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Previous HitCarbonateNext Hit Petrophysical Parameters Derived from 3d Images *

 

Mark Knackstedt1, Mahyar Madadi1, Christoph Arns1, Gregor Baechle2, Gregor Eberli2, and Ralf Weger2

 

Search and Discovery Article (2009)

Posted March 20, 2009

 

*Adapted from extended abstract prepared for presentation at AAPG Annual Convention, San Antonio, Texas, April 20-23, 2008

 

1Australian National University, Canberra, Australia ([email protected] )

2Comparative Sedimentology Lab, University of Miami, Miami, FL. ([email protected] ) 

 

Summary

 

Previous HitCarbonateNext Hit rocks are extremely diverse and their pore spaces complex and heterogeneous. Large uncertainties in the petrophysical properties of carbonates are due to wide variations in pore type, pore shape and interconnectivity. Petrophysical properties such as acoustic velocity, Previous HitpermeabilityNext Hit, and resistivity are directly correlated to the amount and type of Previous HitporosityNext Hit, the dominant feature size, and the interconnectivity of different Previous HitporosityNext Hit types. Accurately measuring these attributes requires the quantitative 3D analysis of the pore structure of carbonates. In this article we describe the imaging and analysis of two types of Previous HitcarbonateNext Hit core: a set of vuggy, recrystallized dolostones and a set of oomoldic limestones. The structure and topology of the pore space is accurately determined via micro-CT analysis and the Previous HitporosityNext Hit consistent with experimental data. Acoustic velocity-Previous HitporosityNext Hit, pore connectivity, and Previous HitporosityNext Hit Previous HitpermeabilityNext Hit relationships are derived directly on the image data via numerical simulation and compared with measured data on the same rock. Acoustic velocity:Previous HitporosityNext Hit trends are good. Pore structural properties (pore size, aspect ratios, pore and throat shape and connectivity) are determined. The correlations between pore geometry and topology and elastic and flow properties can be directly probed in a systematic manner. Three dimensional imaging and analysis of Previous HitcarbonateNext Hit core material can provide a basis for more accurate petrophysical modeling and improve Previous HitcarbonateNext Hit reservoir characterization.

 

 

 

 

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uSummary

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Figure Captions

fig01

Figure 1. 2D slices within 3D tomographic images of (upper row) three recrystallized dolostones and (lower row) three oolitic grainstones.

fig02

Figure 2. Comparison of moduli measured for (a) recrystallized dolostones and (b) oolitic grainstones. The results on the full core are shown in yellow and the comparable data for the micro-CT image is shown in orange. Data on the full set of rock types are shown in blue.

fig03

Figure 3. (a) Pore partitioning of sample in upper left of Figure1 shown by different colors. (b) Left; focus on a subset of the pore network in a sub-sample of the entire volume. Right: Pore network of most of the volume of sample; over 250,000 pores and throats are visible. The thickness of the pore connection is a direct measurement of the pore throat.

fig04

Figure 4. Pore partitioning of sample in lower left of Figure1 shown by different colors; (a) shows the partitioning of all pores, while (b) shows the macroscopically connected pores only; a small subset of the pore space is connected; (c) shows the network based analysis of all pores while (d) shows the connected Previous HitporosityNext Hit.

Introduction

 

Many studies have demonstrated the importance of the pore structure in carbonates on petrophysical properties (e.g., Anselmetti and Eberli, 1993, Wang, 1997; Saleh and Castagna, 2004; Kumar and Han, 2005; Rossebø et al., 2005). Traditional pore type classifications describe the pore structures but fail to quantify the pore system for correlations to the rock’s physical properties. In order to quantitatively describe 2-D pore size, pore surface roughness, aspect ratio, and pore network complexity in carbonates, a digital image analysis (DIA) methodology was developed that produces repeatable quantitative pore shape parameters (Weger, 2006). Each of these quantitative parameters describes a certain aspect of the pore shape. When these parameters are compared to acoustic data, the two DIA parameters that capture the pore complexity and the pore size plus the amount of microporosity prove to be the most influential for the acoustic behavior of the samples. Each of these parameters explains about 60% of the variations in velocity at similar Previous HitporosityNext Hit (Baechle et al., 2004; Weger et al., 2005, Weger 2006). These 2D studies have added much to the understanding of the influence of the pore structure on acoustic properties, yet their 2D nature is a limiting factor for a comprehensive mathematical treatment of pore shapes in simulations of acoustic properties.

