Application of a New Grain-Based Reconstruction Algorithm to Microtomography Images for Quantitative Characterization and Flow Modeling
- Karsten E. Thompson (Louisiana State University) | Clinton S. Willson (Louisiana State University) | Christopher D. White (Louisiana State University) | Stephanie Nyman (The University of Waikato) | Janok P. Bhattacharya (The University of Houston) | Allen H. Reed (Naval Research Laboratory)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2008
- Document Type
- Journal Paper
- 164 - 176
- 2008. Society of Petroleum Engineers
- 4.1.5 Processing Equipment, 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.3.2 Multiphase Flow, 1.14 Casing and Cementing, 5.1 Reservoir Characterisation, 4.3.4 Scale, 1.2.3 Rock properties, 5.5 Reservoir Simulation, 5.6.1 Open hole/cased hole log analysis, 5.3.1 Flow in Porous Media, 5.1.3 Sedimentology, 1.6.9 Coring, Fishing
- 2 in the last 30 days
- 473 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
X-ray computed microtomography (XMT) is used for high-resolution, nondestructive imaging and has been applied successfully to geologic media. Despite the potential of XMT to aid in formation evaluation, currently it is used mostly as a research tool. One factor preventing more widespread application of XMT technology is limited accessibility to microtomography beamlines. Another factor is that computational tools for quantitative image analysis have not kept pace with the imaging technology itself.
In this paper, we present a new grain-based algorithm used for network generation. The algorithm differs from other approaches because it uses the granular structure of the material as a template for creating the pore network rather than operating on the voxel set directly. With this algorithm, several advantages emerge: the algorithm is significantly faster computationally, less dependent on image resolution, and the network structure is tied to the fundamental granular structure of the material. In this paper, we present extensive validation of the algorithm using computer-generated packings. These analyses provide guidance on issues such as accuracy and voxel resolution. The algorithm is applied to two sandstone samples taken from different facies of the Frontier Formation in Wyoming, USA, and imaged using synchrotron XMT. Morphologic and flow-modeling results are presented.
Subsurface transport processes such as oil and gas production are multiscale processes. The pore scale governs many physical and chemical interactions and is the appropriate characteristic scale for the fundamental governing equations. The continuum scale is used for most core or laboratory scale measurements (e.g., Darcy velocity, phase saturation, and bulk capillary pressure). The field scale is the relevant scale for production and reservoir simulation.
Multiscale modeling strategies aim to address these complexities by integrating the various length scales. While pore-scale modeling is an essential component of multiscale modeling, quantitative methods are not as well-developed as their continuum-scale counterparts. Hence, pore-scale modeling represents a weak link in current multiscale techniques.
The most fundamental approach for pore-scale modeling is direct solution of the equations of motion (along with other relevant conservation equations), which can be performed using a number of numerical techniques. The finite-element method is the most general approach in terms of the range of fluid and solid mechanics problems that can be addressed. Finite-difference and finite-volume methods are more widely used in the computational fluid dynamics community. The boundary element method is very well suited for low-Reynolds number flow of Newtonian fluids (including multiphase flows). Finally, the lattice-Boltzmann method has been favored in the porous-media community because it easily adapts to the complex geometries found in natural materials.
A less rigorous approach is network modeling, which gives an approximate solution to the governing equations. It requires discretization of the pore space into pores and pore throats, and transport is modeled by imposing conservation equations at the pore scale. Network modeling involves two levels of approximation. The first is the representation of the complex, continuous void space as discrete pores and throats. The second is the approximation to the fluid mechanics when solving the governing equations within the networks. The positive tradeoff for these significant simplifications is the ability to model transport over orders-of-magnitude larger characteristic scales than is possible with direct solutions of the equations of motion. Consequently, the two approaches (rigorous modeling of the conservation equations vs. network modeling) have complementary roles in the overall context of multiscale modeling. Direct methods will remain essential for studying first-principles behavior and subpore-scale processes such as diffusion boundary layers during surface reactions, while network modeling will provide the best avenue for capturing larger characteristic scales (which is necessary for modeling the pore-to-continuum-scale transition).
This research addresses one of the significant hurdles for quantitative network modeling: the use of high-resolution imaging of real materials for quantitative flow modeling. We focus in particular on XMT to obtain 3D pore-scale images, and present a new technique for direct mapping of the XMT data onto networks for quantitative modeling. This direct mapping (in contrast to the generation of statistically equivalent networks) ensures that subtle spatial correlations present in the original material are retained in the network structure.
|File Size||3 MB||Number of Pages||13|
Al-Raoush, R.I. and Willson, C.S. 2005a. A Pore-scaleInvestigation of a Multiphase Porous Media System. J. Contam.Hydrology 77 (1-2): 67-89. doi: 10.1016/j.jconhyd.2004.12.001.
