• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.347-354
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• 2009

Physical properties of rocks are strongly dependant on details of pore micro-structures, which can be used for quantifying relations between physical properties of rocks through pore-scale simulation techniques. Recently, high-resolution scan techniques, such as X-ray microtomography and high performance computers make it possible to calculate permeability from pore micro-structures of rocks. We try to extend this simulation methodology to velocity and electrical conductivity. However, the smoothing effect during tomographic inversion creates artifacts in pore micro-structures and causes inaccurate property estimation. To mitigate this artifact, we tried to use sharpening filter and neural network classification techniques. Both methods gave noticeable improvement in pore structure imaging and accurate estimation of permeability and electrical conductivity, which implies that our method effectively removes the smoothing effect in pore structures. However, the calculated velocities showed only incremental improvement. By comparison between thin section images and tomogram, we found that our resolution is not high enough, and it is mainly responsible for the inaccuracy in velocity despite the successful removal of the smoothing effect. In conclusion, our methods can be very useful for pore-scale modeling, since it can create accurate pore structure without the smoothing effect. For accurate velocity estimation, the resolution of pore structure should be at least three times higher than that for permeability simulation.

• 한국지구물리탐사학회:학술대회논문집
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• 2009.10a
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• pp.147-154
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• 2009

Physical properties of rocks, such as velocity, are strongly dependant on detailed pore structures, and recently, pore micro-structures by X-ray tomography techniques have been used to simulate and understand the physical properties. However, the smoothing effect during the tomographic reconstruction procedure often causes an artifact - overestimating the contact areas between grains. The pore nodes near a grain contact are affected by neighboring grain nodes, and are classified into grain nodes. By this artifact, the pore structure has higher contact areas between grains and thus higher velocity estimation than the true one. To reduce this artifact, we tried two image processing techniques - sharpening filter and neural network classification. Both methods gave noticeable improvement on contact areas between grains visually; however, the estimated velocities showed only incremental improvement. We then tried to change the resolutions of tomogram and quantify its impact on velocity estimation. The estimated velocity from the tomogram with higher spatial resolution was improved significantly, and with around 2 micron spatial resolution, the calculated velocity was very close to the lab measurement. In conclusion, the resolution of pore micro-structure is the most important parameter for accurate estimation of velocity using pore-scale simulation techniques. Also the estimation can be incrementally improved if combined with image processing techniques during the pore-grain classification.