Search Results (1,103)

The process of geothermal energy development may cause induced seismic activities, posing a potential threat to the sustainable utilization and safety of geothermal energy. To effectively evaluate the danger of induced seismic activities, this paper establishes an artificial neural network model and selects nine influencing factors as the input parameters of the neurons. Based on the results of induced seismic activity under different parameter conditions, a sensitivity analysis is conducted for each parameter, and the influence degree of each parameter on the magnitude of induced seismic activity is ranked from largest to smallest as follows: in situ stress state, fault presence or absence, depth, degree of fracture aggregation, maximum in situ stress, distance to fault, injection volume, fracture dip angle, angle between fracture, and fault. Then, the weights of each parameter in the model are modified to improve the accuracy of the model. Finally, through data collection and the literature review, the Pohang EGS project in South Korea is analyzed, and the induced seismic activity influencing factors of the Pohang EGS site are analyzed and evaluated using the induced seismic activity evaluation model. The results show that the induced seismicity are all located below 3.7 km (drilling depth). As the depth increases, the seismicity magnitude also shows a gradually increasing trend. An increase in injection volume and a shortening of the distance from faults will also lead to an increase in the seismicity magnitude. When the injection volume approaches 10,000 cubic meters, the intensity of the seismic activity sharply increases, and the maximum magnitude reaches 5.34, which is consistent with the actual situation. This model can be used for the induced seismic evaluation of future EGS projects and provide a reference for project site selection and induced seismic risk warning. Full article

In order to design new materials at atomic-length scales, there is a need to connect the fractal nature of fracture surfaces at the atomic scale using quantum mechanics modeling with that of the experimental data of fracture surfaces at macroscopic-length scales. We use a semi-empirical quantum mechanics simulation of fracture in amorphous silica to calculate a parameter identified as a critical characteristic length, $a_0$, which has been experimentally derived from the fractal nature of fracture for many materials that fail in a brittle matter. To our knowledge, there are no known simulation models other than our related research that use the fractal parameter $a_0$ to describe the fractal fracture of the fracture surface using quantum mechanical simulations. We provide evidence that $a_0$ can be calculated at both the atomic and macroscopic scale, making it a fundamental property of the structure and one of the elements of fractal fracture. We use a continuous random network model and reaction coordinate method to simulate fracture. We propose that fracture in amorphous silica occurs due to bond reconfiguration resulting in increased strain volume at the crack tip. We hypothesize two specific configurations leading to fracture from a four-fold ring reconfiguration to three-fold ring or (newly observed) five-fold ring configurations resulting in a change in volume. Finally, we define a reconfiguration fracture energy at the atomic level, which is approximately the value of the experimental fracture surface energy. Full article

A Cu-11.85Al-3.2Mn-0.1Ti shape memory alloy (SMA) with excellent superelasticity and shape memory effect was successfully fabricated via selective laser melting (SLM). Increasing the energy density enhanced grain refinement, achieving a 90% refinement rate compared to cast alloy, with an average width of ~0.15 µm. Refined martensite lowered transformation temperatures and increased thermal hysteresis. Nanoscale Cu₂TiAl phases precipitated densely within the matrix, forming a dual strengthening network combining precipitation hardening and dislocation hardening. This mechanism yielded a room-temperature tensile strength of 829.07 MPa, with 6.38% fracture strain. At 200 °C, strength increased to 883.68 MPa, with 12.26% strain. The maximum tensile strength represents a nearly 30% improvement on existing laser-melted quaternary Cu-based SMAs. Full article