 

There is now an opportunity to image and characterise the pore structure of cores in 3D. This is based on coupling high resolution x-ray micro-tomography and high end computational software methods, including visualizing core material at the pore scale in 3D, measuring structural properties, and directly Previous HitpredictingNext Hit physical properties directly from digitised 3D images (Arns, 2004; Arns, 2005) . This allows one to extend previous 2D studies to the 3D pore scale structure. In this paper we describe the 3D study of a set of Previous HitcarbonateNext Hit samples including pore partitioning and simulation of the elastic properties directly on image data. Predictions from digital images are directly compared to measured data.

 

Results

 

The investigated samples are three recrystallized dolostones and three oolitic grainstones with moldic pores and variable amounts of microporosity. These samples were considered as they are end members in regards to pore structure. In all samples the 2-D pore structure was analyzed using the DIA methodology. In addition, Previous HitporosityNext Hit, Previous HitpermeabilityNext Hit and the elastic properties were measured on the samples. 3D micro-CT scans (2.8 micron per voxel) were obtained on sub-samples of 5.5 mm in diameter drilled out of the original plug. Slices from images are shown in Figure 1. Microporosity is estimated by analyzing the attenuation of the x-ray signal, and its structure is correlated to analysis of SEM images at higher (down to 100 nm) resolution.

 

In the recrystallized dolostone samples, the separation between pores and grains (crystals) is easily achieved. In the moldic limestones two pore size populations are detected; large, partly leached, round pores and small pores (1-2 micron). In these samples mercury injection indicates presence of micro- or disconnected Previous HitporosityNext Hit (Figure 1 ). The volume of the microporosity in these three samples is 0.3% of 14.1%, 8.3% of 24.8%, and 10.4% of 19.5% total Previous HitporosityNext Hit. The Previous HitporosityNext Hit is calculated directly from the image data. In all samples the image Previous HitporosityNext Hit is comparable to the measured Previous HitporosityNext Hit.

 

From the 3D images the elastic properties of the samples are calculated using an elastic simulation (FEM) that takes into account both the solid matrix and the microporosity (Arns, 2002). Voxels in the solid matrix are assigned values of the elastic moduli for calcite (K,G = 63.7, 31.2 GPa) and the elastic properties in the microporous regions are based on the effective medium theory for sintered granular like structure in which the bulk (K) and shear modulus (G) are a function of Previous HitporosityNext Hit. Comparison of the simulated and measured sonic velocities are given in Figure 2 . For both the re-crystallized dolostones and moldic limestones, the calculated sonic velocity:Previous HitporosityNext Hit (Vp:Previous HitporosityNext Hit) does compare very well with the measured velocity.

 

Macro/Microporosity and Macropore Phase Connectivity

 

The three dolostone samples exhibit 100% connected Previous HitporosityNext Hit; in Figure 3 we show an example of the pore partitioning within a slice of one dolostone sample and a 3D network structure for the same sample. The 3D volume allows displaying the pore connectivity. In the sample a number of pore structural parameters can be measured; for example, the average pore connectivity is measured as 15.7, meaning that a pore is connected to 15 other pores. The maximum pore connectivity is 83. Connectivity, pore size information and pore shape can be directly used to calculate Previous HitpermeabilityNext Hit and multiphase flow properties (Arns, 2005).

 

The oolitic limestone samples exhibit both resolvable macroscopic Previous HitporosityNext Hit and significant microporosity. Tests for connectivity of the resolved macroporous phase in the oolitic systems show that only one system is macroscopically connected at image resolution (see Figure 4 ). The other two samples exhibit disconnected macropores at the resolution of the image.

