Classification of pore or grain types in formation samples from a subterranean formation

A system for classifying pores, grains, or both in a formation sample, the system comprising:

• a non-transitory storage medium;
• and
• at least one processor coupled to the non-transitory storage medium, wherein the at least one processor executes one or more instructions stored on the non-transitory storage medium to:
• receive a digital image representation of a formation sample, wherein the digital image is obtained at least in part from a computer-tomographic (CT) scanner;
• identify a plurality of pores, grains, or both in the digital image representation of the formation sample;
• compute a plurality of geometric features associated with the plurality of pores, grains, or both in the digital image representation of the formation sample;
• input the plurality of geometric features into an unsupervised machine learning model;
• determine, using the unsupervised machine learning model, a label for each identified pore and each identified grain, wherein the label comprises a pore-type for the pore or a grain-type for the grain;
• input the plurality of geometric features and the labels determined for each of the identified pores, grains, or both, into a supervised machine learning model;
• determine, using the supervised machine learning model, a final classification of a pore-type for each pore and a grain-type for each grain identified in the digital image representation of the formation sample and
• based on the determination of a final classification of pore-type for each pore and a grain-type for each grain identified in the digital image representation of the formation sample, performing hydrocarbon production operations in a selected location in the formation.

A non-transitory computer-readable medium storing one or more instructions that, when executed by at least one processor, cause the at least one processor to perform one or more operations comprising:

• receiving a digital image representation of a formation sample, wherein the digital image is obtained at least in part from a computer-tomographic (CT) scanner;
• identifying a plurality of pores, grains, or both in the digital image representation of the formation sample;
• computing a plurality of geometric features associated with the plurality of pores, grains, or both in the digital image representation of the formation sample;
• inputting the plurality of geometric features into an unsupervised machine learning model;
• determining, using the unsupervised machine learning model, a label for each identified pore and each identified grain, wherein the label comprises a pore-type for the pore or a grain-type for the grain;
• inputting the plurality of geometric features and the labels determined for each of the identified pores, grains, or both, into a supervised machine learning model;
• determining, using the supervised machine learning model, a final classification of a pore-type for each pore and a grain-type for each grain identified in the digital image representation of the formation sample;
• and
• based on the determination of a final classification of pore-type for each pore and a grain-type for each grain identified in the digital image representation of the formation sample, performing hydrocarbon production operations in a selected location in the formation.