Enhanced Geothermal Systems (EGS) require thermochemically stable proppant materials capable of sustaining fracture conductivity under harsh subsurface conditions. This study systematically investigates the response of commercial proppants to coupled thermo-hydro-chemical (THC) effects, focusing on chemical stability and microstructural evolution. Four proppant types were evaluated: an ultra-low-density ceramic (ULD), a resin-coated sand (RCS), and two quartz-based silica sands. Experiments were conducted under simulated EGS conditions at 130 °C with daily thermal cycling over a 25-day period, using diluted site-specific Utah FORGE geothermal fluids. Static batch reactions were followed by comprehensive multi-modal characterization, including scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), and micro-computed tomography (micro-CT). Proppants were tested in both granular and powdered forms to evaluate surface area effects and potential long-term reactivity. Results indicate that ULD proppants experienced notable resin degradation and secondary mineral precipitation within internal pore networks, evidenced by a 30.4% reduction in intragranular porosity (from CT analysis) and diminished amorphous peaks in the XRD spectra. RCS proppants exhibited a significant loss of surface carbon content from 72.98% to 53.05%, consistent with resin breakdown observed via SEM imaging. While the quartz-based sand proppants remained morphologically intact at the macro-scale, SEM-EDS revealed localized surface alteration and mineral precipitation. The brown sand proppant, in particular, showed the most extensive surface precipitation, with a 15.2% increase in newly detected mineral phases. These findings advance understanding of proppant–fluid interactions under low-temperature EGS conditions and underscore the importance of selecting proppants based on thermo-chemical compatibility. The results also highlight the need for continued development of chemically resilient proppant formulations tailored for long-term geothermal applications. Full article

Loess, a typical soil widely distributed in China, exhibits engineering properties that are highly sensitive to environmental changes, leading to increased erosion and the development of surface cracks. This article examines the influence of initial moisture content, dry density, and thickness on crack formation in compacted loess subjected to wet–dry cycles, using both laboratory experiments and numerical simulation analysis. It quantitatively analyzes the process of crack evolution using digital image processing technology. The experimental results indicate that wet–dry cycles can cause cumulative damage to the soil, significantly encouraging the initiation and expansion of secondary cracks. New cracks often branch out and extend along the existing crack network, demonstrating that the initial crack morphology has a controlling effect over the final crack distribution pattern. Numerical simulations based on MultiFracS software further revealed that soil samples with a thickness of 0.5 cm exhibited more pronounced surface cracking characteristics than those with a thickness of 2 cm, with thinner layers of soil tending to form a more complex network of cracks. The simulation results align closely with the indoor test data, confirming the reliability of the established model in predicting fracture dynamics. The study provides theoretical underpinnings and practical guidance for evaluating the stability of engineering slopes and for managing and mitigating fissure hazards in loess. Full article

This paper provides research that shows that the scale and quantification of the degree of fracturing in a rock mass should and can be considered when estimating geological strength index (GSI) ratings for rock mass strength and deformability estimates. In support of this notion, a brief review is provided to demonstrate why it is imperative that scale is considered when using GSI in engineering design. The impact of scale and scale effects on the engineering response of a rock mass typically requires a definition of fracture intensity relative to the volume or size of rock mass under consideration and the relative scale of the project being built. In this research three volume scales are considered: the volume of a structural domain, a representative elemental REV, and unit volume. A theoretical framework is established that links these three volume scales together, how they are estimated, and how they relate to parameters used to estimate engineering behaviour. Analysis of data from several examples and case histories for real rock masses is presented that compares and validates the use of a new and innovative but practical method (a sphere of unit volume) to estimate fracture intensity parameters VFC or P30 (fractures/m³) and P32 (fracture area—m²/m³) that is included on the vertical axis of the volumetric V-GSI chart. The research demonstrates that the unit volume approach to calculating VFC and P32 used in the V-GSI system compares well with other methods of estimating these two parameters (e.g., discrete fracture network (DFN) modelling). The research also demonstrates the reliability of the VFC-correlated rating scale included on the vertical axis of the V-GSI chart for use in estimating first-order strength and deformability estimates for rock masses. This quantification does not negate or detract from geological logic implicit in the original graphical GSI chart. Full article

