Pore Scale Characterization of Carbonates Using X-Ray Microtomography
- Christoph H. Arns (Australian National U.) | Fabrice Bauget (Australian National U.) | Ajay Limaye (Australian National U.) | Arthur Sakellariou (Australian National U.) | Timothy Senden (Australian National U.) | Adrian Sheppard (Australian National U.) | Robert Martin Sok (Australian National U.) | Val Pinczewski (U. of New South Wales) | Stig Bakke (Statoil) | Lars Inge Berge (Statoil) | Paul E. Oren (Numerical Rock Inc.) | Mark A. Knackstedt (Australian National U.)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2005
- Document Type
- Journal Paper
- 475 - 484
- 2005. Society of Petroleum Engineers
- 1.8 Formation Damage, 4.3.4 Scale, 5.3.1 Flow in Porous Media, 2.4.3 Sand/Solids Control, 5.6.1 Open hole/cased hole log analysis, 5.3.2 Multiphase Flow, 4.1.2 Separation and Treating, 4.1.5 Processing Equipment, 5.5.3 Scaling Methods, 1.6.9 Coring, Fishing, 5.8.7 Carbonate Reservoir, 5.1 Reservoir Characterisation, 1.2.3 Rock properties, 7.5.3 Professional Registration/Cetification
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A reservoir carbonate core plug has been imaged in 3D across a range oflength scales using high-resolution X-ray microtomography. Data from theoriginal 40-mm diameter plug was obtained at the vug scale and allows the size,shape, and spatial distribution of the disconnected vuggy porosity to bemeasured. Within the imaged volume over 32,000 separate vugs are identified anda broad vug size distribution is measured. Higher resolution images on subsetsof the plug exhibit interconnected porosity and allow one to measurecharacteristic, intergranular pore size. Pore scale structure and petrophysicalproperties (permeability, drainage capillary pressure, formation factor, andNMR response) are derived directly on the highest resolution tomographicdataset. We show that data over a range of porosity can be computed from asingle plug fragment. Data for the carbonate core is compared to resultsderived from 3D images of clastic cores and strong differences noted.Computations of permeability are compared to conventional laboratorymeasurements on the same core material with good agreement. This demonstratesthe feasibility of combining digitized images with numerical calculations topredict properties and derive cross-correlations for carbonate lithologies.
Carbonate reservoirs contain more than 50% of the world's hydrocarbonreserves. In carbonate rocks, the processes of sedimentation and diagenesisproduce microporous grains and a wide range of pore sizes, resulting in acomplex spatial distribution of pores and pore connectivity. A reliablepetrophysical interpretation for predicting the transport properties andproducibility of carbonates is lacking.
Much of the poor reliability in estimating carbonate properties is due tothe diverse variety of pore types observed in carbonates. Unlike sandstones,many carbonate sediments have a bi- or tri-modal pore size distribution withorganisms playing an important role in forming the reservoirs. Carbonate rocksare further complicated by the significant diagenesis occurring throughchemical dissolution, reprecipitation, dolomitization, fracturing, etc. Forthese reasons the size and shape of any porous network is expected to be veryheterogeneous and exhibit pore sizes ranging from sub-micron to meters.Excluding fractures, three qualitatively different contributors to porosity canbe identified: Vuggy porosity (r = 100 µm), intergranular (r = 5 µm) andintragranular (r < 5 µm).1 The sizes associated with the three types ofporosity may vary across studies and are given as indicative values only. Thesefeatures distinguish the petrophysical properties and productivity of carbonatefields from other sedimentary rocks including sandstones and shales.
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1. Ramakrishnan, T.S. et al.: AModel-Based Interpretation Methodology for Evaluating Carbonate Reservoirs, paper SPE 71704 presented at the 2001 SPE Annual Technical and Exhibition,New Orleans, 30 September-3 October.
2. Arns, C.H. et al.: Virtual permeametry on microtomographic images, J.Petroleum Sci. and Eng. (2004) 45, 41.
3. Knackstedt, M.A. et al.: Digital Core Laboratory: Propertiesof Reservoir Core Derived From 3D Images , paper SPE 87009 presented at the2004 Asia Pacific Conference on Integrated Modelling for Asset Management,Kuala Lumpur, 29-30 March.
4. Arns, C.H. et al.: Petrophysical properties derived from X-ray CT images,APPEA Journal (2003) 43, 557.
5. Knackstedt, M.A. et al.: Virtual Core Lab: Properties of reservoir rockderived from x-ray CT images, SEG Technical Program, Society of ExplorationGeophysicists, Dallas, Texas (October 2003).
6. Hicks, P.J., Deans, H.A. and Narayanan, K.R.: Distribution of Residual Oil inHeterogeneous Carbonate Cores Using X-Ray CT , SPEFE (September 1992) 235;Trans., AIME, 293.
7. Sakellariou, A. et al.: X-ray tomography for mesoscale physicsapplications, Physica A (2004) 339, 152.
8. Feldkamp, L.A., Davis, L.C. and Kress, J.W.: Practical Cone BeamAlgorithm, J. Opt. Soc. America A (1984) 1, 612.
9. Oh, W. and Lindquist, W.B.: Image thresholding by indicator kriging, IEEETransactions on Pattern Analysis and Machine Intelligence (1999) 21, 590.
