5 research outputs found
Field Scale Modeling of Tracer Injection in Naturally Fractured Reservoirs using the Random-Walk Simulation
No full textAbstractModeling complex transport processes in naturally fractured reservoirs (NFRs) using classical continuum models may not be practically possible, because using classical algorithms for the detailed structure of fracture-matrix system requires unreasonable computational time. Also, fractured reservoirs are highly heterogeneous, and finite-difference calculations for such models often cause convergence problems. In addition to these, an exact representation of a complex fracture network in classical continuum modeling algorithms is highly difficult. An alternative is to use a non-classical technique known as the Random Walk Particle Tracking (RWPT) algorithm.We showed earlier (Stalgorova and Babadagli, 2009) that the random walk (RW) technique can be adapted to model miscible flooding in a fractured porous medium at the lab scale. The unknown parameters used to match the model results were only diffusion coefficients for oil and solvent, as the diffusive/dispersive transport (effective if fracture and matrix) was coupled with viscous (effective in fracture) and gravity (effective in fracture and matrix) displacement. Advantages of this method over classical simulation are: (1) shorter computational time, which allows avoidance of simplifications, and (2) the ability to model the matrix-fracture diffusion process without any transfer function.In the present paper, we modified this lab scale RW model for field scale applications. For validation, a series of tracer test results from the Midale field in Canada was used. Fracture network model was constructed based on geological data, and then we used the RWPT model to calibrate the fracture network against tracer test results.We performed a sensitivity analysis to identify the importance of different parameters for the simulation results. The new model and observations can be used to validate and calibrate stochastically generated fracture network models and to estimate the EOR performance of NFRs.</jats:p
Modeling Miscible Injection in Fractured Porous Media using Non-classical Simulation Approaches
No full textAbstractThe objective of this paper is to introduce an adaptation of a non-classical simulation method (random walk, RW) for simulation of fully miscible displacement in fractured porous media, and to validate this method using production and visual data obtained from an experimental work.First, the limitations of classical (continuum models) modeling approach in the simulation of miscible displacement in fractured media were identified by matching the numerical and experimental results obtained earlier. Classical simulation yielded reasonable matches for low viscosity oil but failed to capture the flow patterns of heavy oil displacement, especially in the cases of vertical displacement. This was attributed to two reasons: (1) Numerical dispersion and grid size limitations and (2) the random nature of the phenomenon (mainly the viscous fingering process). Beyond that, the classical modeling scheme required the intensive use of "matrix-fracture pseudo transfer parameters" to achieve experimental matching.To overcome these problems, a non-classical modeling approach, the Random Walk (RW) model was adapted. This technique deals with particles (walkers), each of which moves randomly, but the probability of the movement is defined considering the physics of the process. By tracing a large number of particles, one can model the process and have an idea about the transport of injected and displaced fluid in complex systems. The RW technique allows capturing micro heterogeneities, the random nature of the diffusion process and viscous fingering. It also requires less computational time compared to classical simulation methods.The RW model introduced was validated using experimental -visual- data for different oil types, displacement directions (horizontal and vertical), and injection rates. This exercise showed that the model presented here captures the physics of the process and hence, can be extended and used for larger (field) scale processes of miscible displacement in complex fracture networks, which would not be possible with classical finite-difference models.</jats:p
