Diffusion of CO2 in Decane Studied by Magnetic Resonance Imaging

doi:10.11938/cjmr20150304

Abstract

Abstract:

Magnetic resonance imaging (MRI) was used to visualize the diffusion of CO₂ in decane, while the pressure was monitored via a double-chamber pressure decay method (PVT). The signal intensities on the MRI images were analyzed to reveal the distribution of dimensionless CO₂ content. Based on Fick's law, we computed the diffusion coefficient related to diffusion time and diffusion distance using the finite volume method. The overall average diffusion coefficient can be obtained at any diffusion time. Comparing the overall average diffusion coefficient obtained by the MRI method during diffusion equilibrium time with that obtained by the PVT method showed a 2.7% error. The diffusion coefficient decreased along the diffusing direction, and declined exponentially with time. The overall average diffusion coefficient decreased with the extension of diffusion time. The results obtained in this study were comparable to previous results obtained under similar experimental conditions.

Key words: MRI, diffusion, mass transfer, decane, CO₂

CLC Number:

O482.53

HAO Min1,2*，ZHAO Yue-chao2，MAO Yun-feng3，SONG Yong-chen2. Diffusion of CO₂ in Decane Studied by Magnetic Resonance Imaging[J]. Chinese Journal of Magnetic Resonance, 2015, 32(3): 430-438.

References

[1] Abedini A, Torabi F. On the CO₂ storage potential of cyclic CO₂ injection process for enhanced oil recovery[J]. Fuel, 2014, 124: 14－27.

[2] Kavousia S, Farshid T, Chanb C W, et al. Experimental measurement and parametric study of CO₂ solubility and molecular diffusivity in heavy crude oil systems[J]. Fluid Phase Equilibr, 2014, 371: 57－66.

[3] Riazi M R. A new method for experimental measurement of diffusion coefficients in reservoir fluids[J]. J Petrol Sci Eng, 1996, 14: 235－250.

[4] Zhang Y P, Hyndman C L, Maini B B. Measurement of gas diffusivity in heavy oils[J]. J Petrol Sci Eng, 2000, 25: 37－47.

[5] Davis P K, Lundy G D, Palamara J E, et al. New pressure decay techniques to study gas sorption and diffusion in polymers at elevated pressures[J]. Ind Eng Chem Res, 2004, 43: 1 537－1 542.

[6] Wen Y W, Kantzas A. Monitoring Bitumen-solvent interactions with low-field nuclear magnetic resonance and X-ray computer-assisted tomography[J]. Energy & Fuels, 2005, 19: 1 319－1 326.

[7] Song L, Kantzas A, Bryan J, et al. Experimental Measurement of Diffusion Coefficient of CO₂ in Heavy Oil Using X-Ray Computed-Assisted Tomography Under Reservoir Conditions[C]. Canada: The Canadian Unconventional Resources & International Petroleum Conference, Society of Petroleum Engineers, 2010, CSUG/SPE 137545.

[8] Araujo S V, Vargas J A V, Trevisan O V. Diffusion Coefficient of CO2 in Light Oil Under Reservoir Conditions Using X-Ray Computed Tomography[C]. Brazil: Offshore Technology Conference, 2013, OTC 24454.

[9] Song L, Kantzas A, Bryan J. Investigation of CO₂ Diffusivity in Heavy Oil Using X-Ray Computer-Assisted Tomography Under Reservoir Conditions[C]. USA: International Conference on CO2 Capture, Storage, and Utilization, Society of Petroleum Engineers, 2010, SPE138205.

[10] Zhang Zong-fu(张宗富), Xiao Li-zhi(肖立志), Liu Hua-bing(刘化冰), et al. Simulation of restricted diffusion of water molecules in mieropores(水分子在微孔隙介质中的受限扩散模拟)[J]. Chinese J Magn Reson(波谱学杂志), 2014, 31(1): 49－60.

[11] Li Xin-jun(李新军), Nie Sheng-dong(聂生东), Wang Yuan-jun(王远军), et al. An improved two-dimensional inversion algorithm for low-field NMR diffusion-transverse relaxation correlation data(低场核磁共振扩散-横向弛豫二维反演算法)[J]. Chinese J Magn Reson(波谱学杂志), 2013, 30(3): 322－335.

[12] Ersland G, husebo J, Graue A, et al. Measuring gas hydrate formation and exchange with CO₂ in Bentheim sandstone using MRI tomography[J]. Chem Eng J, 2010, 158: 25－31.

[13] Liu Y, Zhao Y, Zhao J, et al. Magnetic resonance imaging on CO₂ miscible and immiscible displacement in oil-saturated glass beads pack[J]. Magn Reson Imaging, 2011, 29: 1 110－1 118.

[14] Zhao Y C, Song Y C. Visualization of CO₂ and oil immiscible and miscible flow processes in porous media using NMR micro-imaging[J]. Petrol Sci, 2011, 8: 183－193.

