期刊
MICROPOROUS AND MESOPOROUS MATERIALS
卷 298, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.micromeso.2020.110003
关键词
Low field NMR; Porosity; NMR relaxometry; Shales
类别
资金
- National Centre for Research and Development [PBS2/A2/16/2013, STRATEGMED2/265761/10/NCBR/2015]
- Department of Fossil Fuels, Faculty of Geology, Geophysics and Environmental Protection AGH UST [16.16.140.315]
In the oil and gas industry, the critical volumetric parameter is porosity, one which allows for the estimation of hydrocarbon reserves, constituting important sources of energy. For unconventional reservoirs such as shales, precise porosity measurement is a challenge due to the significant number of pores with diameters of several nanometers and fewer. In this paper, we focus on the use of the modified Low Field NMR approach, applying a Spin Echo CMPG sequence for the quantitative and precise determination of movable porosity in the full range of the occurring pore distribution. The proposed approach minimizes the impact of overlapping signals originating from hydrogen in organic matter, OH groups, chemically bound water and moving water in the pores on the measured porosities. The examined samples from the Baltic Basin (Poland) were characterized by narrow PSD, with a dominant pore size of 2 nm, which is much lower than those known from the literature for shales, which typically range from several to dozens of nanometers. Results from the modified NMR protocol are compared with porosities from MICP and those obtained using the standard NMR approach with T-2 cut-offs. As evidence of the superiority of the novel NMR approach over the standard NMR procedure, the improved accuracy of the porosity determination for the shales with extremely small pores is presented. Relations between T2 log-mean and T-1/T-2 values and differential porosity are also presented, suggesting that the LF NMR method could be utilized for rapid porosity estimation with a single T-2 measurement. Correlations between the T-2 time distribution for saturated samples and TOC allowed us to obtain a model with the use of multivariate PLSR analysis, which gives the potential for the NMR method to predict geochemical parameters such as TOC in a simple and non-invasive way.
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