Abstract:

Carbonate reservoirs are characterized by storage space of high heterogeneity and complex seepage mechanism.It is difficult to quantitatively characterize the size, shape and connectivity of pores, fractures, and vugs in the carbonate reservoirs.In this study, a new workflow is proposed to identify pore types, quantitatively characterize pore structure, and automatically classify core plugs based on CT scanned images.The CT scanned images of carbonates were pre-processed to perform gray conversion and increase signal-to-noise ratio, and further segmented to binary images containing pores and matrix.Then, the characteristic pore structure parameters were extracted through a series of procedures including morphology analysis and parameter calculation.The support vector machine method was adopted to automatically identify pores, vugs, and fractures in the CT images.Based on the pore identification of all cross-sections through CT images of the core, the coefficient was proposed to identify pore types.Application of the proposed workflow to the M and F reservoirs in the T oil field indicates that the workflow shows excellent accuracy in pore identification, can effectively determin the major pore types in reservoirs, thus is of guiding significance to effective petroleum development.

Key words: CT scanned image, image segmentation, support vector machine, classification index, pore-type, carbonate reservoir, oilfield development