Against the backdrop of global energy transition and strict environmental regulations, supercritical carbon dioxide (scCO₂) fracturing and oil displacement technologies have emerged as pivotal green approaches in shale gas exploitation, offering the dual advantages of zero water consumption and carbon sequestration. However, the inherent low viscosity of scCO₂ severely restricts its sand-carrying capacity, fracture propagation efficiency, and oil recovery rate, necessitating the urgent development of high-performance thickeners. The current research on scCO₂ thickeners faces a critical trade-off: traditional fluorinated polymers exhibit excellent philicity CO₂, but suffer from high costs and environmental hazards, while non-fluorinated systems often struggle to balance solubility and thickening performance. The development of new thickeners primarily involves two directions. On one hand, efforts focus on modifying non-fluorinated polymers, driven by environmental protection needs—traditional fluorinated thickeners may cause environmental pollution, and improving non-fluorinated polymers can maintain good thickening performance while reducing environmental impacts. On the other hand, there is a commitment to developing non-noble metal-catalyzed siloxane modification and synthesis processes, aiming to enhance the technical and economic feasibility of scCO₂ thickeners. Compared with noble metal catalysts like platinum, non-noble metal catalysts can reduce production costs, making the synthesis process more economically viable for large-scale industrial applications. These studies are crucial for promoting the practical application of scCO₂ technology in unconventional oil and gas development, including improving fracturing efficiency and oil displacement efficiency, and providing new technical support for the sustainable development of the energy industry. This study innovatively designed an amphiphilic modified amino silicone oil polymer (MA-co-MPEGA-AS) by combining maleic anhydride (MA), methoxy polyethylene glycol acrylate (MPEGA), and amino silicone oil (AS) through a molecular bridge strategy. The synthesis process involved three key steps: radical polymerization of MA and MPEGA, amidation with AS, and in situ network formation. Fourier transform infrared spectroscopy (FT-IR) confirmed the successful introduction of ether-based CO₂-philic groups. Rheological tests conducted under scCO₂ conditions demonstrated a 114-fold increase in viscosity for MA-co-MPEGA-AS. Mechanistic studies revealed that the ether oxygen atoms (Lewis base) in MPEGA formed dipole–quadrupole interactions with CO₂ (Lewis acid), enhancing solubility by 47%. Simultaneously, the self-assembly of siloxane chains into a three-dimensional network suppressed interlayer sliding in scCO₂ and maintained over 90% viscosity retention at 80 °C. This fluorine-free design eliminates the need for platinum-based catalysts and reduces production costs compared to fluorinated polymers. The hierarchical interactions (coordination bonds and hydrogen bonds) within the system provide a novel synthetic paradigm for scCO₂ thickeners. This research lays the foundation for green CO₂-based energy extraction technologies. Full article

Lost circulation is a major challenge in oil and gas drilling operations, severely restricting drilling efficiency and compromising operational safety. Conventional bridging and plugging materials rely on precise particle-to-fracture size matching, resulting in low success rates. Self-healing gels penetrate loss zones as discrete particles that progressively swell, accumulate, and self-repair in integrated gel masses to effectively seal fracture networks. Self-healing gels effectively overcome the shortcomings of traditional bridging agents including poor adaptability to fractures, uncontrollable gel formation of conventional downhole crosslinking gels, and the low strength of conventional pre-crosslinked gels. This work employs stearyl methacrylate (SMA) as a hydrophobic monomer, acrylamide (AM) and acrylic acid (AA) as hydrophilic monomers, and graphene oxide (GO) as an inorganic dopant to develop a GO-based self-healing organic–inorganic hybrid plugging material (SG gel). The results demonstrate that the incorporation of GO significantly enhances the material’s mechanical and rheological properties, with the SG-1.5 gel exhibiting a rheological strength of 3750 Pa and a tensile fracture stress of 27.1 kPa. GO enhances the crosslinking density of the gel network through physical crosslinking interactions, thereby improving thermal stability and reducing the swelling ratio of the gel. Under conditions of 120 °C and 6 MPa, SG-1.5 gel demonstrated a fluid loss volume of only 34.6 mL in 60–80-mesh sand bed tests. This gel achieves self-healing within fractures through dynamic hydrophobic associations and GO-enabled physical crosslinking interactions, forming a compact plugging layer. It provides an efficient solution for lost circulation control in drilling fluids. Full article

