Search Results (72)

Crack initiation, propagation, and slippage serve as the key mesoscopic mechanisms contributing to the deterioration of deep tunnel surrounding rocks. In this study, a secondary anisotropy of deep tunnels surrounding rocks was proposed: The axial-displacement constraint of deep tunnels forces cracks in the surrounding rock to initiate, propagate, and slip in planes parallel to the tunnel axial direction. These cracks have no significant effect on the axial strength of the surrounding rock but significantly reduce the tangential strength, resulting in the secondary anisotropy. First, the secondary anisotropy was verified by a hybrid stress–strain controlled true triaxial test of sandstone specimens, a CT 3D (computed tomography three-dimensional) reconstruction of a fractured sandstone specimen, a numerical simulation of heterogeneous rock specimens, and field borehole TV (television) images. Subsequently, a novel SSA (strain-softening and secondary anisotropy) constitutive model was developed to characterise the secondary anisotropy of the surrounding rock and developed using C++ into a numerical form that can be called by FLAC^3D (Fast Lagrangian Analysis of Continua in 3 Dimensions). Finally, effects of secondary anisotropy on a deep tunnel surrounding rock were analysed by comparing the results calculated by the SSA model and a uniform strain-softening model. The results show that considering the secondary anisotropy, the extent of strain-softening of the surrounding rock was mitigated, particularly the axial strain-softening. Moreover, it reduced the surface displacement, plastic zone, and dissipated plastic strain energy of the surrounding rock. The proposed SSA model can precisely characterise the objectively existent secondary anisotropy, enhancing the accuracy of numerical simulations for tunnels, particularly for deep tunnels. Full article

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model’s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

Fluid dispersion directly influences the transport, mixing, and efficiency of hydrogen storage in depleted gas reservoirs. Pore structure parameters, such as pore size, throat geometry, and connectivity, influence the complexity of flow pathways and the interplay between advective and diffusive transport mechanisms. Hence, these factors are critical for predicting and controlling flow behavior in the reservoirs. Despite its importance, the relationship between pore structure and dispersion remains poorly quantified, particularly under elevated flow conditions. To address this gap, this study employs pore network modeling (PNM) to investigate the influence of sandstone and carbonate structures on fluid flow properties at the micro-scale. Eleven rock samples, comprising seven sandstone and four carbonate, were analyzed. Pore network extraction from CT images was used to obtain detailed pore structure parameters and their statistical measures. Pore-scale simulations were conducted across 60 scenarios with varying average interstitial velocities and water as the injected fluid. Effluent hydrogen concentrations were measured to generate elution curves as a function of injected pore volumes (PV). This approach enables the assessment of the relationship between the dispersion coefficient and pore structure parameters across all rock samples at consistent average interstitial velocities. Additionally, dispersivity and n-exponent values were calculated and correlated with pore structure parameters. Full article

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows. Full article

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. Full article

The influence of porosity on rock mechanical properties constitutes a critical research focus. This investigation explores the relationship between pore structure parameters and mechanical characteristics through reconstructed numerical models. The study employs an integrated approach combining laboratory experiments and numerical simulations. Initially, high-resolution X-ray computed tomography (CT) was utilized to capture three-dimensional geometric features of Sichuan white sandstone microstructures, complemented by mechanical parameter acquisition through standardized testing protocols. The research workflow incorporated advanced image processing techniques, including adaptive total variation denoising algorithms for CT image enhancement and deep learning-based threshold segmentation for feature extraction. Subsequently, pore structure reconstruction models with controlled porosity variations were developed for systematic numerical experimentation. Key findings reveal a pronounced degradation trend in both mechanical strength and elastic modulus with increasing porosity levels. Based on simulation data, two empirical models were established: a porosity–compressive strength correlation model and a porosity–elastic modulus relationship model. These quantitative formulations provide theoretical support for understanding the porosity-dependent mechanical behavior in rock mechanics. The methodological framework and results presented in this study offer valuable insights for geological engineering applications and petrophysical characteristic analysis. Full article

Understanding the microscopic characteristics and evolutionary patterns of pore structures during high-PV waterflooding is critical for improving the accuracy and efficiency of oil field development. While previous studies have primarily emphasized the geometric and morphological features of overall pore structures, they often overlook local pore-scale properties and their relationship with fluid transport capacity. This study proposes a novel classification method for microscopic pore structures that integrates both pore size and local flow conductivity, enabling a more physically grounded and quantitatively robust evaluation of pore systems across rocks with varying permeabilities. The classification scheme divides microscopic pores into six distinct types based on key parameters such as pore diameter and flow flux area. To validate this approach, high-PV waterflooding experiments were performed on six sandstone samples with different permeabilities. High-resolution micro-computed tomography (micro-CT) imaging was employed to capture the internal pore structures before and after flooding. The results reveal that while low-connectivity small pores dominate numerically across all samples, high-connectivity small pores account for the largest volumetric share in medium-permeability rocks. Although overall pore size distributions remain relatively stable during high-PV waterflooding, transitions between pore types occur, driven by localized structural changes. Notably, in medium-permeability rocks, the number of low-connectivity small pores increases, whereas high-connectivity small pores decline. These findings deepen our understanding of microscopic heterogeneity and provide a theoretical foundation for evaluating the occurrence of residual oil. Moreover, the proposed classification framework offers valuable guidance for optimizing enhanced oil recovery strategies in the late stages of ultra-high water cut development. Full article

