, including the transport of the heavier components. The liquid phase is relatively immobile. (An exception is the near-well region, in which the condensate-banking phenomenon is represented by techniques involving two-phase pseudopressure functions.1 However, this region is smaller than a single FFM gridblock and can be decoupled from the reservoir flow problem.) The use of pseudorelative permeability upscaling functions is therefore not needed. Cupiagua is also a good candidate because it has strong heterogeneity, which is too small-scale to be represented explicitly in FFM gridblocks and therefore requires upscaling.
Because the early gas/oil ratio (GOR) increase in Cupiagua is caused by injected gas movement through natural fractures, the only reasonable way to achieve an early FFM match without compositional upscaling would have been through an exaggerated permeability anisotropy. Then, this would have given a completely incorrect picture of subsequent sweep and GOR development.
Cupiagua is not unique in these regards, and we consider that there is no reliable means of modeling rich gas condensate reservoirs in general without a compositional upscaling technique.
Upscaling techniques have been widely reported,2,3 and many applications are already available, mainly for single-phase displacement. The extensive experience in single-phase studies resulted in mature techniques with generally well-known limitations. Two-phase upscaling4,5 studies have been shown to work for realistic black-oil models. However, their application is still limited, indicating that improvements in speed and reliability are important for routine reservoir field applications.
Very limited experience is available for upscaling compositional flows. One method proposed by Barker and Fayers6 using alpha factors was later combined with streamline techniques,7 generating an approach8 that is highly suitable for field applications. This approach was implemented in an internal BP general upscaling program. The inclusion of the resulting alpha factors in compositional flow equations was implemented as a special option of a commercial flow simulator.
In compositional modeling, the fluid behavior is described by an equation of state (EOS) for n pseudocomponents, which determines the split between the liquid and gas phases as well as their densities. The conservation equations are applied for each component, and the flow equations are applied for each phase. However, compositional modeling has one additional step of flash calculation using EOS. So, in addition to incorporating subgrid heterogeneity and controlling numerical dispersion, compositional upscaling has to handle the phase splits and component fluxes appropriately. In this implementation, a streamline approach was used to handle some of the above elements, but the effect of subgrid heterogeneity on the component fluxes is handled by the alpha-factor method. Because the streamline approach is better covered in the literature,9-11 a short overview of the alpha-factor method is presented here.
|File Size||6 MB||Number of Pages||11|