Al-Raoush, R.I., and Willson, C.S. 2005b. Extraction of physicallyrealistic pore network properties from three-dimensional synchrotron X-raymicrotomography images of unconsolidated porous media systems. Journalof Hydrology 300 (1-4): 44-64. doi:10.1016/j.jhydrol.2004.05.005.
Al-Raoush, R.I., Thompson, K., and Willson, C.S. 2003. Comparison of networkgeneration techniques for unconsolidated porous media. Soil Sci. Soc. Am.J. 67: 1687-1700.
Bakke, S. and Øren, P.E. 1997. 3-D Pore-Scale Modelling ofSandstones and Flow Simulations in the Pore Networks. SPEJ 2(2): 136-149. SPE-35479-PA doi: 10.2118/35479-PA
Balhoff, M.T. and Thompson, K.E. 2004. Modeling the steady flow ofyield-stress fluids in packed beds. AIChE J. 50 (12):3034-3048. doi: 10.1002/aic.10234.
Bryant, S.L., King, P.R., and Mellor, D.W. 1993a. Network model evaluation ofpermeability and spatial correlation in a real random sphere packing.Transport in Porous Media 11 (1): 53-70. doi:10.1007/BF00614635.
Bryant, S.L., Mellor, D.W., and Cade, C.A. 1993b. Physically RepresentativeNetwork Models of Transport in Porous Media. AIChE J. 39 (3):387-396. doi: 10.1002/aic.690390303.
Delerue, J.-F. and Perrier, E. 2002. DXSoil, a library for 3Dimage analysis in soil science. Computers & Geosciences28 (9): 1041-1050. doi: 10.1016/S0098-3004(02)00020-1.
Delerue, J.F., Perrier, E., Yu, Z.Y., and Velde, B. 1999. New algorithms in 3Dimage analysis and their application to the measurement of a spatialized poresize distribution in soils. Phys. Chem. Earth A 24 (7):639-644. doi: 10.1016/S1464-1895(99)00093-9.
Fatt, I. 1956. The NetworkModel of Porous Media: I. Capillary Pressure Characteristics.Trans., AIME 207: 144-159. SPE-574-G.
Finney, J.L. 1970. Random packings and the structure of simple liquids I.The geometry of random close packing. Proc. Roy. Soc. Lond. A319: 479-493.
Gani, M.R. and Bhattacharya, J.P. 2003. Bed-scale facies architecture of anancient delta lobe deposit of the Wall Creek Member, Central Wyoming, U.S.A.AAPG Annual Convention, Salt Lake City, Utah, 11-14 May.
Ioannidis, M.A., Kwiecien, M.J., Chatzis, I., MacDonald, I.F., and Dullien,F.A.L. 1997. Comprehensive PoreStructure Characterization Using 3D Computer Reconstruction and StochasticModeling. Paper SPE 38713 presented at the SPE Annual Technical Conferenceand Exhibition, San Antonio, Texas, 5-8 October.
Lee, K. et al. 2007. Three-dimensional facies architecture andthree-dimensional calcite concretion distributions in a tide-influenced deltafront, Wall Creek Member, Frontier Formation, Wyoming. AAPG Bulletin91 (2): 191-214.
Liang, Z., Ioannidis, M.A., and Chatzis, I. 2000a. Geometric and topologicalanalysis of three-dimensional porous media: Pore space partitioning based onmorphological skeletonization. J. Colloid Interface Sci. 221(1): 13-24. doi: 10.1006/jcis.1999.6559.
Liang, Z., Ioannidis, M.A., and Chatzis, I. 2000b. Permeability andelectrical conductivity of porous media from 3D stochastic replicas of themicrostructure. Chem. Eng. Sci. 55 (22): 5247-5262. doi:10.1016/S0009-2509(00)00142-1.
Lindquist, W.B., Lee, S.-M., Coker, D.A., Jones, K.W., and Spanne, P. 1996.Medial axis analysis of voidstructure in three-dimensional tomographic images of porous media. J.Geophys. Res. 101 (B4): 8297-8310. doi: 10.1029/95JB03039.
Lindquist, W.B., Venkatarangan, A., Dunsmuir, J., and Wong, T.-F. 2000. Pore and throat sizedistributions measured from synchrotron X-ray tomographic images ofFontainebleau sandstones. J. Geophys. Res. 105 (B9):21509-21527. doi: 10.1029/2000JB900208.
