Numerical simulation of nuclear magnetic resonance (NMR) can simulate experimental scenarios and quantify the impact of each factor on the physical characteristics. However, general simulation methods lack authentic pore structure information and fail to accurately model the complex geometry of rocks. High-resolution digital rock cores can effectively reflect pore structure. In this paper, a high-resolution digital core of Berea sandstone is taken as the research object, the pore parameters of the core (e.g., pore volume and surface-to-volume ratio) are quantified, and the 12 529 pores extracted from the three-dimensional digital core are statistically analyzed. Subsequently, the pores are classified based on their surface-to-volume ratio and volume. After the simulation parameters are calibrated by the experiments, NMR response of different water-saturated pores is simulated. Finally, the NMR response of the core with different oil saturation is simulated. We find that the distribution of pore quantity in terms of volume and surface area both follows a power function. There is a strong correlation between pore volume and surface area, especially for smaller pores. The T2 (transverse relaxation time) spectrum can generally reflect the volume distribution, but it may not accurately reflect the volume distribution when the pores are large. We also observe that the spectrum peak reflecting oil bulk relaxation is positioned to the left of the peak of the oil bulk relaxation due to the combined effects of surface relaxation of residual water and diffusion relaxation. These simulation results provide a physical basis for interpreting NMR measurements and can help identify fluids in reservoirs.

1.
Y.
Jin
,
L.
Xiao
,
W.
Li
,
G.
Wang
, and
W.
Long
, “
Simulation of nuclear magnetic resonance response based on 3D CT images of sandstone core
,”
J. Petrol. Explor. Prod. Technol.
13
,
2015
2029
(
2023
).
2.
J.
Liu
,
R.
Xie
,
J.
Guo
, and
C.
Xu
, “
Study of nuclear magnetic resonance response mechanism in shale oil and correction of petrophysical parameters
,”
Fuel
358
,
130162
(
2024
).
3.
G. P.
Zientara
and
J. H.
Freed
, “
Spin-echoes for diffusion in bounded, heterogeneous media: A numerical study
,”
J. Chem. Phys.
72
,
1285
1292
(
1980
).
4.
O.
Mohnke
and
N.
Klitzsch
, “
Microscale simulations of NMR relaxation in porous media considering internal field gradients
,”
Vadose Zone J.
9
,
846
857
(
2010
).
5.
K.
Wang
and
N.
Li
, “
Numerical simulation on effects of reservoir characteristics and saturation on T2 spectra of nuclear magnetic resonance
,”
Acta Pet. Sin.
30
,
422
426
(
2009
).
6.
M.
Tan
,
J.
Xu
,
Y.
Zou
, and
C.
Xu
, “
Nuclear magnetic resonance (NMR) microscopic simulation based on random-walk: Theory and parameters analysis
,”
J. Cent. South Univ.
21
,
1091
1097
(
2014
).
7.
D.
Denney
, “
Digital core laboratory: Reservoir-core properties derived from 3D images
,”
J. Pet. Technol.
56
,
66
68
(
2004
).
8.
C. H.
Arns
,
A. P.
Sheppard
,
R. M.
Sok
, and
M. A.
Knackstedt
, “
NMR petrophysical predictions on digitized core images
,”
Petrophysics
48
,
202
221
(
2007
).
9.
L.
Zhang
,
S.
Chen
,
C.
Zhang
,
X.
Fang
, and
S.
Li
, “
The characterization of bituminous coal microstructure and permeability by liquid nitrogen fracturing based on μCT technology
,”
Fuel
262
,
116635
(
2020
).
10.
L.
Chi
and
Z.
Heidari
, “
Diffusional coupling between microfractures and pore structure and its impact on nuclear magnetic resonance measurements in multiple-porosity systems
,”
Geophysics
80
,
D31
D42
(
2015
).
11.
M.
Du
,
Z.
Yang
,
E.
Jiang
,
J.
Lv
,
T.
Yang
,
W.
Wang
,
J.
Wang
,
Y.
Zhang
,
H.
Li
, and
Y.
Xu
, “
Using digital cores and nuclear magnetic resonance to study pore-fracture structure and fluid mobility in tight volcanic rock reservoirs
,”
J. Asian Earth Sci.
259
,
105890
(
2024
).
12.
J.
Guo
, “
Numerical simulation of NMR responses and study of data inversion method in tight sandstone
,”
Dissertation
(
China University of Petroleum
,
2019
).
13.
J.
Guo
,
R.
Xie
, and
Y.
Ding
, “
Three dimensional digital cores reconstructed by MCMC method and numerical simulation of rock NMR response
,”
China Sci.
11
,
280
285
(
2016
).
14.
Y.
Jin
,
L.
