Summary

With advancements in technology and computational capacity, the method of digital rock physics (DRP) for characterizing the storage and flow properties of a reservoir is gradually taking up the space that was once dominated by conventional methods such as routine core analysis (RCA) and special core analysis (SCAL). Unlike RCA and SCAL, the DRP method provides a nondestructive approach to deal with the core samples, which in a way is also more repeatable, economic, and is a clear improvement over the existing conventional methods in terms of its flexibility to experience the multiphysics of the rock and fluids. However, DRP still has some lacunae because the available algorithms have limitations in handling various challenges of complex lithology, such as grain/pore-boundary transition, soft/hard-matter transition, and imprints of intensity gradients of a 3D structure on 2D slices. Therefore, in this paper, we proposed a new approach to handle multiple issues by optimizing the segmentation algorithms and putting them together to standardize workflows (WFs) for reliable determination of the pore volume (PV), which could be verified with the field observation of porosity obtained using industry-standard laboratory methods and well logs. More emphasis was placed on the adaptability of the WF to deal with varying heterogeneity in the rocks.

In this work, we proposed five WFs and compared them with the standard algorithms (including edge detections, watershed, and global thresholding) in terms of accuracy and computation time for a set of four homogeneous and four heterogeneous samples. We found that WF3 was the one that consistently performed better than all other WFs and some of the popular algorithms when compared one to one. We used a data-conditioning filter, contrast-limited adaptive histogram equalization (CLAHE)—a practice used in medical imagery—for local contrast enhancement in the heterogeneous carbonates to increase the signal/noise ratio of the rock-sample images. It successfully handled the contrast variability caused by the pockets of low illumination in the heterogeneous samples. Its limitation has also been detailed in the paper.

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