Carbonate reservoirs are often comprised of a heterogeneous pore system within a matrix of variably distributed minerals including anhydrite, dolomite, and calcite. When describing carbonate thin sections, it is routine to assign relative abundance levels to each of these components, which are qualitative to semiquantitative (e.g., point counting) and vary greatly depending on the petrographer. Over the past few decades image analysis has gained wide use among petrographers; however, thin-section characterization using this technique has been primarily limited to quantifying the pore space due to the difficulty associated with optical recognition beyond the blue-dyed epoxy associated with the pores. Here, we present a new method of computerized object-based image analysis (Quantitative Digital Petrography: QDP) that relies on a predefined rule set to enable rapid, automated thin-section quantification with limited interaction of a petrographer. We have developed a novel work flow that automatically isolates the sample on a high resolution (i.e., <1 μm/pixel) scanned thin section, segments the image, and assigns those segments to predefined categories; e.g., pores, cement, and grains. With this technique, statistically relevant numbers of thin sections can be rapidly batch processed and quality controlled, thereby allowing quantitative data from conventional core analysis, special core analysis, and reservoir surveillance to be integrated with the petrographic data for a more dynamic description of the carbonate rock. Our technique can also incorporate multiple layers, such as cross-polarization, backscatter electron imaging, and elemental maps, which allow additional information to be easily integrated with results from QDP. The QDP approach is a significant improvement over previous digital image analysis methods because it (1) does not require binarization, (2) eliminates the subjectivity in assessing abundance levels, (3) requires less interaction with a petrographer, and (4) provides a much fuller dataset that can be incorporated across an entire well or field to better address common challenges associated with carbonate reservoir characterization, such as understanding pore type and cement abundance, pore connectivity, grain distribution, and reservoir flow characteristics.