Estimating pore and fluid characteristic properties in rock samples using nuclear magnetic resonance analyses

A computing device for improving the production of hydrocarbons in a subsurface region, comprising:

• a processor;
• a memory coupled to the processor;
• and
• instructions provided to the memory, wherein the instructions are executable by the processor and are configured to:
• identify modes in nuclear magnetic resonance (NMR T1-T2 data from a plurality of samples;
• assign the modes to a poro-fluid class;
• cluster the modes based on poro-fluid class to produce a plurality of clusters, wherein each cluster represents one or more poro-fluid classes;
• estimate a bulk fluid relaxation time (estimated TB) based on an asymptote fit of the clusters, wherein the asymptote is based on T1 and T2 relaxation mechanisms in a bulk fluid relaxation-dominated limit;
• estimate a ratio of a transverse T2 pore surface relaxivity constant (ρ2) to a longitudinal T1 pore surface relaxivity constant (ρ1), the ratio represented as ρ2/ρ1, based on an asymptote fit of the clusters, wherein the asymptote is based on T1 and T2 relaxation mechanisms in a surface relaxation-dominated limit;
• fit the T1 relaxation mechanisms and the T2 relaxation mechanisms to one or more of the clusters using the estimated TB;
• derive at least one pore or fluid relaxation parameter and endpoint for the poro-fluid classes from the fit, wherein the at least one pore or fluid relaxation parameter and endpoint is selected from the group consisting of ρ1, ρ2, a pore surface-to-volume ratio (A/V), an equivalent pore-throat radius (req), and a bulk fluid relaxation time (TB);
• and
• identify potential hydrocarbon-bearing formations in the subsurface region based on the derived at least one pore or fluid relaxation parameter and endpoint for the poro-fluid classes.