Geostatistical models are used to provide equally probable reservoir descriptions that honour available data of a given reservoir. The differences between these descriptions provide an indication of reservoir uncertainty due to lack of information. Transferring this uncertainty into the reservoir performance forecast would require flow simulation of a large number of these equally probable descriptions. The spread in the response derived from the flow simulation measures its uncertainty. However, this approach is not a viable option in most cases because it would require excessive amounts of computer time. A new approach for transferring geological uncertainty is presented. Each reservoir description is first ranked using a Fast Simulator (FS) rather than the Comprehensive flow Simulation (CS). A few selected descriptions are then processed through the CS to generate an approximate probability distribution of tile reservoir production response. This approach yields a considerable saving in the computer time over the use of the CS alone. This approach is tested using a waterflood example in a quarter of a five-spot. A standard black-oil type simulator is used as the CS and a tracer model is used as the FS. Approximate probability distribution of production parameters (water breakthrough rime, cumulative oil recovery and cumulative water-oil ratio) are generated by selecting just five reservoir descriptions among 100, and processing them through the CS. The approximation is tested by running the CS on all 100 cases to generate the reference probability distributions. The tracer concentration at the producer provided very good results with a 90% reduction in computer time over the use of the CS for all 100 descriptions. Introduction Many complex geological processes, such as sedimentation, erosion, migration, compaction and diagenesis produce complex spatial distributions of reservoir properties. Rock type, porosity, permeability, fault occurrence, degree of cementation, and hydrocarbon saturation are some of these properties. However, the exact conditions that result from these complex geological processes are never known e.xacrly. This spatia [distribution of reservoir properties generally presems some degree of Variability, often shared with a complex geological architecture. Moreover, in practice, samples obtained represem only a very small fraction of [he whole reservoir volume, resulting in incomplete knowledge about the reservoir structure at all scales. For all these reasons, it is not advisable to consider only one single reservoir description or image and describe the spatial distribution of reservoir properties in a purely deterministic manner. Rather, a statistic.a1 treatment of the variables involved is desirable one that recognizes the lack of knowledge or uncertainty associated with any description selected. The geological uncertainty can be determined through the differences between many equally probable reservoir descriptions generated using a geostatistical technique known as slochastic simulation(l,2). Each of the images reproduces a prior measure of spatial continuity (covariances) and may be conditioned to honour available data, such as, core and log data, well test, geophysical and geological interpretation, etc. The variables that should mostly be described using probablistic techniques are those that influence the amount, position, accessibility and flow of fluids through the reservoir (e.g. lithofacies or flow unit distributions, porosity, permeability, and fault occurrences).
To describe two-phase displacement with hysteresis we use the Buckley-Leverett model with the imbibition, drainage and scanning fractional flows. Mathematical theory for the initial-boundary non-self-similar problems is developed. Structure of solutions is presented together with the physical interpretation of phenomena. Analytical solutions for the injection of the water slug with the gas drive and for the sequential injection of water and gas slugs with the water drive are obtained. The solutions show that the hysteresis decreases the gas flux in the case where the drainage relative permeability is lower than the imbibition one, which is a positive effect for the WAG injection.
The first application of compositional upscaling to the routine modeling of a major reservoir is described. The reservoirCupiagua, 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. IntroductionCompositional 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 com-
Summary The RESCUE consortium formed in 1995 in response to the requirement to transfer the structural framework, 3D gridded models, and associated well data from "geomodels to upscalers." RESCUE developed a data standard and libraries that allowed multiple vendors to support subsurface projects across the geoscience and engineering domains. To date, more than 20 of the sponsoring petroleum companies and application vendors have integrated RESCUE into their applications. In late 2008, the consortium began a transition to a much more flexible standard: RESQML™. RESQML joins with WITSML™ and PRODML™ as the latest XML-based data-transfer standards managed by Energistics. The V1.0 developer's release of the RESQML data standard was published in December 2010, followed by a fully commercial V1.1 release in October 2011. We expect vendor applications using RESQML V1.1 to be available commercially in 2012. RESQML has been designed to support Interaction with real-time production and drilling domains Transfer of gigacell reservoir-simulation models, as are currently in use in some areas of the world, together with static reservoir models, which may be several orders of magnitude larger Lossless data transfer for complex grids, especially for nonstandard connectivity Retention of the geologic and geophysical meta-data associated with 3D grids Data standards to support flexible and iterative multivendor subsurface workflows across geology, geophysics, and engineering As an example of the latter, workflows that support fault-seal analysis or 4D-seismic interpretation benefit greatly from the integration of data, metadata, and interpretations from multiple geophysical, geologic, and engineering applications into a coherent subsurface model. Such integration is not possible for a single application or vendor. In this paper, we will present our objectives, challenges, and work plan for 3D/4D-reservoir-model exchange. Details of the technical design defined by participating petroleum oil companies and software vendors will be shared to demonstrate the efficiency of the RESQML standard.
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