TY - JOUR

T1 - Estimation of the permeability of hydrocarbon reservoir samples using induced polarization and nuclear magnetic resonance methods

AU - Razavirad, Fatemeh

AU - Schmutz, Myriam

AU - Binley, Andrew

N1 - © 2019 Society of Exploration Geophysicists Estimation of the permeability of hydrocarbon reservoir samples using induced polarization and nuclear magnetic resonance methods Fatemeh Razavirad, Myriam Schmutz, and Andrew Binley GEOPHYSICS 2019 84:2, MR73-MR84 Use is subject to SEG terms of use and conditions.

PY - 2019/3/1

Y1 - 2019/3/1

N2 - We have evaluated several published models using induced polarization (IP) and nuclear magnetic resonance (NMR) measurements for the estimation of permeability of hydrocarbon reservoir samples. IP and NMR measurements were made on 30 samples (clean sands and sandstones) from a Persian Gulf hydrocarbon reservoir. We assessed the applicability of a mechanistic IP-permeability model and an empirical IP-permeability model recently proposed. The mechanistic model results in a broader range of permeability estimates than those measured for sand samples, whereas the empirical model tends to overestimate the permeability of the samples that we tested. We also evaluated an NMR permeability prediction model that is based on porosity φ and the mean of the log transverse relaxation time (T2ml). This model provides reasonable permeability estimations for the clean sandstones that we tested but relies on calibrated parameters. We also examined an IP-NMR permeability model, which is based on the peak of the transverse relaxation time distribution, T2p and the formation factor. This model consistently underestimates the permeability of the samples tested. We also evaluated a new model. This model estimates the permeability using the arithmetic mean of log transverse NMR relaxation time (T2ml) and diffusion coefficient of the pore fluid. Using this model, we improved estimates of permeability for sandstones and sand samples. This permeability model may offer a practical solution for geophysically derived estimates of permeability in the field, although testing on a larger database of clean granular materials is needed.

AB - We have evaluated several published models using induced polarization (IP) and nuclear magnetic resonance (NMR) measurements for the estimation of permeability of hydrocarbon reservoir samples. IP and NMR measurements were made on 30 samples (clean sands and sandstones) from a Persian Gulf hydrocarbon reservoir. We assessed the applicability of a mechanistic IP-permeability model and an empirical IP-permeability model recently proposed. The mechanistic model results in a broader range of permeability estimates than those measured for sand samples, whereas the empirical model tends to overestimate the permeability of the samples that we tested. We also evaluated an NMR permeability prediction model that is based on porosity φ and the mean of the log transverse relaxation time (T2ml). This model provides reasonable permeability estimations for the clean sandstones that we tested but relies on calibrated parameters. We also examined an IP-NMR permeability model, which is based on the peak of the transverse relaxation time distribution, T2p and the formation factor. This model consistently underestimates the permeability of the samples tested. We also evaluated a new model. This model estimates the permeability using the arithmetic mean of log transverse NMR relaxation time (T2ml) and diffusion coefficient of the pore fluid. Using this model, we improved estimates of permeability for sandstones and sand samples. This permeability model may offer a practical solution for geophysically derived estimates of permeability in the field, although testing on a larger database of clean granular materials is needed.

KW - induced polarization

KW - nuclear magnetic resonance

KW - permeability

U2 - 10.1190/geo2017-0745.1

DO - 10.1190/geo2017-0745.1

M3 - Journal article

AN - SCOPUS:85061066572

VL - 84

SP - MR73-MR84

JO - Geophysics

JF - Geophysics

SN - 0016-8033

IS - 2

ER -