ABSTRACT
The injection of CO2 into coal reservoirs offers a promising approach for reducing CO2 emissions and enhancing coalbed methane production. An inadequate understanding of the CO2 permeability variation in coals under the coupled constraints of temperature and effective stress limits the accurate evaluation of the CO2 injection potential. To address this issue, coal samples of different ranks were collected from the Sihe, Zhangjiamao, and Dahebian coal mines in China (designated as SH-1, ZJM-1, and DHB-1, respectively). These samples were used to investigate the characteristics of permeability variation and sensitivity to coal rank, temperature, and effective stress. The coal samples ZJM-1, DHB-1, and SH-1 had absolute permeability of 0.01355, 0.03953, and 0.00064mD, respectively. The SH-1 sample had the lowest absolute permeability due to its high coal rank, and the DHB-1 sample had the highest absolute permeability due to its tectonically deformed and fractured coal structure. The association of permeability to effective stress can be more accurately described by the permeability model with variable pore compression coefficient than that with constant pore compression coefficient. The CO2 permeability of the coal samples decreased exponentially with increasing effective stress, while the permeability damage rate increased exponentially and the permeability curvature decreased exponentially. Based on the fitted curve of the permeability damage rate to effective stress, three stages of stress sensitivity were identified: rapidly increasing stage, slowly increasing stage, and approximately stable stage. The permeability and temperature sensitivity coefficient exhibited a decreasing trend with increasing temperature, following a power function relationship. Among the undisturbed coal samples, the SH-1 sample showed higher sensitivity to stress and temperature compared to the ZJM-1 sample. The DHB-1 sample, characterized by a fractured coal structure, exhibited the lowest temperature sensitivity and the highest stress sensitivity. The temperature sensitivity of different coal samples tended to be consistent at temperatures above 70°C. Predictive models for CO2 permeability under the dual constraints of temperature and effective stress were constructed for the three samples using the surface fitting method. These models can assist in evaluating the feasibility of CO2 injection into coal reservoirs at various depths in associated areas.
Table of symbols
Parameter | = | Definition (Units) |
Kge | = | the measured gas permeability (mD) |
p0 | = | the gas pressure at the outlet(MPa) |
p1 | = | the gas pressure at the inlet(MPa) |
qeg | = | the gas flow(mL/s) |
μg | = | the gas viscosity under experimental conditions(mPa·s) |
L | = | the coal sample length(cm) |
A | = | the cross-sectional area of the coal(cm2) |
Ka | = | the absolute permeability(mD) |
pm | = | the average gas pressure(MPa) |
B | = | the Klinkenberg constant(MPa) |
σ | = | the total stress(MPa) |
Δσ | = | the effective stress(MPa) |
α | = | the effective stress coefficient Commonly considered as 1 |
p | = | the pore fluid pressure(MPa) |
cp | = | the pore compression coefficient(MPa−1) |
Vp | = | the pore volume of coal sample(m3) |
k | = | the coal permeability(mD) |
k0 | = | the coal initial permeability(mD) Δσ = 0 |
c0 | = | the initial pore compression coefficient(MPa−1) |
γ | = | the attenuation coefficient of pore fluid pressure with effective stress |
ks | = | the permeability damage rate(%) |
ki | = | the permeability at the specifiedeffective stress(mD) |
kc | = | the permeability curvature(%) Quantify the permeability decreasing rate as effective stress increases |
k” | = | the first derivative of formula |
k”’ | = | the second derivative of formula |
T | = | temperature(°C) |
δT | = | the temperature sensitivity coefficient(°C−1) Quantitatively evaluate the temperature sensitivity of permeability |
k1 | = | the coal permeability at the initial temperature(mD) |
∂T | = | the temperature change(°C) |
∂K | = | the permeability change(mD) |
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
Notes on contributors
Chen Guo
Chen Guo, Ph.D., is the corresponding author and is an associate professor at the College of Geology and Environment at the Xi’an University of Science and Technology. E-mail: [email protected].
Jinxiao Yang
Jinxiao Yang, is a postgraduate at the College of Geology and Environment at the Xi’an University of Science and Technology. E-mail: [email protected].
Qiang Sun
Qiang Sun, Ph.D., is a professor at the College of Geology and Environment at the Xi’an University of Science and Technology. E-mail: [email protected].
Jiang Gou
Jiang Gou, is a postgraduate at the College of Geology and Environment at the Xi’an University of Science and Technology. E-mail: [email protected].
Junzhe Gao
Junzhe Gao, is a postgraduate at the College of Geology and Environment at the Xi’an University of Science and Technology. E-mail: [email protected].