Abstract
A procedure is developed to extract numerical features which characterize the pore structure of reservoir rocks. The procedure is based on a set of descriptors which give a statistical description of porous media. These features are evaluated from digitized photomicrographs of reservoir rocks and they characterize the rock grain structure in term of (1) the linear dependency of grey tones in the photomicrograph image, (2) the degree of "homogeneity" of the image and (3) the angular variations of the image grey tone dependencies. On the basis of these textural features, a simple identification rule using piecewise linear discriminant functions is developed for categorizing the photomicrograph images. The procedure was applied to a set of 243 distinct images comprising 6 distinct rock categories. The coefficients of the discriminant functions were obtained using 143 training samples. The remaining (100) samples were then processed, each sample being assigned to one of 6 possible sandstone categories. Eighty-nine per cent of the test samples were correctly identified.
Keywords
Pattern recognition (psychology)DiscriminantArtificial intelligenceLinear discriminant analysisPiecewise linear functionMathematicsHomogeneity (statistics)GeologyImage (mathematics)MineralogyComputer scienceStatisticsGeometry
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Publication Info
- Year
- 1973
- Type
- article
- Volume
- 11
- Issue
- 4
- Pages
- 171-177
- Citations
- 97
- Access
- Closed
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Cite This
Robert M. Haralick,
Karthikeyan Shanmugam
(1973).
Computer Classification of Reservoir Sandstones.
IEEE transactions on geoscience electronics
, 11
(4)
, 171-177.
https://doi.org/10.1109/tge.1973.294312
Identifiers
- DOI
- 10.1109/tge.1973.294312