Determining reservoir formation properties

A computing system, comprising:

• one or more memory modules that store instructions;
• and
• one or more hardware processors communicably coupled to the one or more memory modules and configured to execute the instructions to perform operations, comprising:
• generating a plurality of digital models of a plurality of core samples taken from one or more reservoir formations based on a corresponding plurality of images of the plurality of core samples;
• determining a pore throat size distribution of the plurality of core samples based on the generated digital models;
• determining corresponding capillary pressure curves and corresponding NMR value distributions of the plurality of core samples with one or more numerical simulations;
• generating one or more machine-learning (ML) models based on the pore throat size distribution, corresponding capillary pressure curves, and corresponding NMR value distributions of the plurality of core samples;
• adjusting the one or more ML models with one or more reservoir data of the one or more reservoir formations;
• generating adjusted capillary pressure curves and NMR value distributions from the one or more adjusted ML models;
• and
• determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or the adjusted NMR value distributions.

An apparatus comprising one or more tangible, non-transitory computer readable media that stores instructions executable by one or more hardware processors communicably coupled to the media to cause the one or more hardware processors to perform operations, comprising:

• generating a plurality of digital models of a plurality of core samples taken from one or more reservoir formations based on a corresponding plurality of images of the plurality of core samples;
• determining a pore throat size distribution of the plurality of core samples based on the generated digital models;
• determining corresponding capillary pressure curves and corresponding NMR value distributions of the plurality of core samples with one or more numerical simulations;
• generating one or more machine-learning (ML) models based on the pore throat size distribution, corresponding capillary pressure curves, and corresponding NMR value distributions of the plurality of core samples;
• adjusting the one or more ML models with one or more reservoir data of the one or more reservoir formations;
• generating adjusted capillary pressure curves and NMR value distributions from the one or more adjusted ML models;
• and
• determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or the adjusted NMR value distributions.