The measurement of physical parameters of porous rock, which constitute reservoirs, is an essential part of hydrocarbon exploration. Typically, the measurement of these physical parameters is carried out through core analysis in a laboratory, which requires considerable time and high costs. Another approach involves using digital rock models, where the physical parameters are calculated through image processing and numerical simulations. However, this method also requires a significant amount of time for estimating the physical parameters of each rock sample. Machine learning, specifically convolutional neural network (CNN) algorithms, has been developed as an alternative method for estimating the physical parameters of porous rock in a shorter time frame. The advancement of CNN, particularly through transfer learning using pre-trained models, has contributed to rapid prediction capabilities. However, not all pre-trained models are suitable for estimating the physical parameters of porous rock. In this study, transfer learning was applied to estimate parameters of sandstones such as porosity, specific surface area, average grain size, average coordination number, and average throat radius. Six types of pre-trained models were utilized: ResNet152, DenseNet201, Xception, InceptionV3, InceptionResNetV2, and MobileNetV2. The results of this study indicate that the DenseNet201 model achieved the best performance with an error rate of 2.11%. Overall, this study highlights the potential of transfer learning to ultimately lead to more efficient and effective computation.

A new positive muon position in La₂CuO₄ is suggested from electronic and nuclear dipoles simulations. The muon site was found to be close to the center of a triangle plane of the CuO₆ octahedral that was formed by the apical and two in-plane oxygen. The magnetic moment of the Cu spin was estimated to be 0.66 μ_B/Cu in order to optimize the muon site. We also applied our method to the new vanadium compound, V₄S₉Br₄, in which four muon sites were observed in a magnetically ordered state.

This study aims to enhance the understanding of fluid flow and effective properties in three-dimensional (3D) porous rock by combining Lattice Boltzmann Method (LBM) simulations with image-based correlation techniques. The effective properties analyzed include absolute porosity, effective porosity, specific surface area, coordination number, tortuosity, permeability, and average throat size. These parameters were estimated using digital rock images from various sandstone samples, preprocessed to isolate pore networks and simulate fluid flow under creeping flow conditions. The results demonstrate that effective porosity and tortuosity exhibit strong correlations with permeability, with correlation coefficients of 0.91 and −0.65, respectively. In contrast, the coordination number shows a weaker correlation, likely due to rock heterogeneity and the presence of ineffective porosity. A comparison between permeability values derived from LBM simulations and the empirical Kozeny–Carman model reveals that both methods provide representative estimates, with LBM offering a reliable alternative for analyzing complex rock structures. The permeability distribution follows a lognormal trend, influenced by the intricate geometry of pore networks. This study highlights the effectiveness of combining LBM simulations with image-based techniques for accurately characterizing fluid flow and effective properties in 3D porous rock, providing valuable insights for applications in petroleum engineering and geophysics.

Understanding tortuosity is essential for accurately modeling fluid flow in complex porous media, particularly in the sub-surface reservoir rock; therefore, tortuosity estimation was evaluated using three approaches: Streamline streamline simulations via the Lattice Boltzmann Method (LBM), geometric pathfinding using Dijkstra’s algorithm, and empirical modeling based on pore-structure parameters. The analysis encompassed 1963 micro-Computed Tomography (micro-CT) images of Brazilian pre-salt carbonate and sandstone samples, with the effective porosity extracted from LBM velocity fields, isolating flow-contributing pores, establishing streamline tortuosity as the reference standard. Sandstones exhibited relatively narrow tortuosity ranges (Dijkstra: 1.29–1.75; Streamline: 1.18–2.61; Empirical: 1.18–4.42), whereas carbonates display greater heterogeneity (Dijkstra: 1.00–3.18; Streamline: 1.00–3.68; Empirical: 1.59–4.93). Model performance assessed using the corrected Akaike Information Criterion (AICc) revealed that the best agreement with the data was achieved by the semi-empirical model incorporating coordination number and minimum throat length (AICc = −113.11), followed by the Dijkstra-based geometrical approach (−99.74) and the empirical porosity-based model (202.23). There was a nonlinear inverse correlation between tortuosity and effective porosity across lithologies. This comprehensive comparison underscores the importance of incorporating multiple pore-scale parameters for robust tortuosity prediction, improving the understanding of flow behavior in heterogeneous reservoir rocks.

The study of Land Use Land Cover (LULC) is essential to understanding how land has been altered in recent years and what has caused the processes behind the change. This is significant for the future development of the area, particularly on the campus of the Universitas Padjadjaran Jatinangor. The purpose of this study was to apply remote-sensing techniques to map a university campus and vicinity by comparing the area of urban green space (UGS) and floor area ratios (FARs) of the campus in 2015 and 2017. Additionally, surface runoff analysis was also conducted. For our research, we used WorldView-2’s high-resolution satellite imagery with a resolution of 0.46 m in the Universitas Padjadjaran (Padjadjaran University, or Unpad) Jatinangor campus, Jawa Barat, Indonesia. Our approach was to interpret the imagery by running the normalized difference vegetation index (NDVI) to distinguish UGS and FAR and using digital elevation model (DEM) interferometric synthetic aperture radar (SAR) data with hydrologic analysis to identify the direction of surface runoff. The results obtained are as follows: the UGS remained more extensive compared with FAR, but the difference decreased over time owing to infrastructure development. Surface runoff has tended to flow toward the southeast in direct relation to the slope configuration.

Several numerical simulations of fluid flow were performed using the Lattice Boltzmann method and image processing techniques to estimate the effective properties of 2-D porous rocks. The effective properties evaluated were the physical characteristics that allow fluid flow including the effective porosity, permeability, tortuosity, and average throat size to determine the storage and transport of fluids in porous rocks. The permeability was compared using the Darcy model simulation and the empirical Kozeny–Carman Equation. The results showed that the Lattice Boltzmann method and image processing techniques can estimate the effective parameters of porous rocks. Furthermore, there was a good correlation between permeability and parameters such as effective porosity, tortuosity, and average throat size. The Darcy model simulation revealed a gamma distribution in the permeability, while the empirical Kozeny–Carman Equation showed a log-normal distribution.