A laboratory study to estimate pore geometric parameters of sandstones using complex conductivity and nuclear magnetic resonance for permeability prediction

Abstract

We estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multisalinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty‐five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time ( normalR2 of 0.69 and 0.33 and normalized root mean square errors (NRMSE) of 0.16 and 0.21, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE=0.23). We demonstrate that that permeability estimates from the joint‐NMR‐CC model (NRMSE=0.13) compare favorably to estimates from the Katz and Thompson model (NRMSE=0.074). This model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

Characterizing the Critical Zone Using Borehole and Surface Nuclear Magnetic Resonance

“…1). (Keating and Knight, 2007;Osterman et al, 2016). At our site, visible Fe staining has been observed along fractures in extracted core (Flinchum et al, 2018b), so we might expect artificially reduced T 2 * relaxation times.…”

“…In crystalline rock, the common assumption that T 2 * is directly related to pore‐scale properties (i.e., T 2 * ≅ T 2 ) may not be valid because of the presence of Fe oxides (Legchenko et al, 2002; Grunewald and Knight, 2012; Walsh et al, 2014). Iron(III) in Fe oxides like hematite and goethite can reduce T 2 relaxation times (Keating and Knight, 2007; Osterman et al, 2016). At our site, visible Fe staining has been observed along fractures in extracted core (Flinchum et al, 2018b), so we might expect artificially reduced T 2 * relaxation times.…”

Estimation of the permeability of hydrocarbon reservoir samples using induced polarization and nuclear magnetic resonance methods

“…There are a few studies that compare IP (or SIP) permeability models with NMR permeability models (Weller et al, 2010a;Weller et al, 2014;Osterman et al, 2016). Osterman et al (2016) with a broad range of permeability.…”

“…There are a few studies that compare IP (or SIP) permeability models with NMR permeability models (Weller et al, 2010a;Weller et al, 2014;Osterman et al, 2016). Osterman et al (2016) with a broad range of permeability. They used the Katz and Thompson permeability model, which is based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure (MICP), and the intrinsic formation factor, F. NMR measurements of the relaxation time associated with the peak of transverse relaxation time distribution ( ) and effective surface relaxivity ( ) were used to estimate the T 2p…”

“…There is substantial experimental evidence that the surface relaxivity is a function of the concentration and mineralogy of paramagnetic species on the surface of the pore (Foley et al, 1996;Bryar et al, 2000;Knight, 2007, 2010). Therefore, Osterman et al (2016) first determined the effective surface relaxivity ( ) from a log-linear fit between pore-throat size ( ) and the peak of ρ 2eff Λ transverse relaxation time distribution ( ). Based on the model of Osterman et al…”