Abstract: A system and method to partition pores and throats in 2D or 3D scanned tomographic representations. To achieve partitioning, the methods employ a series of techniques that include the Maximum Inscribed Ball technique, Connected Component Labelling technique, and the 3D Binary Morphology technique.

Abstract: A method is provided for automatically classifying grains, pores, or both of a formation sample. The method includes receiving a digital image representation of the formation sample, and identifying a plurality of pores, grains, or both in the digital image representation. The method also includes computing a plurality of geometric features associated with the pores, grains, or both in the digital image representation, and inputting the geometric features into an unsupervised machine learning model. The unsupervised machine learning model determines a label for each identified pore and grain, the label being a pore-type or a grain-type, and the plurality of geometric features and the labels determined for each pore, grain, or both, are input into a supervised machine learning model. The supervised machine learning model determines a final classification of a pore-type for each pore and a grain-type for each grain in the digital image representation of the formation sample.

Abstract: A system and method to partition pores and throats in 2D or 3D scanned tomographic representations. To achieve partitioning, the methods employ a series of techniques that include the Maximum Inscribed Ball technique, Connected Component Labelling technique, and the 3D Binary Morphology technique.