公开(公告)号：US12000279B2

公开(公告)日：2024-06-04

申请号：US16947764

申请日：2020-08-14

Applicant: ExxonMobil Technology and Engineering Company

Inventor： Olabode Ijasan ,  Darren M. McLendon

IPC: G01N24/08 ,  E21B7/04 ,  E21B49/02 ,  E21B49/08 ,  G01N15/08 ,  G01N33/24 ,  G01R33/50 ,  G01V3/32 ,  G01V3/38

CPC classification number: E21B49/088 ,  E21B7/046 ,  E21B49/02 ,  E21B49/0875 ,  G01N15/088 ,  G01N24/081 ,  G01N24/082 ,  G01N33/241 ,  G01R33/50 ,  G01V3/32 ,  G01V3/38 ,  G01N2015/0846

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 (ρ1), a transverse T2 pore surface relaxivity constant (ρ2), 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 ρ2/ρ1 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.

公开(公告)号：US11852013B2

公开(公告)日：2023-12-26

申请号：US16947762

申请日：2020-08-14

Applicant: ExxonMobil Technology and Engineering Company

Inventor： Olabode Ijasan ,  Darren M. McLendon

IPC: E21B49/08 ,  E21B49/02 ,  G01N24/08 ,  G01V3/32 ,  G01V3/38 ,  E21B7/04 ,  G01N33/24 ,  G01R33/50 ,  G01N15/08

CPC classification number: E21B49/088 ,  E21B7/046 ,  E21B49/02 ,  E21B49/0875 ,  G01N15/088 ,  G01N24/081 ,  G01N24/082 ,  G01N33/241 ,  G01R33/50 ,  G01V3/32 ,  G01V3/38 ,  G01N2015/0846

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.