[15] Guerrero A U, Kantzas A. Diffusion of Hydrocarbon Gases in Heavy Oil and Bitumen[C]. Colombia: The 2009 SPE Latin American and Caribbean Petroleum Engineering Conference held in Cartagena, SPE 1222783.

[16] Jamialahmadi M, Emadi M. Diffusion coefficients of methane in liquid hydrocarbons at high pressure and temperature[J]. J Petrol Sci Eng, 2006, 53: 47－60.

[17] Wang L S, Lang Z X, Guo T M. Measurement and correlation of the diffusion coefficients of carbon dioxide in liquid hydrocarbons under elevated pressures[J]. Fluid Phase Equilibr, 1996, 117(1, 2): 364－372.

[18] Grogan A T, Pinczewski V W, Ruskauff G J, et al. Diffusion of CO₂ at reservoir conditions: models and measurements[J]. Spe Reservoir Eng, 1988, 3(1): 93－102.

Diffusion of CO₂ in Decane Studied by Magnetic Resonance Imaging

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	JIANG Fan, WANG Yuan-jun. A Review on Interpolation Methods for Diffusion Tensor Images [J]. Chinese Journal of Magnetic Resonance, 2019, 36(3): 392-407.
[2]	WEI Guo-jing, YI Pei-wei, TAO Quan, FENG Yan-qiu. Comparisons of Different CEST Quantification Metrics Applied in Acute Parkinson's Disease Mouse Model [J]. Chinese Journal of Magnetic Resonance, 2019, 36(2): 195-207.
[3]	HUANG Zhao-hui, ZHANG Zhi, CHEN Li, CHEN Jun-fei, ZHANG Zhen, CHEN Fang, LIU Chao-yang. A Time-Division Multiplexing Design for Gradient Preemphasis Module in Magnetic Resonance Imaging Scanner [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 465-474.
[4]	ZHAI Guo-qiang, ZHANG Miao, BO Bin-shi, WANG Yi, FAN Ming-xia, LI Jian-qi. Quantifying Liver Fat with Combined Complex-Based and Magnitude-Based Water-Fat Separation [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 417-426.
[5]	CHAI Qing-huan, SU Guan-qun, NIE Sheng-dong. Compressive Sensing Low-Field MRI Reconstruction with Dual-Tree Wavelet Transform and Wavelet Tree Sparsity [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 486-497.
[6]	LIU Ying, SONG Ming-hui, WANG Kun, ZHANG Hao-wei. A Magnetic Resonance Receiver System Design Based on All Programmable System-on-a-Chip and LabVIEW [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 475-485.
[7]	WANG Hong-zhi, ZHAO Di, YANG Li-qin, XIA Tian, ZHOU Xiao-yue, MIAO Zhi-ying. An Approach for Training Data Enrichment and Batch Labeling in AI+MRI Aided Diagnosis [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 447-456.
[8]	CHEN Hai-yan, ZHAO Shi-long, LI Xiao-nan, LIU Guo-qiang, HU Li-li, LIU Tao. B₁ Mapping on Low-Field Permanent Magnet MRI Scanner [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 498-504.
[9]	JIANG Fan, WANG Yuan-jun. Construction of Human Brain Templates with Diffusion Tensor Imaging Data: A Review [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 520-530.
[10]	TAO Quan, YI Pei-wei, WEI Guo-jing, FENG Yan-qiu. pH Imaging Based on Chemical Exchange Saturation Transfer: Principles, Methods, Applications and Recent Progresses [J]. Chinese Journal of Magnetic Resonance, 2018, 35(4): 505-519.
[11]	LI Hong-wei, YUAN Zhi-liang, XIA Bin. Determination of Apparent Protein Molecular Weight in Solution by Diffusion Ordered NMR Spectroscopy [J]. Chinese Journal of Magnetic Resonance, 2018, 35(3): 280-286.
[12]	HUANG Jun-lin, YU Yi-hua. Effects of Digital Resolution on Diffusional Dimension in DOSY Experiments [J]. Chinese Journal of Magnetic Resonance, 2018, 35(3): 287-293.
[13]	XU Jia-wen, XU Jian, ZHOU Xiao-dong, ZHANG Cong, CHEN Qun. Multi-GPU Distributed Magnetic Resonance Image Reconstruction Based on Gadgetron [J]. Chinese Journal of Magnetic Resonance, 2018, 35(3): 303-317.
[14]	ZHANG Bo, XIE Hai-bin, YAN Xu, LI Wen-jing, YANG Guang. Rotation Invariant Non-Local Means for Noise Reduction in Magnetic Resonance Images [J]. Chinese Journal of Magnetic Resonance, 2018, 35(2): 162-169.
[15]	CUI Yan, XIAO Liang. High Accuracy Generation and Test of B₀ Signals on Magnetic Resonance Imaging Spectrometer [J]. Chinese Journal of Magnetic Resonance, 2018, 35(2): 170-177.