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This paper was prepared for presentation at the 1999 SPE Asia Pacific Improved Oil Recovery Conference held in Kuala Lumpur, Malaysia, 25–26 October 1999.

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractGas injection is becoming a significant and economic IOR method, often implemented as a miscible or "near miscible" WAG project. Numerical dispersion effects in standard compositional simulation of WAG processes can cause serious errors in the predicted phase behavior, so that the compositional results for large area models can be very misleading.The 4-component Todd and Longstaff (T&L) method provides the basis of an upscaling technique, since it approximately represents the physics of these near-miscible processes, and includes five adjustable parameters to facilitate coarse grid T&L matching. The solutions of the T&L equations are well behaved, including less serious sensitivity to numerical dispersion. The upscaling is achieved systematically from a combination of steps with a final stage based on fine grid compositional reference solutions for small representative elements of the reservoir. Details in reservoir description influence the competition between gravity segregation and channeling due to heterogeneities. We illustrate for two example reservoir descriptions how the best choices of the viscosity and density mixing parameters, ω µ and ω ρ , can be established by a special optimization procedure. A critical matching requirement is to minimize the errors in distributions of gas saturation. A saving of CPU time by a factor of 2000 is demonstrated for upscaled T&L coarse grid solutions in 2D. We make recommendations on how an upgraded T&L method should be used in reservoir studies.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir simulation has become the industry standard for reservoir management. It is now used in all phases of field development in the oil and gas industry. The full field reservoir models that have become the major source of information and prediction for decision making are continuously updated and major fields now have several versions of their model with each new version being a major improvement over the previous one. The newer versions have the latest information (geologic, geophysical and petrophysical measurements, interpretations and calculations based on new logs, seismic data, injection and productions, etc.) incorporated in them along with adjustments that usually are the result of single-well or multi-well history matching. A typical reservoir model consists of hundreds of thousands and in many cases millions of grid blocks. As the size of the reservoir models grow the time required for each run increases. Schemes such as grid computing and parallel processing helps to a certain degree but cannot close the gap that exists between simulation runs and real-time processing. On the other hand with the new push for smart fields (a.k.a. ifields) in the industry that is a natural growth of smart completions and smart wells, the need for being able to process information in real time becomes more pronounced. Surrogate Reservoir Models (SRMs) are the natural solution to address this necessity. SRMs are prototypes of the full field models that can run in fractions of a second rather than in hours or days. They mimic the capabilities of a full field model with high accuracy. These models can be developed regularly (as new versions of the full field models become available) off-line and can be put online for automatic history matching and real-time processing that can guide important decisions. SRMs can efficiently be used for real-time optimization, real-time decision making as well as analysis under uncertain conditions. This paper presents a unified approach for development of SRMs using the state-of-the-art in intelligent systems techniques. An example for developing an SRM for a giant oil field in the Middle East is presented and the results of the analysis using the SRM for this field is discussed. In this example application SRM is used in order to analyze the impact of the uncertainties associated with several input parameters into the full field model.

TX 75083-3836, U.S.A., fax 01-972-952-9435. Abstract

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe viability of miscible WAG injection as an EOR scheme for the Magnus reservoir is under consideration. Performance prediction and optimization under this type of recovery mechanism, in which the residual oil is usually recovered by a multi-contact miscible (MCM) process, rely mostly upon compositional simulation. Unfortunately, numerical dispersion effects, associated with large grid blocks required in field scale compositional simulation of MCM processes, can result in erroneous phase behavior. Reduction of dispersion to acceptable levels may require very small grid blocks, implying model sizes that exceed the capacity of current conventional computer installations. Thus, full field compositional models are not practical for reliable field-wide benefit predictions. This paper presents a systematic procedure that we successfully employed for prediction of field wide performance and recovery benefit of miscible WAG injection for the Magnus reservoir. The procedure involves an extension of the upscaling technique proposed by Fayers et al [1]. It starts with a 3D fine grid compositional sector model of a small representative element of the reservoir. Then, this reference model is upscaled to block sizes corresponding to those in the Magnus full field model (FFM), using the singlephase half-cell upscaling technique. The upscaled model employs the Todd and Longstaff (T&L) formulation [1,2] for simulating MCM displacement and three-pseudo components for representing phase behavior. The PVT, solvent equilibrium constants and miscibility pressure vs. composition tables are developed through matching with a calibrated 12-component equation of state (EOS) and 1D slim tube simulations, for a wide range of pressure and composition. The PVT treatment also allows for vaporization of oil by the contacting gas. We also discuss impacts of various uncertain parameters on the performance of MCM WAG injection, which were investigated using the calibrated upscaled model, taking advantage of about 1000 times gain in CPU time compared to that for the reference fine grid compositional model.