A Numerical Simulation Model for Thermal Recovery Processes

Crookston, R.B.; Culham, W.E.; Chen, W.H.

doi:10.2118/6724-pa

Cited by 144 publications

(84 citation statements)

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“…This parameter defines the time-interval of applicability of (Abdalla and Coats, 1971;Coats, 1976, Vinsome, 1974, and Ito, 1977. The Kvalue simulators (Ferrer and Farouq-Ali, 1977;Crookston et al 1977;Rubin and Vinsome, 1979;Coats, 1978;Abu-Kassem and Aziz, 1982;Luo and Baruffet, 2005;and Huang and Yang, 2007) are a function of only the temperature and pressure and not composition while the EOS based thermal simulators (Bratferger, 1991;Varavei andSepehrnoori, 2009, andHeidari andMaini, 2014) are a function of temperature, pressure and composition of the phases present.…”

Section: Gravity Override Modelsmentioning

confidence: 99%

Upscaling for Thermal Recovery

Abubakar¹,

Muggeridge²,

King³

2014

SPE International Heavy Oil Conference and Exhibition

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Thermal recovery is one of the most widely applied EOR methods worldwide. It is used mainly to improve the recovery of more viscous oils. Heat is used primarily to reduce the viscosity of oil but may also improve recovery by other mechanisms such as oil expansion, steam distillation and reduction in capillary forces. As a result, the prediction of thermal recovery performance requires more complex and computationally demanding numerical simulations than is the case when evaluating a waterflood. The simulator has to evaluate the impact of both heat transfer and compositional behaviour on the fluid flows.In most cases the engineer will have to use coarser grids in the simulation models than would be needed for a waterflood in order to keep simulation times within acceptable limits. However, it has been shown in the modelling of other EOR processes that in fact much finer grids may be needed to capture the frontal dynamics of the displacement than would be the case in waterflooding. As a result, the simulation of steamflooding on the field scale is unlikely to capture the impact of small scale heterogeneity on flow, as well as being severely affected by numerical dispersion. The ideal solution is to use homogenization to capture the effects of sub-grid-block heterogeneity combined with advanced numerical techniques such as dynamic/adaptive gridding or higher order schemes to reduce the impact of numerical diffusion and dispersion. However, such methods are not currently available in the commercial simulators available to most engineers.The alternative is to use upscaling, but as yet there are no upscaling methodologies suitable for thermal oil recovery methods. Upscaling is a way of altering the inputs to numerical flow models so that simulations run on a coarse grid. These will a) predict the impacts on flow of sub-grid heterogeneity (homogenization) and b) will be less affected by numerical dispersion. For waterflooding this is typically achieved by calculating the effective absolute permeability for each coarse grid cell. In many cases, it is also necessary to upscale the well index and the transmissibilities of the well blocks. Upscaling the absolute permeability, the well index and the well block transmissibilities, ensures that the overall pressure drop for single phase flow between wells is correctly predicted in the coarse grid, whilst upscaling the relative permeabilities ensures that the two-phase flow effects (pressure drop, breakthrough time and water cut development) are correctly modelled. Although there is a significant literature on single-phase upscaling and the development of pseudo relative permeabilities for waterflooding applications, these methods do not capture the heat transport or the impact of temperature on the oil mobility. ivIn this research, a combination of pseudoisation procedures (by making reasonable assumptions) with the Buckley-Leverett solution approximated for thermal EOR processes is used to derive analytical pseudo-relative permeabilities for both hot water and steam ...

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Section: Gravity Override Modelsmentioning

confidence: 99%

Upscaling for Thermal Recovery

Abubakar¹,

Muggeridge²,

King³

2014

SPE International Heavy Oil Conference and Exhibition

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“…Heat was assumed to be transported by convection and conduction in the reservoir and by conduction to the underlying and overlying formations. Crookston (1979) and Grabowski (1979) reported on the development of ISCOM, a general purpose thermal simulator which was the predecessor of the commercial code STARS (Steam, Thermal and Advanced Processes Reservoir Simulator) from Computer Modelling Group, Inc. The model operated in three dimensions and included a variable number of components distributed among four phases (water, oil, gas, and solid).…”

Section: A Review Of Isc Modelsmentioning

confidence: 99%

“…We note that thermal reservoir models usually account for heat exchanges with the surroundings, typically through heat conduction to the reservoir cap and base rock (see e.g., Coats (1980), Crookston (1979)). Since in this thesis we focus primarily on one-dimensional problems, we model heat exchanges as a source/sink term in the equation.…”

Section: Boundary Conditionsmentioning

confidence: 99%

Experimental Investigation and High Resolution Simulation of In-Situ Combustion Processes

Gerritsen¹,

Kovscek²

2008

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“…The standard formulation of the in-situ combustion equations include convective mass transfer, convective and conductive heat transfer, kinetically controlled chemical reactions and fluid phases in thermodynamic equilibrium [4,5,6]. The assumption of immediate phase equilibrium implies that the timescale for mass transfer between phases is faster than all other timescales.…”

Section: Motivationmentioning

confidence: 99%

Experimental Investigation and High Resolution Simulator of In-Situ Combustion Processes

Gerritsen¹,

Kovscek²

2005

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Accurate simulation of in-situ combustion processes is computationally very challenging because the spatial and temporal scales over which the combustion process takes place are very small. In the last report, we focused heavily on experimental work. The experimental work is being continued and new combustion tube runs will be discussed in the next quarterly report. In this current and seventh report, we will focus on our research in numerical methods. In the previous reports, we discussed in detail the development of our adaptive parallel pressure solver, which is based on an efficient Cartesian Adaptive Mesh Refinement technique. This methodology allows much higher grid densities to be used near typical fronts than current simulators. Last quarter we improved the computation of upscaled permeabilities. This quarter we derived a method to compute accurate transmissibilities. We present preliminary results in this quarterly report. We have also started the development of a Virtual Kinetic Cell (VKC) that we will use to test numerical methods for solving the kinetics in combination with phase behavior. A discussion of the approach followed is included. .

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A Numerical Simulation Model for Thermal Recovery Processes

Cited by 144 publications

References 0 publications

Upscaling for Thermal Recovery

Upscaling for Thermal Recovery

Experimental Investigation and High Resolution Simulation of In-Situ Combustion Processes

Experimental Investigation and High Resolution Simulator of In-Situ Combustion Processes

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