Investigation of Anisotropic Mixing in Miscible Displacements
- Olaoluwa O. Adepoju (The University of Texas at Austin) | Larry W. Lake (The University of Texas at Austin) | Russell T. Johns (The Pennsylvania State University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- January 2013
- Document Type
- Journal Paper
- 85 - 96
- 2013. Society of Petroleum Engineers
- 4.3.4 Scale, 5.4.9 Miscible Methods, 5.7.2 Recovery Factors
- 0 in the last 30 days
- 678 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Dispersion (or local mixing) degrades miscibility in miscible flood displacements by interfering with the transfer of intermediate components that develop miscibility. Dispersion, however, also can improve oil recovery by increasing sweep efficiency. Either way, dispersion is an important factor in understanding miscible-flood performance. This paper investigates longitudinal and transverse local mixing in a finite-difference compositional simulator at different scales (both fine and coarse scale) using a 2D convection-dispersion model. All simulations were of constant-mobility and -density, first-contact miscible flow. The model allows for variations of velocity in both directions. We analyzed local (gridblock) concentration profiles for various miscible-displacement models with different scales of heterogeneity and permeability autocorrelation lengths. To infer dispersivity, we fitted an analytical 2D convection-dispersion model to the local concentration profile to determine local longitudinal and transverse dispersivities simultaneously. Streamlines of simulation models were traced using the algorithm proposed by Pollock (1988). To our knowledge, this is the first systematic attempt to numerically study local transverse dispersivity. The results show that transverse mixing, which is usually neglected in the 1D convection-dispersion model of dispersion, is significant when the flow direction changes locally as a result of heterogeneity. The computed streamlines, which highlight the variation in flow directions, agree with the computed transverse dispersivity trends. We find that both transverse and longitudinal dispersion can grow with travel distance and that there are several instances in which transverse dispersion is the larger of the two. Often, the variations in the streamlines are suppressed (homogenized) during upscaling. This paper gives a quantitative and systematic procedure to estimate the degree of transverse mixing (dispersivity) in any model. We conclude that local mixing, including transverse mixing, should be considered when upscaling a fine-scale model for miscible displacement to ensure proper preservation of fine-scale sweep and displacement efficiency and ultimate oil recovery for miscible-displacement simulations.
|File Size||3 MB||Number of Pages||12|
Arya, A., Hewett, T.A., Larson, R.G. et al. 1988. Dispersion and ReservoirHeterogeneity. SPE Res Eng 3 (1): 139-148. SPE-14364-PA. http://dx.doi.org/10.2118/14364-PA.
Bear, J. 1972. Dynamics of Fluids in Porous Media. New York:Elsevier.
Begg, S.H., Carter, R.R., and Dranfield, P. 1989. Assigning EffectiveValues to Simulator Gridblock Parameters for Heterogeneous Reservoirs. SPERes Eng 4 (4): 455-463. SPE-16754-PA. http://dx.doi.org/10.2118/16754-PA.
Cleary, R.W. and Ungs, M.J. 1978. Analytical Models for GroundwaterPollution and Hydrology. Technical Report 78-WR-15, ASIN B0006Y1D4C, WaterResources Program, Department of Civil Engineering, Princeton University,Princeton, New Jersey.
Durlofsky, L.J., Behrens, R.A., Jones, R.C. et al. 1996. Scale Up ofHeterogeneous Three Dimensional Reservoir Descriptions. SPE J. 1 (3): 313-326. SPE-30709-PA. http://dx.doi.org/10.2118/30709-PA.
Fanchi, J.R. 1983. Multidimentional Numerical Dispersion. SPE J. 23 (1): 143-151. SPE-9018-PA. http://dx.doi.org/10.2118/9018-PA.
Garmeh, G., Johns, R.T., and Lake, L.W. 2009. Pore-Scale Simulation ofDispersion in Porous Media. SPE J. 14 (4): 559-567.SPE-110228-PA. http://dx.doi.org/10.2118/110228-PA.
Haajizadeh, M., Fayers, F.J., and Cockin, A.P. 2000. Effects of PhaseBehavior, Dispersion and Girding on Sweep Patterns for Nearly Miscible GasDisplacement. Presented at the SPE Annual Technical Conference, Dallas, 1-4October. SPE-62995-MS. http://dx.doi.org/10.2118/62995-MS.
