Understanding reservoir performance and predicting hydrocarbon recovery in carbonate reservoirs are challenging due to the complexity of the pore system and the dynamic interplay of multiphase fluids that move through the pore network. A multiyear study of carbonate reservoirs across a broad spectrum of geologic conditions, fluid types, and field maturities has resulted in key insights on the links between pore-system characteristics and dynamic fluid-flow behavior with material relevance to carbonate resource assessment, field development optimization, and maximizing ultimate recovery. Pore-system heterogeneity is a primary control on hydrocarbon displacement efficiency. Multiphase flow through heterogeneous pore systems with a mix of pore types results in lower recovery than flow through more homogeneous pore systems. Due to the homogeneous nature of the micropore system, rocks dominated by micropores can have favorable hydrocarbon displacement with residual oil saturation to water displacement (Sorw) less than 5%. Rocks with a heterogeneous mix of interparticle and micropores have less favorable displacement, with Sorw as high as 20%, despite having higher permeability. A threshold of approximately 80% microporosity appears to distinguish: (1) more favorable displacement in micropore-dominated rocks vs. less favorable displacement in rocks with a mixed pore system, (2) the magnitude of permeability for a given porosity in mixed vs. micropore systems, and (3) the proportion of microporosity above which pore space of any type is connected exclusively through the micropore network and flow properties reflect the homogeneous nature of that pore system. Within the homogeneous micropore system, Sorw increases from about 5% to 20% as porosity and permeability decrease and micropore type transitions from type 1 (higher quality) to type 2 (lower quality). A major control on multiphase fluid movement in reservoirs with interlayered mixed and micropore-dominated flow units is the contrast in capillary pressure (Pc) and water relative permeability (Krw) between these distinct pore systems. When compared on a consistent basis, 60% water saturation, for instance, rocks with a mixed pore system have approximately neutral (0 psi, 0 kPa) Pc values and higher Krw values, whereas rocks dominated by microporosity have more strongly negative (–6 psi, –41 kPa) Pc values and lower Krw values. In the case of a water flood operation, this contrast in Pc and Krw can lead to more heterogeneous sweep patterns and lower recovery. A new method for tagging in-place oil with xenon was coupled with flow-through micro-computed tomography imaging to directly investigate oil displacement under water flood conditions. The results provide a qualitative demonstration of how brine flooding displaces xenon-saturated oil. Displacement patterns in micropore-dominated rocks are homogeneous and compact with limited bypass of oil, consistent with relatively low Sorw. Conversely, the displacement pattern in rocks with a mixed pore system is more heterogeneous and exhibits significant regions of bypassed oil, consistent with higher Sorw and Krw.

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.