 

Conclusion

 

The results of the 3D imaging and analysis study show that the structure and petrophysical properties of Previous HitcarbonateNext Hit core can be quantified in 3D. Specific conclusions include:

 

1. High-resolution CT scans with a resolution of 2.5 microns provide a 3D quantitative structure of Previous HitcarbonateNext Hit samples. Image Previous HitporosityNext Hit correlates well with measured plug Previous HitporosityNext Hit.

2. Calculated sonic velocities from FEM simulation based on information from high-resolution CT scans compare well to measured sonic velocities.

3. Recrystallized dolostones exhibit well connected Previous HitporosityNext Hit which can be directly quantified in 3D. The pore space topology and pore size information is directly measurable. Moldic rocks exhibit significant proportions of microporosity (Previous HitporosityNext Hit below image resolution of ~5 microns). Only one of three moldic samples exhibits connected Previous HitporosityNext Hit in 3D.

4. Previous HitPermeabilityNext Hit:Previous HitporosityNext Hit crossplots for the same rock type will be undertaken. Correlation of Previous HitpermeabilityNext Hit to key geometrical attributes (e.g., pore connectivity and pore to throat aspect ratios) and to acoustic data will be undertaken.

 

References

 

Anselmetti, F.S., and Eberli, G.P. 1993, Controls on sonic velocity in carbonates: Pure and Applied Geophysics, v. 141, p. 287-323.

 

Arns, C.H., Bauget, F., Limaye, A., Sakellariou, A., Senden, T.J., Sheppard, A.P., Sok, R.M., Pinczewski,W.V., Bakke, S., Berge, L.I., Oren, P.E., and Knackstedt, M.A., 2005, Pore scale characterization of carbonates using micro x-ray ct: SPE Journal, p. 475-484.

 

Arns, C.H., Knackstedt, M.A., Pinczewski, W.V., and Garboczi, E.G., 2002, Computation of linear elastic properties from microtomographic images: Methodology and agreement between theory and experiment: Geophysics, v. 67(5), p. 1396-1405.

 

Arns, C., Bauget, F., Ghous, A., Sakellariou, A., Senden, T.J., Sheppard, A.P., Sok, R.M., Pinczewski, W.V., Kelly, J., and Knackstedt, M.A., 2005, Digital core laboratory: Petrophysical analysis from 3D imaging of reservoir core fragments, Petrophysics, v. 46(4), p. 260-277.

 

Baechle, G.T., Weger, R., Massaferro, J.L., and Eberli, G.P., 2004, The role of macroporosity and microporosity in constraining uncertainties and in relating velocity to Previous HitpermeabilityNext Hit in Previous HitcarbonateNext Hit rocks: Ann. Internat. Mtg. Soc. Expl. Geophys.; Expanded Abstracts, Denver Colorado.

 

Kumar, M., and Han, D.-h., 2005, Pore shape effect on elastic properties of Previous HitcarbonateNext Hit rocks: SEG Technical Program Expanded Abstracts, p. 1477-1480.

 

Rossebø, Ø.H., Brevik, I., Ahmadi, G.R., and Adam, L., 2005, Modeling of acoustic properties in Previous HitcarbonateNext Hit rocks: SEG Technical Program Expanded Abstracts, p. 1505-1508.

 

Wang, Z., 1997, Seismic properties of Previous HitcarbonateTop rocks: Geophysical Development Series, v. 6, p. 29-52.

 

Weger R.J., 2006, Quantitative pore/rock type parameters in carbonates and their relationship to velocity deviations: Ph.D. dissertation thesis, University of Miami, RSMAS, Miami, USA, 232p.

 

Weger, R.J., Baechle, G.T., Masaferro, J.L., and Eberli, G.P., 2004, Effects of porestructure on sonic velocity in carbonates: SEG Expanded Abstracts, v. 23, p. 1774.

 

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