Haajizadeh, M., Fayers, F.J., Cockin, A.P. et al. 1999. On the Importance ofDispersion and Heterogeneity in the Compositional Simulation of Miscible GasProcesses. Presented at the SPE Asia Pacific Improved Oil Recovery Conference,Kuala Lumpur, 25-26 October. SPE-57264-MS. http://dx.doi.org/10.2118/57264-MS.
Jennings, J.W. Jr., Ruppel, S.C., and Ward, W.B. 2000. GeostatisticalAnalysis of Permeability Data and Modeling of Fluid-Flow Effects in CarbonateOutcrops. SPE Res Eval & Eng 3 (4): 292-303.SPE-65370-PA. http://dx.doi.org/10.2118/65370-PA.
Jha, R.K., Bryant, S.L., Lake, L.W. et al. 2006. Investigation of pore-scale(local) mixing. Presented at the SPE Improved Oil Recovery Symposium, Tulsa,22-26 April. SPE-99782-MS. http://dx.doi.org/10.2118/99782-MS.
Jha, R.K., John, A., Bryant, S.L. et al. 2009. Flow Reversal andMixing. SPE J. 14 (1): 41-49. SPE-103054-PA. http://dx.doi.org/10.2118/103054-PA.
John, A.K., Lake, L.W., Bryant, S. et al. 2010. Investigation ofMixing in Field-Scale Miscible Displacements Using Particle-TrackingSimulations of Tracer Floods With Flow Reversal. SPE J. 15 (3):598-609. SPE-113429-PA. http://dx.doi.org/10.2118/113429-PA.
Johns, R.T., Pashupati, S., and Subramanian, S.K. 2000. Effect of GasEnrichment Above the MME on Oil Recovery in Enriched-Gas Floods. SPE J. 5 (3): 331-338. SPE-65704-PA. http://dx.doi.org/10.2118/65704-PA.
Lake, L.W. 1989. Enhanced Oil Recovery. Englewood Cliffs, New Jersey:Prentice Hall.
Lantz, R.B. 1971. Quantitative Evaluation of Numerical Diffusion (TruncationError). SPE J. 11 (3): 315-320. SPE-2811-PA. http://dx.doi.org/10.2118/2811-PA.
Mahadevan, J., Lake, L.W., and Johns, R.T. 2003. Estimation of TrueDispersivity in Field-Scale Permeable Media. SPE J. 8 (3):272-279. SPE-86303-PA. http://dx.doi.org/10.2118/86303-PA.
Parakh, H. and Johns, R.T. 2004. Use of Stripping Ratios to IdentifyDispersion Levels and Displacement Mechanisms in Miscible Gas Floods. Presentedat the SPE Annual Technical Conference and Exhibition, Houston, 26-29September. SPE-90854-MS. http://dx.doi.org/10.2118/90854-MS.
Pickens, J.F. and Grisak, G.E. 1981. Modeling of scale-dependent dispersionin hydrogeologic systems. Water Resour. Res. 17 (6):1701-1711. http://dx.doi.org/10.1029/WR017i006p01701.
Pollock, D.W. 1988. Semianalytical Computation of Path Lines forFinite-Difference Models. Ground Water 26 (6): 743-750. http://dx.doi.org/10.1111/j.1745-6584.1988.tb00425.x.
Qi, D. and Hesketh, T. 2005. An Analysis of Upscaling Techniques forReservoir Simulation. Petroleum Science and Technology 23(7-8): 827-842. http://dx.doi.org/10.1081/lft-200033132.
Solano, R., Johns, R.T., and Lake, L.W. 2001. Impact of ReservoirMixing on Recovery in Enriched-Gas Drives Above the Minimum MiscibilityEnrichment. SPE Res Eval & Eng 4 (5): 358-365.SPE-73829-PA. http://dx.doi.org/10.2118/73829-PA.
Yang, A.P. 1990. Stochastic heterogeneity and dispersion. PhD thesis,University of Texas at Austin, Austin, Texas (January 1990).