Lohman, G. 1998. Volumetric Image Analysis. New York City: John Wiley& Sons.
Lopez, X., Valvatne, P.H., and Blunt, M.J. 2003. Predictive networkmodeling of single-phase non-Newtonian flow in porous media. J. ColloidInterface Sci. 264 (1): 256-265. doi:10.1016/S0021-9797(03)00310-2.
Luchnikov, V.A., Medvedev, N.N., Oger, L., and Troadec, J.-P. 1999. Voronoi-Delaunay analysis ofvoids in systems of nonspherical particles. Phys. Rev. E 59(6): 7205-7212. doi: 10.1103/PhysRevE.59.7205.
Mellor, D.W. 1989. Random close packing (RCP) of equal spheres: structureand implications for use as a model porous medium. PhD thesis, Milton Keynes,U.K.: Open University.
Oh, W. and Lindquist, B. 1999. Image thresholding by indicatorkriging. IEEE Transactions on Pattern Analysis and MachineIntelligence 21 (7): 590-602. doi: 10.1109/34.777370.
Øren, P.-E., Bakke, S., and Arntzen, O.J. 1998. Extending Predictive Capabilities toNetwork Models. SPEJ 3 (4): 324-336. SPE-52052-PA doi:10.2118/52052-PA
Patzek, T.W. 2001. Verificationof a Complete Pore Network Simulator of Drainage and Imbibition.SPEJ 6 (2): 144-156. SPE-71310-PA doi: 10.2118/71310-PA
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. 1992.Numerical Recipes in Fortran 77. New York City: Cambridge UniversityPress.
Reed, A.H. et al. 2005. Quantification of sediment properties andgeoacoustic parameters from pore structure and grain contacts: A microcomputedtomography analyisis of SAX04 sands. Proc., International Conference onBoundary Influences in High-Frequency, Shallow Water Acoustics, University ofBath, Bath, U.K., 5-9 September.
Scheidegger, A.E. 1974. The Physics of Flow Through Porous Media.Toronto, Ontario, Canada: University of Toronto Press.
Seright, R.S., Liang, J.-T., Lindquist, W.B., and Dunsmuir, J.H. 2002. Characterizing DisportationPermeability Reduction Using Synchrotron X-Ray Computed Tomography.SPEREE 5 (5): 355-364. SPE-79717-PA doi: 10.2118/79717-PA
Seright, R.S., Liang, J.-T., Lindquist, W.B., and Dunsmuir, J.H. 2003. Use of X-ray computedmicrotomography to understand why gels reduce relative permeability to watermore than that to oil. J. Pet. Sci. Eng. 39 (3-4): 217-230.doi: 10.1016/S0920-4105(03)00064-0.
Silin, D.B., Jin, G., and Patzek, T.W. 2003. Robust Determination of the PoreSpace Morphology in Sedimentary Rocks. Paper SPE 84296 presented at the SPEAnnual Technical Conference and Exhibition, Denver, 5-8 October. doi:10.2118/84296-MS
Sok, R.M. et al. 2002. Direct and stochasticgeneration of network models from tomographic images; effect of topology onresidual saturations. Transport in Porous Media 46 (2-3):345-371. doi: 10.1023/A:1015034924371.
Talukdar, M.S., Torsaeter, O., Ioannidis, M.A., and Howard, J.J. 2002. Stochasticreconstruction, 3D characterization and network modeling of chalk. J.Pet. Sci. Tech. 35 (1-2): 1-21. doi:10.1016/S0920-4105(02)00160-2.
Thompson, K.E. 2002. Fast androbust Delaunay tessellation in periodic domains. Int. J. Numer. Meths.Eng. 55 (11): 1345-1366. doi: 10.1002/nme.558.
Thompson, K.E. and Fogler, H.S. 1997. Modeling Flow in DisorderedPacked Beds from Pore-Scale Fluid Mechanics. AIChE J. 43 (6):1377-1389. doi: 10.1002/aic.690430602.
Thompson, K.E., Willson, C.S., and Zhang, W. 2006. Quantitative computerreconstruction of particulate materials from microtomography images.Powder Technology 163 (3): 169-182. doi:10.1016/j.powtec.2005.12.016.
Valvatne, P.H. and Blunt, M.J. 2003. Predictive Pore-Scale NetworkModeling. Paper SPE 84550 presented at the SPE Annual Technical Conferenceand Exhibition, Denver, 5-8 October. doi: 10.2118/84550-MS