Xiao
,
W.
Li
,
G.
Wang
, and
W.
Long
, “
Simulation of NMR response of microfractures based on digital rock technology
,”
Geoenergy Sci. Eng.
227
,
211876
(
2023
).
15.
P. E.
Øren
,
F.
Antonsen
,
H. G.
Rueslåtten
, and
S.
Bakke
, “
Numerical simulations of NMR responses for improved interpretations of NMR measurements in reservoir rocks
,” paper presented at the
SPE Annual Technical Conference and Exhibition
, San Antonio, Texas,
2002
.
16.
M.
Tan
,
K.
Wang
,
Y.
Zou
,
S.
Wang
,
Y.
Fang
, and
X.
Cheng
, “
Nuclear magnetic resonance simulations of nano-scale cores and microscopic mechanisms of oil shale
,”
Fuel
256
,
115843
(
2019
).
17.
J.
Zhao
,
X.
Ge
,
Y.
Xiao
,
Y.
Fan
,
F.
Hu
,
J.
Liu
, and
Z.
Sha
, “
Forward simulation and characteristic analysis on the low field NMR transverse relaxation response of fractured shale reservoir
,”
Chin. J. Geophys.
66
,
2621
2630
(
2023
).
18.
Y.
Zou
,
R.
Xie
,
J.
Guo
,
M.
Tan
,
F.
Hu
,
C.
Li
,
C.
Li
, and
C.
Zhou
, “
Reconstruction of digital core of tight reservoir and simulation of NMR response
,”
J. China Univ. Pet.
39
,
63
71
(
2015
).
19.
O.
Talabi
,
S.
AlSayari
,
S.
Iglauer
, and
M. J.
Blunt
, “
Pore-scale simulation of NMR response
,”
J. Pet. Sci. Eng.
67
,
168
178
(
2009
).
20.
G. R.
Coates
,
L.
Xiao
, and
M. G.
Prammer
,
NMR Logging Principles and Applications
(
Halliburton Energy Services Publication
,
1999
).
21.
K.
Deng
,
Nuclear Magnetic Resonance Petrophysical and Logging Applications
(
China University of Petroleum
,
Dongying
,
2010
).
22.
X.
Li
, “
Characterization of pore structure and simulation of elasticity and permeability based on 3D digital cores
,”
Dissertation
(
China University of Geosciences
,
2021
).
23.
L.
Zhang
,
M.
Huang
,
M.
Li
,
S.
Lu
,
X.
Yuan
, and
J.
Li
, “
Experimental study on evolution of fracture network and permeability characteristics of bituminous coal under repeated mining effect
,”
Nat. Resour. Res.
31
,
463
486
(
2022
).
24.
X.
Liu
,
W.
Zhang
, and
J.
Sun
, “
Methods of constructing 3-D digital cores: A review
,”
Prog. Geophys.
28
,
3066
3072
(
2013
).
25.
M.
Elsayed
,
A.
Isah
,
M.
Hiba
,
A.
Hassan
,
K.
Al-Garadi
,
M.
Mahmoud
,
A.
El-Husseiny
, and
A. E.
Radwan
, “
A review on the applications of nuclear magnetic resonance (NMR) in the oil and gas industry: Laboratory and field-scale measurements
,”
J. Petrol. Explor. Prod. Technol.
12
,
2747
2784
(
2022
).
26.
L. O.
Pires
,
A.
Winter
, and
O. V.
Trevisan
, “
Dolomite cores evaluated by NMR
,”
J. Pet. Sci. Eng.
176
,
1187
1197
(
2019
).
27.
J.
Chen
,
G. J.
Hirasaki
, and
M.
Flaum
, “
NMR wettability indices: Effect of OBM on wettability and NMR responses
,”
J. Pet. Sci. Eng.
52
,
161
171
(
2006
).
28.
G.
Jin
,
C.
Torres-Verdín
, and
E.
Toumelin
, “
Comparison of NMR simulations of porous media derived from analytical and voxelized representations
,”
J. Magn. Reson.
200
,
313
320
(
2009
).
29.
H.
Wang
,
V.
Alvarado
,
J. F.
McLaughlin
,
D. A.
Bagdonas
,
J. P.
Kaszuba
,
E.
Campbell
, and
D.
Grana
, “
Low‐field nuclear magnetic resonance characterization of carbonate and sandstone reservoirs from rock spring uplift of Wyoming
,”
J. Geophys. Res.
123
,
7444
7460
, https://doi.org/10.1029/2018JB015779 (
2018
).
30.
D.
Xing
, “
Experimental study and application of NMR response mechanism in tight oil and gas reservoirs
,”
Dissertation
(
China University of Petroleum
,
2019
).
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