Summary Surface roughness is an essential rock parameter affecting petrophysical properties that are surface sensitive such as characterization of pore structure and wettability. For instance, Wenzel’s contact angle formula for rough surfaces requires knowledge of the surface roughness, and surface roughness is expected to speed up the aging of cores in crude oil for wettability restoration. In addition, proper quantification of surface roughness is critical for obtaining representative, roughness-independent, pore sizes for applications such as prediction of permeability and interpretation of capillary pressure curves. Intuitively, a surface is better characterized in 2D than in 1D. This 2D study is a continuation and enhancement of the previous 1D work, recently published in the SPE Journal (Ma et al. 2021). In this current paper, a comprehensive investigation of 1D vs. 2D surface roughness measurements is conducted to evaluate and cross validate the two approaches. In this study, surface roughness is measured on 26 carbonate rock samples by laser scanning confocal microscopy (LSCM), where both the 1D absolute increment surface roughness, Sr, and the 2D interfacial area ratio of surface roughness, Sdr, are reported. As expected, the results indicate that surface roughness characterized by 2D Sdr has a greater dynamic range than the 1D Sr measurement, i.e., the 2D Sdr provides a more representative characterization of surface roughness. A detailed account of methodologies, assumptions, limitations, validation, and applications of the 1D and 2D surface roughness characterization is documented in this paper. To extract the roughness features present on rock grain surfaces, effects of de-spiking and filter length, used to eliminate pore-size effects, are investigated. For specific applications of surface roughness corrected pore-size estimation from nuclear magnetic resonance (NMR) measurements, differences in length scales of surface roughness are compared between LSCM measurement and that derived from NMR diffusion-T2 plus BET (Brunauer-Emmett-Teller) surface area. The surface roughness-corrected NMR pore-size distribution is also validated against the pore-size distribution obtained from the measurement of micro-computed tomography (CT) scanning.

Surface roughness is an essential rock parameter affecting petrophysical properties that are surface sensitive such as characterization of pore structure and wettability. For instance, Wenzel's contact angle formula for rough surfaces requires knowledge of the surface roughness, and surface roughness is expected to speed up aging of cores in crude oil for wettability restoration. In addition, proper quantification of surface roughness is critical for obtaining representative, roughness-independent, pore sizes for applications such as prediction of permeability and interpretation of capillary pressure curves. Intuitively, a surface is better characterized in 2D than in 1D. This 2D study is a continuation and enhancement of the previous 1D work, recently published in the SPE Journal (Ma et al., 2021). In this current paper, a comprehensive investigation of 1D versus 2D surface roughness measurements is conducted to evaluate and cross validate the two approaches. In this study, surface roughness is measured on 26 carbonate rock samples by laser scanning confocal microscopy (LSCM), where both the 1D absolute increment surface roughness, Sr, as well as 2D interfacial area ratio of surface roughness, Sdr, are reported. As expected, results indicate that surface roughness characterized by 2D Sdr has a greater dynamic range than the 1D Sr measurement, i.e., the 2D Sdr provides a more representative characterization of surface roughness. A detailed account of methodologies, assumptions, limitations, validation and applications of the 1D and 2D surface roughness characterization is documented in this paper. To extract the roughness features present on rock grain surfaces, effects of de-spiking and filter length, used to eliminate pore size effects, are investigated. For specific applications of surface roughness corrected pore size estimation from nuclear magnetic resonance (NMR) measurements, differences in length-scales of surface roughness are compared between LSCM measurement and that derived from NMR diffusion-T2 plus BET surface area. The surface roughness-corrected NMR pore-size distribution is also validated against the pore-size distribution obtained from measurement of micro-CT scanning.

The formation evaluation of Saudi Arabian reservoirs presents multiple challenges. The complexities encountered include varying mineralogy and mixed lithologies, a wide range of porosities and pore types, hydrocarbon viscosity, and variable formation water salinities.Two-dimensional (2D) analysis of NMR data acquired with simultaneous T1-T2 has proven to be beneficial for the identification and quantification of hydrocarbon-bearing reservoirs and providing valuable information about porosity and reservoir quality.NMR porosity measurements are free from mineralogical effects and, therefore, provide a very good estimate of formation porosity. Moveable and bound fractional fluid porosities from NMR provide additional reservoir information and are used for estimating permeability. Simultaneous T1-T2 acquisition and two dimensional analyses provide graphic 2D identification for the presence of hydrocarbons and hydrocarbon type, as well as a volumetric estimate of near wellbore hydrocarbons independent of formation water resistivity.Results from a simultaneous NMR T1-T2 acquisition are compared to formation tester results. The strong correlation between the NMR predictions and the formation tester results suggests this method is effective in the evaluation of challenging formations and might also be applicable to other reservoirs.

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A refined radial-basis-function (RBF) method with a forward selection algorithm to improve the stability of the prediction of pore throat sizes was recently reported by the authors. Subsequently, from the pore throat size distribution data, permeability and pore typing models were developed. These models were developed with the core samples from the high-to-medium quality reservoir sections of several Middle East carbonate wells. Because the RBF is an interpolation method, the validity of RBF based petrophysical models is enveloped by the petrophysical parameter range that the core samples represent. For economic reasons, it is common practice that core analysis be conducted on high reservoir quality rock samples because they are most important to production. To apply RBF-based models for interpreting well logging data, it is important that such models be developed with a broad range of rock qualities to help prevent misinterpreting the lower-quality formation rocks. To expand the application envelop of the RBF based nuclear magnetic resonance (NMR) permeability models, a new set of core measurements from different reservoir quality sections, as well as non-reservoir quality sections of several carbonate wells, are added to retrain the RBF-based NMR permeability models. Standard statistical validation methods are used to demonstrate the necessity and improvements of newly retrained RBF-based models. The new models are applied to well logging data with varying reservoir quality sections, proving that the new models are adequate for better permeability prediction of all rock quality formations.