Summary
Typically, smooth solid surfaces of reservoir rocks are assumed in formation evaluation, such as nuclear-magnetic-resonance (NMR) petrophysics and reservoir-wettability characterization through contact-angle measurements. Measuring the degree of surface roughness (R), or smoothness, and evaluating its effects on formation evaluation are topics of much research. In this paper, we primarily focus on details in characterizing solid-surface roughness and its applications in NMR pore-sizeanalysis.
R can be measured by contact techniques and noncontact techniques, such as stylus profilometer, atomic-force microscopy, and different kinds of optical measurements. Each technique has different sensitivities, measurement artifacts, resolutions, and field of view (FOV). Intuitively, although a finer resolution measurement provides the closest account of all surface details, the correspondingly small FOV might compromise the representativeness of the measurement, which is particularly challenging for charactering heterogeneous samples such as carbonates. To balance the FOV and measurement representativeness, and to minimize artifacts, laser scanner confocal microscopy (LSCM) is selected in this study.
Results for the more than 27 rock samples tested indicate that rocks of similar rock types have similar R-values. Grainy limestones have relatively higher R-values compared with dolostones, consistent with the dolostone’s crystallization surface features. Muddy limestones have smoother surfaces, resulting in the lowest R-values among the rocks studied. For sandstones, R varies with clay types and content. For rocks containing two distinct minerals, two R-values are observed from the R profiles, which for these rock types justifies the use of two NMR surface relaxivity (ρ2) parameters for determining the pore-size distribution (PSD) from the NMR T2distribution.
The novelty here is the integration of LSCM and NMR to obtain an NMR PSD relevant for permeability, capillary pressure, and other petrophysical parameters. Typically, ρ2 is calibrated using the total surface area from Brunauer-Emmett-Teller (BET; Brunauer et al. 1938) gas adsorption, but this underestimates the NMR pore size because of surface-roughness effects. In our novel approach, we use R measured from LSCM to correct ρ2 for surface-roughness effects, and thereby obtain the NMR pore size more relevant for permeability and other petrophysical parameters. We then compare the roughness-corrected NMR PSD against pore size from microcomputed tomography (micro-CT) scanning (which is roughness independent). The good agreement between roughness-corrected NMR and micro-CT pore sizes in the micropore region validates our new technique, and highlights the importance of surface-roughness characterization in NMR petrophysics.
Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India