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Permeability characterizes how saturating fluids penetrate and flow through porous rocks. It is an important quantity that can be used to model the behavior of reservoir rocks and to optimize underground resource exploration. Permeability can be characterized via nuclear magnetic resonance (NMR) relaxometry techniques by utilizing the yielded pore volume and pore length scales. However, for heterogeneous rocks a simple estimation of a permeability value based on the existing models may be inadequate. In this work, a new relaxation technique is applied to rock cores in conjunction with magnetic resonance imaging schemes. This allows the interior structures of studied rock cores to be recorded along a chosen sample axis. Based on the spatially resolved relaxation maps obtained, a factor of local connectivity was introduced for the first time and calculated by using the correlation degree between adjacent relaxation time distributions originating from neighboring image slices. Consequently, a permeability profile was estimated by consecutively considering the local porosity, the spatially resolved relaxation time distribution, and the connectivity factor. Experimental results prove that permeability profiles trace the heterogeneity of rock samples. Furthermore, averaged permeabilities of rock cores were calculated while taking into account local connectivity. The values obtained approach better to brine‐permeability measurements as compared to data processing disregarding connectivity. A rapid, nondestructive method was proposed to estimate rock permeabilityRock heterogeneity can be visualized by using obtained permeability profilesThe obtained permeability values from the proposed technique were comparable to conventional approach

Since its inception in the 1970s, multi-dimensional magnetic resonance (MR) has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions. MR spectroscopy going beyond one dimension allows the study of the correlation, exchange processes, and separation of overlapping spectral information. The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media. Apart from Fourier transform, methods have been developed for processing the multi-dimensional time-domain data, identifying the fluid components, and estimating pore surface permeability via joint relaxation and diffusion spectra. Through the resolution of spectroscopic signals with spatial encoding gradients, multi-dimensional MR imaging has been proposed to investigate the microscopic environment of living tissues and distinguish diseases. Signals in each voxel are usually expressed as multi-exponential decay, representing microstructures or environments along multiple pore scales. The separation of contributions from different environments is a common ill-posed problem, which can be resolved numerically. Moreover, the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra. This paper reviews the toolbox that has been proposed to process multi-dimensional MR datasets in different scenarios. Detailed information at the microscopic level, such as tissue components in multi-disciplinary sciences, could also be revealed through multi-dimensional MR.

Heavy oil is a complicated mixture and a potential resource and has attracted much attention since the end of last century. It is important to characterize the composition of heavy oil to enhance its recovery efficiency. A designed unilateral Nuclear Magnetic Resonance (NMR) sensor with a Larmor frequency of 20 MHz and a well-defined constant gradient of 23.25 T/m was employed to acquire three-dimensional (3D) data for three heavy oil samples. The highly-constant gradient is advantageous for diffusion coefficient measurement of heavy oil. A fast data-implementation procedure including specially designed 3D pulse sequence and Inversion Laplace Transform (ILT) algorithm was adopted to process the data and extract 3D T1D-T2probability function. It indicates that NMR relaxometry and diffusometry are useful to characterize the components of heavy oil samples. NMR results were compared with independent measurements of fractionation and gas chromatography analysis.