The investigation of the pore structure and fractal characteristics of coal-bearing shale is critical for unraveling reservoir heterogeneity, storage-seepage capacity, and gas occurrence mechanisms. In this study, 12 representative Upper Paleozoic coal-bearing shale samples from the Yangquan Block of the Qinshui Basin were systematically analyzed through field emission scanning electron microscopy (FE-SEM), high-pressure mercury intrusion, and gas adsorption experiments to characterize pore structures and calculate multi-scale fractal dimensions (D₁–D₅). Key findings reveal that reservoir pores are predominantly composed of macropores generated by brittle fracturing and interlayer pores within clay minerals, with residual organic pores exhibiting low proportions. Macropores dominate the total pore volume, while mesopores primarily contribute to the specific surface area. Fractal dimension D₁ shows a significant positive correlation with clay mineral content, highlighting the role of diagenetic modification in enhancing the complexity of interlayer pores. D₂ is strongly correlated with the quartz content, indicating that brittle fracturing serves as a key driver of macropore network complexity. Fractal dimensions D₃–D₅ further unveil the synergistic control of tectonic activity and dissolution on the spatial distribution of pore-fracture systems. Notably, during the overmature stage, the collapse of organic pores suppresses mesopore complexity, whereas inorganic diagenetic processes (e.g., quartz cementation and tectonic fracturing) significantly amplify the heterogeneity of macropores and fractures. These findings provide multi-scale fractal theoretical insights for evaluating coal-bearing shale gas reservoirs and offer actionable recommendations for optimizing the exploration and development of Upper Paleozoic coal-bearing shale gas resources in the Yangquan Block of the Qinshui Basin. Full article

Accurate detection of fractures in X-ray images is important to initiate appropriate medical treatment in time—in this study, an advanced combined attention CNN model with multiple attention mechanisms was developed to improve fracture detection by deeply representing features. Specifically, our model incorporates squeeze blocks and convolutional block attention module (CBAM) blocks to improve the model’s ability to focus on relevant features in X-ray images. Using computed tomography X-ray images, this study assesses the diagnostic efficacy of the artificial intelligence (AI) model before and after optimization and compares its performance in detecting fractures or not. The training and evaluation dataset consists of fractured and non-fractured X-rays from various anatomical locations, including the hips, knees, lumbar region, lower limb, and upper limb. This gives an extremely high training accuracy of 99.98 and a validation accuracy 96.72. The attention-based CNN thus showcases its role in medical image analysis. This aspect further complements a point we highlighted through our research to establish the role of attention in CNN architecture-based models to achieve the desired score for fracture in a medical image, allowing the model to generalize. This study represents the first steps to improve fracture detection automatically. It also brings solid support to doctors addressing the continued time to examination, which also increases accuracy in diagnosing fractures, improving patients’ outcomes significantly. Full article

Naturally fractured reservoirs (NFRs) account for a significant portion of the world’s oil and gas reserves. Among them, corridor-type NFRs, characterized by discrete fracture corridors, exhibit complex flow behavior that challenges conventional development strategies and reduces recovery efficiency. A review of previous studies indicates that failing to identify these corridors often leads to suboptimal recovery, whereas correctly detecting and utilizing them can significantly enhance production. This study introduces a well-testing technique designed to identify fracture corridors and to evaluate well placement for improved recovery prediction. A simplified modeling framework is developed, combining a local model for matrix/fracture wells with a global continuous-media model representing the corridor network. Diagnostic pressure and derivative plots are used to estimate corridor properties—such as spacing and conductivity—and to determine a well’s location relative to fracture corridors. The theoretical analysis is supported by numerical simulations in CMG, which confirm the key diagnostic features and flow regime sequences predicted by the model. The results show that diagnostic patterns can be used to infer fracture corridor characteristics and to approximate well positions. The proposed method enables early-stage structural interpretation and supports practical decision-making for well placement and reservoir management in corridor-type NFRs. Full article