Oil–water relative permeability is essential for reservoir development and enhanced oil recovery (EOR). Traditional core displacement experiments assume static wettability, whereas in real reservoirs, wettability evolves over time due to waterflooding and rock–fluid interactions, significantly altering flow behavior. Existing numerical methods, including conventional lattice Boltzmann models (LBM), fail to account for these changes and lead to inaccurate predictions. This study integrates laboratory-measured contact angles into a time-dependent wettability-adjusted LBM framework, ensuring real-time wettability updates during simulation. Micro-CT imaging captures oil–water displacement and contact angle evolution at different flooding stages, which are incorporated into the Shan–Chen LBM model. Results show that neglecting the time-dependent wettability overestimates the residual oil saturation and underestimates the water-phase permeability. In contrast, our method reduces the residual oil saturation by up to 35% and expands the two-phase flow region by 15%, aligning closely with experimental observations. This approach enhances the accuracy of relative permeability modeling, providing a more reliable tool for optimizing waterflooding strategies and improving oil recovery efficiency. Full article

An understanding of the textures (grain size, grain shape, porosity, etc.), composition (mineralogy), and distribution of constituent components of geological materials such as carbonate and siliciclastic sedimentary rocks is essential in their classification, interpretation, and significance in terms of their geomechanical strength and liquid/gas storage potential. In terms of scanning electron microscopy (SEM), this is limited to relatively flat areas of selected rough surfaces, or the analysis of polished thin sections. Here, we illustrate a new technique that can image large areas of the external surface of mini-cores (approximately 10 mm or smaller in diameter) drilled from carbonate and siliciclastic rock samples. The technique utilises a specially developed horizontal rotation stage within an SEM and allows the collection of high-resolution images that can be reconstructed into realistic surface representations of the mini-core surfaces. Elemental data (representative of mineralogy) can also be added using a combined backscattered electron and X-ray (BEX) detector. Currently, these reconstructions can be used as a useful tool for the analysis of both carbonate and siliciclastic geological materials. Further work may allow such reconstructions to aid in the improvement of resolution in micro-CT scans and the direct identification of mineral phases within such scans. Full article

Understanding pore-scale oil-water two-phase flow dynamics in reservoir rocks is fundamental for optimizing petroleum exploitation. However, limitations in real-time observation have hindered comprehensive characterization of these processes. This study employs a novel three-dimensional visualization platform that integrates online micro-CT imaging (3.78 μm resolution) with oil-water displacement experiments in Berea sandstone. Experiments conducted at 20 °C and 50 °C across flow rates (0.10–0.35 mL/min) revealed distinct temperature-dependent saturation patterns: non-monotonic N-type behavior (initial increase, decrease, and then increase with flow rate) at 20 °C and V-type behavior (initial decrease followed by increase) at 50 °C, accounting for 76.0–94.3% of observed variations. Quantitative analysis demonstrated that these dominant patterns correlate with the evolution of maximum oil cluster volumes and their dynamic merging-splitting processes. Significantly, we identified temperature-sensitive preferential flow pathways that maintain stable oil phases independent of flow rate variations, occupying 17.1% and 13.6% of pore space at 20 °C and 50 °C, respectively. These findings advance our understanding of oil migration mechanisms by revealing temperature-dependent non-monotonic saturation patterns and quantifying the dynamics of preferential pathway formation, providing insights for optimizing reservoir development through enhanced characterization of fluid distribution patterns at varying depths and temperature conditions. Full article

