Experimental Study on the Relationship Between Capillary Pressure and Resistivity Index in Tight Sandstone Rocks

To further understand the relationship between the capillary pressure and resistivity index of tight reservoir sandstones, we measured these parameters using the semiporous plate method in four tight clayey sandstones from the Junggar Basin, China. To provide a benchmark for analyzing our results, we applied the same experimental procedures to four clay-free, tight Fontainebleau sandstones from the Paris Basin, France. We observed approximate power law relationships between the resistivity index and water saturation for Fontainebleau sandstones and Junggar Basin sandstones, in agreement with the Archie’s equation and Waxman-Smits equation, respectively. The capillary pressure and resistivity index data were differently related in the two types of sandstones. We observed a power law correlation for the Fontainebleau sandstones, whereas the Junggar sandstones showed a linear relationship. These differences in behavior appear to be primarily linked to the presence of clays in the Junggar sandstones but not in the Fontainebleau sandstone, and the difference in pore structure between the two types of sandstones.

Critical role of the structure of the mineral-water interface in the zeta potential measured by streaming potential method

<p><span>In this study, zeta potential has been measured by using the streaming potential method for the intact sandstone in contact with CaCl<sub>2</sub> electrolytes. The experimental results show that a positive zeta potential has been observed for the first time for the intact Fontainebleau sandstone under high salinity of CaCl<sub>2</sub>, and its magnitude increases with increasing ionic strength. It cannot be explained by the Gouy-Chapman theory anticipating a constant potential for high salinities due to the collapse of the electrical double layer. Meanwhile, the brine effluents after the completion of the streaming potential measurements were collected and then pH and brine composition were analysed suggesting that those variations of pH and chemical composition are negligible and cannot explain the polarity change at high salinity. The anomalous positive potential of the intact Fontainebleau sandstone is due to that overcharge of calcium ions sorbed into the mineral surface, which is consistence with previous literature data.</span></p>

Pore-scale permeability prediction for Newtonian and non-Newtonian fluids

The flow of fluids through porous media such as groundwater flow or magma migration is a key process in geological sciences. Flow is controlled by the permeability of the rock; thus, an accurate determination and prediction of its value is of crucial importance. For this reason, permeability has been measured across different scales. As laboratory measurements exhibit a range of limitations, the numerical prediction of permeability at conditions where laboratory experiments struggle has become an important method to complement laboratory approaches. At high resolutions, this prediction becomes computationally very expensive, which makes it crucial to develop methods that maximize accuracy. In recent years, the flow of non-Newtonian fluids through porous media has gained additional importance due to, e.g., the use of nanofluids for enhanced oil recovery. Numerical methods to predict fluid flow in these cases are therefore required. Here, we employ the open-source finite difference solver LaMEM (Lithosphere and Mantle Evolution Model) to numerically predict the permeability of porous media at low Reynolds numbers for both Newtonian and non-Newtonian fluids. We employ a stencil rescaling method to better describe the solid–fluid interface. The accuracy of the code is verified by comparing numerical solutions to analytical ones for a set of simplified model setups. Results show that stencil rescaling significantly increases the accuracy at no additional computational cost. Finally, we use our modeling framework to predict the permeability of a Fontainebleau sandstone and demonstrate numerical convergence. Results show very good agreement with experimental estimates as well as with previous studies. We also demonstrate the ability of the code to simulate the flow of power-law fluids through porous media. As in the Newtonian case, results show good agreement with analytical solutions.

Pore-scale permeability prediction for Newtonian and non-Newtonian fluids

The flow of fluids through porous media such as groundwater flow or magma migration are key processes in geological sciences. Flow is controlled by the permeability of the rock, thus an accurate determination and prediction of its value is of crucial importance. For this reason, permeability has been measured across different scales. As laboratory measurements exhibit a range of limitations, the numerical prediction of permeability at conditions where laboratory experiments struggle has become an important method to complement laboratory approaches. At high resolutions, this prediction becomes computationally very expensive, which makes it crucial to develop methods that maximize accuracy. In recent years, the flow of non-Newtonian fluids through porous media has gained additional importance due to e.g., the use of nanofluids for enhanced oil recovery. Numerical methods to predict fluid flow in these cases are therefore required. Here, we employ the open-source finite difference solver LaMEM to numerically predict the permeability of porous media at low Reynolds numbers for both Newtonian as well as non-Newtonian fluids. We employ a stencil rescaling method to better describe the solid-fluid interface. The accuracy of the code is verified by comparing numerical solutions to analytical ones for a set of simplified model setups. Results show that stencil rescaling significantly increases the accuracy at no additional computational cost. Finally, we use our modeling framework to predict the permeability of a Fontainebleau sandstone, and demonstrate numerical convergence. Results show very good agreement with experimental estimates as well as with previous studies. We also demonstrate the ability of the code to simulate the flow of power law fluids through porous media. As in the Newtonian case, results show good agreement with analytical solutions.

Deep Network-Based Feature Extraction and Reconstruction of Complex Material Microstructures

Computational material design (CMD) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable CMD, it is required that (1) a feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. Existing solutions rely on the designer’s understanding of specific material systems to identify geometric and statistical features, which could be insufficient for reconstructing physically meaningful microstructures of complex material systems. This paper develops a feature learning mechanism that automates a two-way conversion between microstructures and their lower-dimensional feature representations. The proposed model is applied to four material systems: Ti-6Al-4V alloy, Pb-Sn alloy, Fontainebleau sandstone, and spherical colloids, to produce random reconstructions that are visually similar to the samples. This capability is not achieved by existing synthesis methods relying on the Markovian assumption of material systems. For Ti-6Al-4V alloy, we also show that the reconstructions preserve the mean critical fracture force of the system for a fixed processing setting. Source code and datasets are available.