This study investigates the impact of mineralogical and petrographic characteristics on the mechanical behavior of granitic and mafic rocks from the Shuangjiangkou (Sichuan Province) and Damiao complexes (Hebei Province) in China. The research methodology combined petrographic investigation, comprising optical microscopy and Scanning Electron Microscopy–Energy-Dispersive X-ray Spectroscopy (SEM-EDS) methods, with methodical geotechnical characterization to establish quantitative relationships between mineralogical composition and engineering properties. The petrographic studies revealed three lithologic groups: fine-to-medium-grained Shuangjiangkou granite (45%–60% feldspar, 27%–35% quartz, 10%–15% mica), plagioclase-rich anorthosite (more than 90% of plagioclase), and intermediate mangerite (40%–50% of plagioclase, 25%–35% of perthite). The uniaxial compressive strength tests showed great variations: granite (127.53 ± 15.07 MPa), anorthosite (167.81 ± 23.45 MPa), and mangerite (205.12 ± 23.87 MPa). Physical properties demonstrated inverse correlations between mechanical strength and both water absorption (granite: 0.25%–0.42%; anorthosite: 0.07%–0.44%; mangerite: 0.10%–0.25%) and apparent porosity (granite: 0.75%–0.92%; anorthosite: 0.20%–1.20%; mangerite: 0.29%–0.69%), with positive correlations to specific gravity (granite: 1.88–3.03; anorthosite: 2.67–2.90; mangerite: 2.43–2.99). Critical petrographic features controlling mechanical behavior include the following: (1) mica content in granite creating anisotropic properties, (2) extensive feldspar alteration through sericitization increasing microporosity and reducing intergranular cohesion, (3) plagioclase micro-fracturing and alteration to clinozoisite–sericite assemblages in anorthosite creating weakness networks, and (4) mangerite’s superior composition of >95% hard minerals with minimal sheet mineral content and limited alteration. Failure mode analysis indicated distinct patterns: granite experiencing shear-dominated failure (30–45° diagonal planes), anorthosite demonstrated tensile fracturing with vertical splitting, and mangerite showed catastrophic brittle failure with extensive fracture networks. These findings provide quantitative frameworks that relate petrographic features to engineering behavior, offering valuable insights for rock mass assessment and engineering design in similar crystalline rock terrains. Full article

Formation MicroScanner Image (FMI) technology is a key method for identifying fractured reservoirs and optimizing oil and gas exploration, but its inherent insufficient resolution severely constrains the fine characterization of geological features. This study innovatively applies a Super-Resolution Generative Adversarial Network (SRGAN) to the super-resolution reconstruction of FMI logging image to address this bottleneck problem. By collecting FMI logging image of glutenite from a well in Xinjiang, a training set containing 24,275 images was constructed, and preprocessing strategies such as grayscale conversion and binarization were employed to optimize input features. Leveraging SRGAN’s generator-discriminator adversarial mechanism and perceptual loss function, high-quality mapping from low-resolution FMI logging image to high-resolution images was achieved. This study yields significant results: in RGB image reconstruction, SRGAN achieved a Peak Signal-to-Noise Ratio (PSNR) of 41.39 dB, surpassing the optimal traditional method (bicubic interpolation) by 61.6%; its Structural Similarity Index (SSIM) reached 0.992, representing a 34.1% improvement; in grayscale image processing, SRGAN effectively eliminated edge blurring, with the PSNR (40.15 dB) and SSIM (0.990) exceeding the suboptimal method (bilinear interpolation) by 36.6% and 9.9%, respectively. These results fully confirm that SRGAN can significantly restore edge contours and structural details in FMI logging image, with performance far exceeding traditional interpolation methods. This study not only systematically verifies, for the first time, SRGAN’s exceptional capability in enhancing FMI resolution, but also provides a high-precision data foundation for reservoir parameter inversion and geological modeling, holding significant application value for advancing the intelligent exploration of complex hydrocarbon reservoirs. Full article

