Abstract: A method for calibrated multi-mineral, multi-fluid interpretation is provided herein. The method includes generating a multi-mineral, multi-fluid interpretation model for a number of log types using core and/or specialized log data acquired from subsurface region(s) that relate to components within the subsurface region(s). Generating the model includes: (1) for each log type, calibrating component end-members for the log type via an inversion of the core and/or specialized log data relating to the components across all depths of interest; and (2) incorporating the resulting calibrated end-members for the log types into the model. The method also includes generating component volume fraction profiles using log data acquired from analogous subsurface region(s) using the model, wherein the log data relate to any of the log types used to generate the model. Each component volume fraction profile includes a range of component volume fractions that accounts for a degree of uncertainty within the model.

Abstract: A method for deriving at least one pore or fluid relaxation parameter and endpoint selected from the group consisting of a longitudinal T1 pore surface relaxivity constant (?1), a transverse T2 pore surface relaxivity constant (?2), a pore surface-to-volume ratio (A/V), an equivalent pore-throat radius (req), and a bulk fluid relaxation time (TB) comprising: identifying modes in NMR T1-T2 data; assigning the modes to a poro-fluid class; clustering the modes based on poro-fluid class; estimating TB based on an asymptote fit of the clusters using T1 and T2 relaxation mechanisms in a bulk fluid relaxation-dominated limit; estimating ?2/?1 based on an asymptote fit of the clusters using T1 and T2 relaxation mechanisms in a surface relaxation-dominated limit; fitting the T1 and T2 relaxation mechanisms to the clusters using the estimated TB; and deriving the pore or fluid relaxation parameter and endpoint for the poro-fluid classes from the fit.

Abstract: A method for partitioning NMR T1-T2 data may comprise: identifying modes in NMR T1-T2 data from a plurality of samples with a multimodal deconvolution or decomposition with regularized nonlinear inversion; deriving a modal properties vector comprising modal properties for each of the modes; performing a cluster analysis of the modes to identify clusters; assigning a poro-fluid class to the clusters based on one or more of the modal properties of the modes in each of the clusters; and deriving partitioned representations for the clusters based on the cluster analysis.

Abstract: A method for deriving at least one pore or fluid relaxation parameter and endpoint selected from the group consisting of a longitudinal T1 pore surface relaxivity constant (?1), a transverse T2 pore surface relaxivity constant (?2), a pore surface-to-volume ratio (A/V), an equivalent pore-throat radius (req), and a bulk fluid relaxation time (TB) comprising: identifying modes in NMR T1-T2 data; assigning the modes to a poro-fluid class; clustering the modes based on poro-fluid class; estimating TB based on an asymptote fit of the clusters using T1 and T2 relaxation mechanisms in a bulk fluid relaxation-dominated limit; estimating ?2/?1 based on an asymptote fit of the clusters using T1 and T2 relaxation mechanisms in a surface relaxation-dominated limit; fitting the T1 and T2 relaxation mechanisms to the clusters using the estimated TB; and deriving the pore or fluid relaxation parameter and endpoint for the poro-fluid classes from the fit.

Abstract: A method for partitioning NMR T1-T2 data may comprise: identifying modes in NMR T1-T2 data from a plurality of samples with a multimodal deconvolution or decomposition with regularized nonlinear inversion; deriving a modal properties vector comprising modal properties for each of the modes; performing a cluster analysis of the modes to identify clusters; assigning a poro-fluid class to the clusters based on one or more of the modal properties of the modes in each of the clusters; and deriving partitioned representations for the clusters based on the cluster analysis.