SPE Reservoir Evaluation & Engineering
Volume 11, Number 3, June 2008, 513-520

Summary

Concerns over global warming have led to interest in removing greenhouse gases, specifically CO₂, from the atmosphere. Sequestration of CO₂ in oil reservoirs as part of enhanced oil recovery (EOR) projects is one method that is being considered.

This paper first presents the scaling groups necessary to describe CO₂ flooding for a typical line-drive pattern and then uses these groups in a Box-Behnken experimental design to create a screening model most applicable to candidate Gulf Coast reservoirs (Box and Behnken 1960). By generating oil recovery and CO₂ storage curves, the model estimates the cumulative oil recovery and CO₂ storage potential for a given reservoir. Past screening models—Rivas et al. (1992) and Diaz et al. (1996)—focused only on oil recovery and simply assigned qualitative rankings to reservoirs. Models that did include quantitative results, including CO₂ Prophet (Dobitz and Prieditis 1994) and the CO2 Predictive Model (Paul et al. 1984), did not include the effects of dip. This model focuses on both oil recovery and CO₂ storage potential, produces quantitative results for each, and includes the effects of dip.

This model quickly estimates the oil recovery and CO₂ storage potential for a reservoir. Operators can quickly screen large databases of reservoirs to identify the best candidates for CO₂ flooding and storage. The scaling groups also provide the basis for future models that may be more specific to other regions.

The results show that continuous CO₂ flooding can be fully described using 10 dimensionless groups: aspect ratio, dip angle group, water and CO₂ mobility ratios, buoyancy number, dimensionless injection and producing pressures, residual oil saturation to water and gas, and initial oil saturation. The effects of capillary forces and dispersion were secondary effects in this model and were not included in the scaling. Dimensionless oil recovery was effectively modeled with the dimensionless oil breakthrough time and the dimensionless recovery at three different dimensionless times, while CO₂ storage potential was calculated only at the final dimensionless time. The reservoir-specific parameters discussed above were calculated from response surface fits. The scaling does not work as well at small buoyancy numbers; however, it is effective in the range of values typical of Gulf Coast reservoirs.

Introduction

CO₂ flooding is a popular EOR technique; however, it has not heretofore been scaled for dipping reservoirs. Scaling is done using a process called inspectional analysis. In this process, the equations governing fluid flow in a reservoir are described and then converted into dimensionless equations. For example, the variable z (distance in the vertical direction) can be transformed into a dimensionless variable by dividing by a scalar parameter z₁*, which can be set equal to H, the height of the reservoir. This new group z/z₁* is dimensionless. These transformations are made until the equations are entirely in dimensionless form. Then, through various assumptions and mathematical manipulations of the equations, dimensionless terms are canceled out and removed until a final group of independent dimensionless groups is extracted from the equations.

Using inspectional analysis, Shook et al. (1992) scaled waterfloods for a homogeneous, 2D, cartesian, dipping reservoir with two phases (oleic and aqueous) present and found five necessary dimensionless groups. They are:

R_L = [Equation] effective aspect ratio
M^o_w = [Equation] mobility ratio (water)
N_a = [Equation] dip angle group
N^o_g = [Equation] buoyancy number
N_Pc = [Equation] capillary number

These groups served as the initial basis for the scaling of CO₂ flooding; however, they proved insufficient. This paper presents the additional groups necessary to scale CO₂ flooding.

The desire to undertake CO₂ flooding begets the need to identify economically attractive candidate reservoirs. Comprehensive simulations may be too costly and time-consuming when large databases of reservoirs must be evaluated. This paper presents a model based on the aforementioned dimensionless groups that quickly estimates the oil recovery and CO₂ storage potential for candidate reservoirs.

© 2008. Society of Petroleum Engineers

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History

• Original manuscript received: 17 February 2006
• Meeting paper published: 22 April 2006
• Revised manuscript received: 26 January 2008
• Manuscript approved: 11 February 2008
• Version of record: 20 June 2008