## 1 Introduction  
The Eocene low-permeability sandstone reservoir is a major petroleum source in the Dongying Depression, Bohai Bay Basin, China [1, 2, 3]. The reservoirs are characteristic of poor petrophysical properties, complex pore structures, and strong reservoir heterogeneities owing to their terrestrial depositional environments and complex diagenetic modifications. Detailed and integrated analyses of pore structures are crucial for reservoir quality evaluation [4, 5, 6, 7], permeability prediction [8, 9, 10], and identification of reservoir formation mechanisms [11, 12]. Pore structures are defined as the geometric shape, type, size, distribution, and connectivity of pore throats, as well as the relationships among various properties [13, 14]. The complex pore systems, highly variable pore size distribution, and poor connectivity in the Eocene sandstone reservoirs pose a great challenge in the adequate characterization of their microscopic pore structures. Various analytical methods have been applied to investigate the pore structures of sandstone reservoirs, including optical petrography [15, 16, 17], SEM [18, 19], MICP [20, 21, 22], X-ray computer tomography [23, 24, 25], nitrogen gas adsorption [26, 27], and NMR [28, 29]. NMR, as a non-invasive technique, is widely used in pore structure characterization and movable fluid evaluation [30; 31; 32; 33; 34]. The NMR technique has also been successfully applied in other fields, such as CO\({}_{2}\) storage and geological modeling [35; 36]. Previous studies mainly invoked indirect analysis of NMR data, including qualitative characterization of pore size distribution [30; 37], simulation of capillary pressure curves [38; 39], and estimation of movable fluid distribution in pore space [40; 41]. Several researchers also paid attention to analyzing some sensitive NMR parameters for pore structure characterization and evaluation (e.g., NMR porosity, \(T_{2}\) geometric mean value (\(T_{\text{2 gm}}\)), \(T_{2}\) cut-off time (\(T_{\text{2 cutoff}}\)), maximum \(T_{2}\) relaxation time (\(T_{\text{2 max}}\)), porosity for effective movable fluids, and pore components (\(S_{1}\), \(S_{2}\), and \(S_{3}\)) [42; 43]. We had reported the effectiveness of fractal analysis and machine learning on pore structure characterization [3]. In this paper, we attempt to shed light on the application of the NMR \(T_{2}\) spectral parameters in pore structure evaluation. Different from the previous study, to the focus is on the morphological analysis of NMR \(T_{2}\) spectra. From the perspective of pore geometry, NMR \(T_{2}\) spectra show similar morphological characteristics with the pore size distribution derived from MICP measurements. Hence, the direct extraction of quantitative NMR parameters based on mathematical morphology analysis of NMR \(T_{2}\) spectra would be more practical. We also try to verify the feasibility of using the NMR \(T_{2}\) spectral parameters to derive pore structure information by correlating the NMR \(T_{2}\) spectral parameters, petrophysical parameters, and capillary parameters. NMR spectral parameters can also be potentially applied to evaluate pore structures and further extend the NMR logging capability. In this study, several technical methods were combined to quantify pore structure features in low-permeability sandstone reservoirs. We attempted to directly extract NMR \(T_{2}\) spectral parameters from the morphological and statistical analysis of NMR data. We also hoped to shed light on pore structure identification and evaluation using extracted NMR \(T_{2}\) spectral parameters in combination with other experimental data. In this paper, the first section describes the significance, aims, and scopes of this study. The second section presents the geological background, sampling information, and methodology; The third section investigated the petrophysical properties, pore structures, and effective movable fluid in pore space using an integrated method.