Modeling Miscible WAG Injection EOR in the Magnus Field

Abstract

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.

The Magnus Field: extending field life through good reservoir management and enhanced oil recovery

“…The gasisenriched atthe Sullom Voeterminalbyinjection ofpropanea ndb utane.The target molecularw eightfor the injection gasis2 1 ( Lee etal. 2001;Haajizadeh etal. 2001).…”

Development of More-Efficient Gas Flooding Applicable to Shallow Reservoirs

“…Previous research on WAG for immiscible and CO 2 floods has shown that WAG can cause a significant increase in oil recovery primarily because of improved sweep efficiency based on changes in mobility contrasts (Sanchez, 1999;Haajizadeh et al, 2001;Jarrell et al, 2002). WAG may also reduce the amount of gas injection required, although determining the optimum WAG parameters is sometimes difficult and reservoir dependent.…”

“…Stalkup also showed that simultaneous grid refinement in horizontal and vertical 52 directions caused numerical dispersion to overestimate the recovery with gas enrichment beyond the MME (Stalkup et al 1990;Stalkup, 1990; Stalkup, 1987). Other researchers have also examined the effect of viscous crossflow, capillary pressure, diffusion, gravity, heterogeneities, and numerical gridding on recovery (Lim et al, 1997;Lim, 1993;Fayers, 1992; Solano et al, 2001a).Previous research on WAG for immiscible and CO 2 floods has shown that WAG can cause a significant increase in oil recovery primarily because of improved sweep efficiency based on changes in mobility contrasts (Sanchez, 1999;Haajizadeh et al, 2001;Jarrell et al, 2002). WAG may also reduce the amount of gas injection required, although determining the optimum WAG parameters is sometimes difficult and reservoir dependent.…”

Trends in Reservoir Simulation: Big Models, Scalable Models? Will you Please Make up Your Mind?

“…1,2 This trend has been fueled by enhancements in hardware, software and geologic modeling and reservoir simulation technologies. More specifically, the abilities to create large geologic models are being driven by faster computer processors with access to more memory, better methods for representation of complex geology, improvements in surface, framework, geocellular and geostatistical modeling and easier to use software.…”