Acid fracturing has become a crucial technology for developing carbonate reservoirs, playing a particularly significant role in enhancing oil and gas recovery. However, the retention and flowback behaviors of residual acid in fractured-vuggy carbonate reservoirs after acid fracturing remain poorly understood, and this uncertainty significantly hinders the efficient development of such reservoirs. In this study, the micro-computed tomography images of carbonate rocks were used to extract actual fracture–vug structures. A microscopic flow model for fractured-vuggy carbonate reservoirs was then designed and fabricated using wet etching techniques. Microfluidic experiments were performed to investigate the invasion and flowback behavior of residual acid within these reservoirs. This study introduces a novel approach by integrating actual fracture-vuggy structures from micro-CT images into a microfluidic model, providing a more realistic representation of fractured-vuggy carbonate reservoirs compared to previous studies that relied on simplified or idealized geometries. Additionally, the invasion coefficient (the ratio of acid invaded area to total pore area) and flowback rate (the proportion of residual acid expelled during flowback) were introduced to quantitatively assess the efficiency of acid invasion and flowback under varying flow rates, viscosities, and the presence or absence of surfactants. The results demonstrate that the invasion coefficient of residual acid increases with the injection rate, while the flowback rate decreases as the injection rate is reduced. A higher viscosity of the oil phase hinders acid invasion and results in slower flowback due to increased flow resistance in the micro model. However, the final flowback rate is higher with a higher viscosity oil phase compared to a lower viscosity phase. The addition of surfactants enhances the efficiency of acid invasion and flowback, increasing the invasion coefficient by up to 5% and the flowback rate by up to 3%. Full article

Engineering structures, including rock slopes and embankments, are vulnerable to wetting–drying cycles caused by tidal shifts and rainfall, which exacerbate mechanical degradation in hole-fissured sandstone. This study investigated the effects of 0, 10, and 20 wetting–drying cycles on sandstone samples using uniaxial compression tests combined with digital image correlation (DIC), computed tomography (CT), and scanning electron microscopy (SEM). The results revealed that wetting–drying cycles progressively reduced peak strength and the elastic modulus while increasing macroscopic crack quantity and width. Internal crack networks simplified, transitioning from tensile-dominated to combined tensile–shear and shear failure modes. An energy analysis showed diminished energy storage capacity—both the total energy density at peak stress and elastic strain energy density declined with increasing cycle numbers, whereas dissipated energy density decreased initially before rising. SEM observations indicated that wetting–drying cycles enhanced the surface roughness of the sandstone, characterized by a scaly texture, thereby compromising its structural integrity. This study provides a theoretical basis for stability and safety assessments of protective engineering systems. Full article

Objective: This study evaluated the effects of bony increased offset (BIO) and metallic augments (MAs) on primary reverse shoulder arthroplasty (RSA) baseplate stability in cadaveric specimens with variable bone densities. Methods: Thirty cadaveric specimens were analyzed in an imaging and biomechanical investigation. Computed tomography (CT) scans allowed for preoperative RSA planning and bone density analysis. Three correction methods of the glenoid were used: (1) corrective reaming with a standard baseplate, which served as the reference group (n = 10); (2) MA-RSA (n = 10); and (3) angled BIO-RSA (n = 10). Each augment group consisted of 10° (n = 5) and 20° (n = 5) corrections. Biomechanical testing included cyclic loading in an articulating setup, with optical pre- and post-cyclic micromotion measurements in a rocking horse setup. Results: There were no differences in bone density between groups based on CT scans (p > 0.126). The BIO-RSA group had higher variability in micromotion compared to the MA-RSA and reference groups (p = 0.013), and increased total micromotion compared to the reference group (p = 0.039). Both augmentations using 20° corrections had increased variance in rotational stability compared to the reference group (p = 0.043). Micromotion correlated with the subchondral bone density in the BIO-RSA group (r = −0.63, p = 0.036), but not in the MA-RSA (p > 0.178) or reference (p > 0.117) groups. Conclusions: Time-zero baseplate implant fixation is more variable with BIO-RSA and correlates with bone density. Corrections of 20° with either augmentation approach increase variability in rotational micromotion. The preoperative quantification of bone density may be useful before utilizing 20° of correction, especially when adding a bone graft in BIO-RSAs. Full article

Acid fracturing is an effective method of reservoir stimulation and has been widely used for carbonate reservoir development. However, knowledge on the propagation characteristics of acid-etched fracture is still poor due to the complexities of acidization and stress conditions, as well as the limitations of the fracture network reconstruction method, especially when dealing with large specimens. In this paper, a new method based on image-based 3D object reconstruction is proposed to study the fracture networks of specimens after acid fracturing by cutting rock specimens into thin slices, scanning them, and reconstructing 3D fracture networks. This method is more precise than the method of separating specimens into pieces and scanning, and it has advantages over the method of CT X-ray scanning when dealing with large specimens. Using this approach, the effects of natural fractures, stress conditions, and acid systems on the fracture propagation of specimens after true triaxial acid-fracturing tests were investigated. The fracture initiation and propagation patterns of specimens under different conditions were summarized. The results of the study show that the presence of a natural fracture will induce the propagation of fractures, in addition to demonstrating the positive effect of high horizontal stress difference on fracture initiation and provide an acid system conducive to the formation of a fracture network. Full article