Inference of Microporosity Phase Properties in Heterogeneous Carbonate Rock With Data Assimilation Techniques

Accurate digital rock modeling of carbonate rocks is limited by the difficulty in acquiring morphological information on small-scale pore structures (microporosity phases in computed tomography images), which provide crucial connectivity and contribute significant surface area during geochemical reactions. However, some carbonate rocks are heterogeneous, and high-resolution scans are resource-intensive, impeding comprehensive sampling of microporosity phases. In this context, we propose using the ensemble smoother with multiple data assimilation (ESMDA) algorithm to infer hard-to-measure properties of microporosity phases from experimental observations for digital rock modeling. The algorithm's effectiveness and applicability are validated through two case studies on mm-scale carbonate rock image data. The first case study applies ESMDA to infer microporosity specific surface area from low-resolution (pixel size 2.5 μ⁢m) images using high-resolution (pixel size 0.1 μ⁢m) 2D scanning electron microscopy (SEM) images of carbonate rock as ground truth. The second case study applies ESMDA to two capillary pressure models to infer the multiphase flow properties of microporosity phases using whole core and phase saturation history from Estaillades drainage image data. In both case studies, ESMDA shows improved performance with increasingly comprehensive observations, while the second case study outperforms recently published techniques. Additionally, ESMDA can assess the consistency between various forward physical models and experimental observations, serving as a diagnostic tool for future characterization. Given the diverse application conditions, we propose that ESMDA can be a general method in the characterization workflow of carbonate rocks.