The value of T\({}_{2}\) varies according to the various relaxation mechanisms, including those associated with the surface-to-volume ratio and consequently the T\({}_{2}\) distribution depends on pore size. To obtain these distributions, a multi-exponential decay function is fitted to the overall relaxation response of the spin-echo train. Similarly, the diffusion coefficient can also be derived by analogous analysis. Given a proper relaxation model, this technique can serve as a powerful tool in the determination of the pore-size distribution and wettability tendency of the surface of interest [28; 29; 34; 35; 36; 37]. [PERSON] et al. [34] showed a relationship between the pore-size distribution and the surface relaxivity. In addition, the use of NMR analysis can allow us to analyze the influence of liquid films, previously addressed as a possible weakness of the traditional and standardized methods of wettability determination, as shown when complexation models are developed [12; 35; 38]. NMR enables inferences about pore size from relaxation rates by assuming that mobile fluids reside in large pores, bound fluids in small pores, and adhering fluids on grain surfaces. The intensity of the signal relates to the number of hydrogen nuclei found in each sample, while the rate of decay relates to the mobility of the molecules. Surface relaxation of the wetting phase will vary depending on the wettability environment within the pore space. A non-wetting fluid gives up a restricted pore space response, while the wetting phase will relax according to its degree of adhesion with the surface. The surface area (S) to pore volume (V) ratio is coupled to the surface relaxivity (\(\rho_{2}\)); for fluids confined in pores, T\({}_{2}\) is equal to or shorter than that of the bulk fluid [39]. In the fast diffusion limit, the measured T\({}_{2}\) values are given by Equation (2) [40]. As such, when surface relaxivity increases for given porous media, a decrease in T\({}_{2}\) response is expected, resulting in shifts to shorter T\({}_{2}\) values in correspondence with an increase in wettability to the fluid in use [29]. For example, if an oil-wet system is exposed to water, a shift to the left will occur, namely to shorter T\({}_{2}\) values, as the rock becomes more water-wet over time. The same response is expected for a water-wet system becoming more oil-wet when exposed to oil. In mixed-wet systems such as the case of a mix of oil-wet and water-wet glass bead, a linear relationship is observed between the relaxation response and wettability [36; 37]. Both [PERSON] et al. and [PERSON] et al. saw the same 4:1 ratio for surface relaxivity of a water-wet surface to an oil-wet surface. Plotting the NMR response by either the Amott or USBM techniques also provides a linear relationship, described as a wettability index, which improves the use of the technique and further validates it [29; 30; 32; 33]. \[\frac{1}{T_{2 surface}}=\rho_{2}(\frac{S}{V})_{pore}. \tag{2}\]  
The diffusion coefficient of fluid molecules restricted by pore surfaces depends not only on pore geometry [41], but also on pore surface characteristics [42]. In restricted geometries, the interaction with surfaces gives rise to a competition between the surface-liquid and liquid-liquid interactions. This competition determines the water spatial distribution, including water molecules on the bound water layer near the pore surface [42; 43; 44]. The pulsed field gradient NMR (PFG-NMR) can be used to analyze the diffusion coefficient of hydrogen-containing molecules restricted inside porous spaces, which is sensitive to surface-liquid interactions. T\({}_{2}\) trends similarly to T\({}_{1}\)[35], is less affected by bulk relaxation time [30] and turns out to be a relatively fast measurement. In principle, this relaxation should be a good indicator of wettability alteration, but a few challenges arise from changes in ionic profile, as the case of low-salinity or smart water injection.