Practical Analytical Model to Simulate Production of Horizontal Wells with Branch Fractures
No full text
First-principles Prediction Of A Metastable Crystalline Phase Of Ga With Cmcm Symmetry
No full textWe report on evidence for the existence of an unknown metastable crystalline phase of gallium by the combination of classical molecular-dynamics (MD) simulations and density-functional theory (DFT) calculations. The MD simulations, based on a modified embedded-atom potential, reveal the unknown crystalline form through a first-order phase transition originating from the Cmca symmetric α-Ga phase under hydrostatic tension. Subsequently, the DFT calculations using two different generalized-gradient approximation functionals are employed to verify its stability and determine its electronic structure. The structure of the orthorhombic phase is described by symmetry group Cmcm and shows a dimer arrangement resembling the α-Ga phase. A first-order phase transition from α-Ga to the unknown phase is estimated to occur at -1.3 GPa. © 2009 The American Physical Society.804Soares, B.F., MacDonald, K.F., Fedotov, V.A., Zheludev, N.I., (2005) Nano Lett., 5, p. 2104. , 10.1021/nl0515652Welnic, W., Botti, S., Reining, L., Wuttig, M., (2007) Phys. Rev. Lett., 98, p. 236403. , 10.1103/PhysRevLett.98.236403Soares, B.F., Jonsson, F., Zheludev, N.I., (2007) Phys. Rev. Lett., 98, p. 153905. , 10.1103/PhysRevLett.98.153905Lencer, D., Salinga, M., Grabowski, B., Hickel, T., Neugebauer, J., Wuttig, M., (2008) Nature Mater., 7, p. 972. , 10.1038/nmat2330Gromnitskaya, E.L., Yagafarov, O.F., Stalgorova, O.V., Brazhkin, V.V., Lyapin, A.G., (2007) Phys. Rev. Lett., 98, p. 165503. , 10.1103/PhysRevLett.98.165503Bosio, L., (1978) J. Chem. Phys., 68, p. 1221. , 10.1063/1.435841Degtyareva, O., McMahon, M.I., Allan, D.R., Nelmes, R.J., (2004) Phys. Rev. Lett., 93, p. 205502. , 10.1103/PhysRevLett.93.205502Schulte, O., Holzapfel, W.B., (1997) Phys. Rev. B, 55, p. 8122. , 10.1103/PhysRevB.55.8122Bosio, L., Defrain, A., Curien, H., Rimsky, A., (1969) Acta Crystallogr., Sect. B: Struct. Crystallogr. Cryst. Chem., 25, p. 995. , 10.1107/S0567740869003360Bosio, L., Curien, H., Dupont, M., Rimsky, A., (1973) Acta Crystallogr., Sect. B: Struct. Crystallogr. Cryst. Chem., 29, p. 367. , 10.1107/S0567740873002530Bosio, L., Curien, H., Dupont, M., Rimsky, A., (1972) Acta Crystallogr., Sect. B: Struct. Crystallogr. Cryst. Chem., 28, p. 1974. , 10.1107/S0567740872005357Defrain, A., (1977) J. Chim. Phys. Phys.-Chim. Biol., 74, p. 851Baskes, M.I., Chen, S.P., Cherne, F.J., (2002) Phys. Rev. B, 66, p. 104107. , 10.1103/PhysRevB.66.104107Plimpton, S., (1995) J. Comput. Phys., 117, p. 1. , 10.1006/jcph.1995.1039Bernasconi, M., Chiarotti, G.L., Tosatti, E., (1995) Phys. Rev. B, 52, p. 9988. , 10.1103/PhysRevB.52.9988Kresse, G., Furthmüller, J., (1996) Comput. Mater. Sci., 6, p. 15. , 10.1016/0927-0256(96)00008-0Kresse, G., Furthmüller, J., (1996) Phys. Rev. B, 54, p. 11169. , 10.1103/PhysRevB.54.11169Kresse, G., Joubert, D., (1999) Phys. Rev. B, 59, p. 1758. , 10.1103/PhysRevB.59.1758Perdew, J.P., Burke, K., Ernzerhof, M., (1996) Phys. Rev. Lett., 77, p. 3865. , 10.1103/PhysRevLett.77.3865Perdew, J.P., Wang, Y., (1992) Phys. Rev. B, 45, p. 13244. , 10.1103/PhysRevB.45.13244Wyckoff, R.W.G., (1962) Crystal Structures, 1. , 2nd ed. (Wiley, New YorkBrandes, E.A., (1983) Metals Reference Book, , Butterworths, LondonRose, J.H., Smith, J.R., Guinea, F., Ferrante, J., (1984) Phys. Rev. B, 29, p. 2963. , 10.1103/PhysRevB.29.2963Wilson, M., McMillan, P.F., (2003) Phys. Rev. Lett., 90, p. 135703. , 10.1103/PhysRevLett.90.135703Heine, V., (1968) J. Phys. C, 1, p. 222. , 10.1088/0022-3719/1/1/32