10. Hilpert, M. and Miller, C.T.: Pore-morphology based simulation ofdrainage in totally wetting porous media, Advances in Water Resources (2001)24, 243.
11. Thovert, J.F. et al.: Grain reconstruction of porous media: Applicationto a low-porosity Fontainebleau sandstone, Phys. Rev. E (2001) 63, 061307.
12. Coles, M.E. et al.: Developments in Synchrotron X-RayMicrotomography with Applications to Flow in Porous Media, SPEREE (August1998) , 288.
13. Qian, Y.H., d'Humieres, D. and Lallemand, P.: Lattice BKG models forNavier-Stokes equation, Europhys. Lett. (1986) 2, 291.
14. Martys, N.S. and Chen, H.: Simulation of multicomponent Fluids incomplex three-dimensional geometries by the lattice Boltzmann method, Phys.Rev. E (1996) 53, 743.
15. Ferreol, B. and Rothman, D.: Lattice-Boltzmann Simulations of FlowThrough Fontainebleau Sandstone, Transport in Porous Media (1995) 20, 3.
16. Martys, N.S. et al.: Large scale simulations of single and multicomponent Flow in porous media, SPIE (1999) 309, 403.
17. Chen, S. et al.: Lattice gas automata for flow through porous media,Physica (1989) 47D, 72.
18. Frisch, U., Hasslacher, B. and Pomeau, Y.: Lattice-gas automata forNavier-Stokes equations, Phys. Rev. Lett. (1986) 56, 1505.
19. Rothman, D.: Cellular-automaton Fluids: A model for Flow in porousmedia, Geophysics (1988) 53, 509.
20. Mendelson, K.S.: Percolation model of nuclear magnetic relaxation inporous media, Phys. Rev. B (1990) 41, 562.
21. Stark, P. and Parker, R.: Bounded-Variable Least-Squares: an Algorithmand Applications, Computational Statistics (1995) 10, 129.
22. Lawson, C.L. and Hansen, R.J.: Solving Least Squares Problems,Prentice-Hall, New York City (1974).
23. Auzerias, F.M. et al.: Transport in Sandstone: A study based on threedimensional microtomography, Geophys. Res. Lett. (1996) 23, 705.
24. Arns, C.H. et al.: Computation of linear elastic properties frommicrotomographic images: Methodology and agreement between theory andexperiment, Geophysics (2002) 67, 1396.
25. Arns, C.H. et al.: Accurate Computation of transport properties frommicrotomographic images, Geophysical Research Letters (2001) 28, 3361.
26. Stauffer, D. and Aharony, A.: Introduction to Percolation Theory, secondedition, Taylor and Francis, London (1994).
27. Lucia, F.J.: Carbonate Reservoir Characterisation, Springer-Verlag(1999).
28. Babadagli, T. and Al-Salmi, S.: Improvement of PermeabilityPrediction for Carbonate Reservoirs Using Well Log Data, paper SPE 77889presented at the 2002 Asia Pacific Oil and Gas Conference and Exhibition,Melbourne, 8-10 October.
29. Publication SMP-7006: Log Interpretation Charts, Schlumberger Pty. Ltd.(1984).
30. Moctezuma-Berthier, A., Vizika, O. and Adler, P.M.: MacroscopicConductivity of Vugular Porous Media, Transport in Porous Media (2002) 49,313.
31. Archie, G.E.: The Electrical Resistivity Log as an Aid in Determiningsome Reservoir Characteristics, Trans. AIME (1942) 146, 54.
32. Moss, A.K., Jing, X.D. and Archer, J.S.: Laboratory investigation ofwettability and hysteresis effects on resistivity index and capillary pressurecharacteristics, Journal of Petroleum Science and Engineering (1999) 24,231.
33. Bekri, S. et al.: Electrical Resistivity Index in Multiphase Flowthrough porous media, Transport in Porous Media (2003) 51, 41.
34. Hürlimann, M.D. et al.: Restricted Diffusion in Sedimentary Rocks.Determination of Surface-Area-to-Volume Ratio and Surface Relaxivity, J. Magn.Res. A (1994) 111, 169.
35. Øren, P.E. et al.: Numerical Simulations of NMRResponses for Improved Interpretations of NMR Measurements in ReservoirRocks, paper SPE 77398 presented at the 2002 SPE Annual TechnicalConference and Exhibition, San Antonio, Texas, 29 September-2 October.
36. Song, Y.Q., Ryu, S. and Sen, P.N.: Determining multiple length scales inrocks, Nature (2000) 406, 178.
37. Arns, C.H.: A comparison of pore size distributions derived by NMR andX-ray CT techniques, Physica A (2004) 339, 159.
38. Banavar, J.R. and Schwartz, L.M.: Magnetic Resonance as a Probe ofPermeability in Porous Media, Phys. Rev. Lett. (1987) 58, 1411.
39. Kenyon, W.E. et al.: AThree Part Study of NMR Longitudinal Relaxation Properties of Water SaturatedSandstones, SPEFE (September 1988) 622.
40. Katz, A.J. and Thompson, A.H.: Prediction of rock electricalconductivity from mercury injection experiments, J. Geophys. Res. (1987) 92,599.
41. Turner, M. et al.: Three Dimensional imaging of Multiphase Flow inporous media, Physica A (2004) 339, 166.