Underground hydrogen storage (UHS) in geological formations offers a promising solution for large-scale energy buffering, but its long-term safety and mechanical stability remain concerns, particularly in fractured rock environments. This study develops a fully coupled thermo-mechanical model to investigate the cyclic response of a dual-cavern hydrogen storage system with polymer-based sealing layers. The model incorporates non-isothermal gas behavior, rock heterogeneity via a Weibull distribution, and fracture networks represented through stochastic geometry. Two operational scenarios, single-cavern and dual-cavern cycling, are simulated to evaluate stress evolution, displacement, and inter-cavity interaction under repeated pressurization. Results reveal that simultaneous operation of adjacent caverns amplifies tensile and compressive stress concentrations, especially in inter-cavity rock bridges (i.e., the intact rock zones separating adjacent caverns) and fracture-dense zones. Polymer sealing layers remain under compressive stress but exhibit increased residual deformation under cyclic loading. Contour analyses further show that fracture orientation and spatial distribution significantly influence stress redistribution and deformation localization. The findings highlight the importance of considering thermo-mechanical coupling and rock fracture mechanics in the design and operation of multicavity UHS systems. This modeling framework provides a robust tool for evaluating storage performance and informing safe deployment in complex geological environments. Full article

Carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) have attracted significant interest as hybrid reinforcements in epoxy (Ep) composites for enhancing mechanical performance in structural applications, such as aerospace and automotive. These 1D and 2D nanofillers possess exceptionally high aspect ratios and intrinsic mechanical properties, substantially improving composite stiffness and tensile strength. In this study, epoxy nanocomposites were fabricated with 0.1 wt.% and 0.3 wt.% of CNTs and GNPs individually, and with 1:1 CNT:GNP hybrid fillers at equivalent total loadings. Scanning electron microscopy of fracture surfaces confirmed that the CNTGNP hybrids dispersed uniformly, forming an interconnected nanostructured network. Notably, the 0.3 wt.% CNTGNP hybrid system exhibited minimal agglomeration and voids, preventing crack initiation and propagation. Mechanical testing revealed that the 0.3 wt.% CNTGNP/Ep composite achieved the highest tensile strength of approximately 84.5 MPa while maintaining a well-balanced stiffness profile (elastic modulus ≈ 4.62 GPa). The hybrid composite outperformed both due to its synergistic reinforcement mechanisms and superior dispersion despite containing only half the concentration of each nanofiller relative to the individual 0.3 wt.% CNT or GNP systems. In addition to mechanical performance, electrical conductivity analysis revealed that the 0.3 wt.% CNTGNP hybrid composite exhibited the highest conductivity of 0.025 S/m, surpassing the 0.3 wt.% CNT-only system (0.022 S/m), owing to forming a well-connected three-dimensional conductive network. The 0.1 wt.% CNT-only composite also showed enhanced conductivity (0.0004 S/m) due to better dispersion at lower filler loadings. These results highlight the dominant role of CNTs in charge transport and the effectiveness of hybrid networks in minimizing agglomeration. These findings demonstrate that CNTGNP hybrid fillers can deliver optimally balanced mechanical enhancement in epoxy matrices, offering a promising route for designing lightweight, high-performance structural composites. Further optimization of nanofiller dispersion and interfacial chemistry may yield even greater improvements. Full article