A Screening Model for CO2 Flooding and Storage in Gulf Coast Reservoirs Based on Dimensionless Groups
- Derek J. Wood (The University of Texas at Austin) | Larry W. Lake (The University of Texas at Austin) | Russell T. Johns (The University of Texas at Austin) | Vanessa Nunez (The University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- June 2008
- Document Type
- Journal Paper
- 513 - 520
- 2008. Society of Petroleum Engineers
- 5.3.4 Reduction of Residual Oil Saturation, 6.5.7 Climate Change, 5.4.2 Gas Injection Methods, 5.2.1 Phase Behavior and PVT Measurements, 5.4.9 Miscible Methods, 5.2.2 Fluid Modeling, Equations of State, 5.4 Enhanced Recovery, 5.4.1 Waterflooding, 4.3.4 Scale
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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.
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 z 1*, which can be set equal to H, the height of the reservoir. This new group z/z 1* 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:
RL = [Equation] effective aspect ratio
Mo w = [Equation] mobility ratio (water)
Na = [Equation] dip angle group
No g = [Equation] buoyancy number
NPc = [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.
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Box, G.E.P. and Behnken, D.W. 1960. Some New Three Level Designs for theStudy of Quantitative Variables. Technometrics 2: 455-475.
Box-Behnken designs. 2006. NIST/SEMATECH e-Handbook of Statistical Methods,www.itl.nist.gov/div898/handbook//pri/section3/pri3362.htm.Downloaded 3 February 2006.
Diaz, D., Bassiouni, Z., Kimbrell, W., and Wolcott, J. 1996. Screening Criteria for Application ofCarbon Dioxide Miscible Displacement in Waterflooded Reservoirs ContainingLight Oil. Paper SPE 35431 presented at the SPE/DOE Improved Oil RecoverySymposium, Tulsa, 21-24 April. DOI: 10.2118/35431-MS.
Dobitz, J.K. and Prieditis, J. 1994. A Stream Tube Model for the PC.Paper SPE 27750 presented at the SPE/DOE Improved Oil Recovery Symposium,Tulsa, 17¬-20 April. DOI: 10.2118/27750-MS.
Experimental Design. 2006. Electronic Statistics Textbook, www.statsoft.com/textbook/stathome.html.Downloaded 2006.
Jarrell, P., Fox, C., Stein, M., et al. 2002. Practical Aspects ofCO2 Flooding. Monograph Series, SPE, Richardson, Texas 22.
Lake, Larry W. 1989. Enhanced Oil Recovery. Austin, Texas: Universityof Texas at Austin.
Lane, D.M. 2005. Factorial Design. Hyperstat Online, http://www.davidmlane.com/hyperstat/factorial_ANOVA.html.
NPC (National Petroleum Council) Public Database. 1984. Downloaded from http://www.netl.doe.gov/technologies/oil-gas/Software/database.html.
Paul, G.W., Lake, L.W., and Gould, T.L. 1984. A Simplified Predictive Model forCO2 Miscible Flooding. Paper SPE 13238 presented at the SPEAnnual Technical Conference and Exhibition, Houston, 16-19 September. DOI:10.2118/13238-MS.
Peng, D.Y. and Robinson, D.B. 1976. A New Two-Constant Equation ofState. Industrial and Engineering Chemistry Fundamentals 15(1): 59-64. DOI: 10.1021/i160057a011.
Rivas, O., Embid, S., and Bolivar, F. 1992. Ranking Reservoirs for Carbon DioxideFlooding Processes. SPE Advanced Technology Series 2 (1):95-103. SPE-23641-PA. DOI: 10.2118/23641-PA.
Shook, M., Lake, L.W., and Li, D. 1992. Scaling Immiscible Flow ThroughPermeable Media By Inspectional Analysis. In Situ 16 (4):311-349.
Wood, D. 2006. A Screening Model for CO2 Flooding and Storage inDipping Gulf Coast Reservoirs Based on Dimensionless Groups. MS thesis,University of Texas at Austin: Austin, Texas.