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Porosity observed in thin section can be objectively classified using a combination of digital acquisition procedures and pattern recognition algorithms. Pore types are derived from the frequency distributions of sizes and shapes of patches of porosity exposed in thin section. Each pore type is represented by a characteristic distribution of sizes and shapes found in thin section. Most sandstone reservoirs contain fewer than six pore types. Much of the intersample variability in reservoir physics is associated with changes in pore type abundance. The advantages of this approach to porosity classification are (1) the criteria for classification are objectively defined, (2) classification procedure is rapid, accurate, and precise, (3) pore types are understood easily in ter s of conventional genetic classification schemes, and (4) pore type data are related strongly to petrophysical properties.
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