Search Results (315)

This study aims to accurately characterize the nonlinear stress–strain evolution of rocks under uniaxial compression considering crack propagation. First, the rock meso-structure was generalized into intact rock unit cells, crack propagation damage unit cells, and pore unit cells according to phenomenological theory. A mesoscopic rock stress model considering crack propagation was established based on the static equilibrium relationship of the unit cells, and the effective stress of the crack propagation damage unit cells was solved based on fracture mechanics. Then, the geometric damage theory and conservation-of-energy principle were introduced to construct the damage evolution equation for rock crack propagation. On this basis, the effective stress of the damage unit cells and the crack propagation damage equation were incorporated into the rock meso-structure static equilibrium equation, and the effect of nonlinear deformation in the soft rock compaction stage was considered to establish a rock damage constitutive model based on mesoscopic crack propagation evolution. Finally, methods for determining model parameters were proposed, and the effects of the model parameters on rock stress–strain curves were explored. The results showed that the theoretical model calculations agreed well with the experimental results, thus verifying the rationality of the damage constitutive model and the clear physical meaning of the model parameters. Full article

This study focuses on tunnel construction in fault fracture zones and systematically investigates the energy evolution and damage catastrophe mechanisms of surrounding rock during excavation, based on energy conservation principles and cusp catastrophe theory. A tunnel instability prediction and support optimization framework integrating energy damage evolution and intelligent optimization algorithms was developed. Field tests, rock mechanics experiments, and Discrete Fracture Network (DFN) numerical simulations reveal the intrinsic relationships among energy input, dissipation, damage accumulation, and instability under complex geological conditions. Particle Swarm Optimization–Back Propagation (PSO-BP) is applied to optimize tunnel support parameters. Model performance is evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). The results show that upon reaching structural mutation zones, the system damage variable (ds), displacement, and dissipated energy increase abruptly, indicating critical instability. Numerical simulation and catastrophe feature analysis demonstrate that energy-related damage accumulation is effectively suppressed, the system damage variable decreases significantly, and crown stability is greatly enhanced. These findings provide a theoretical basis and practical reference for optimizing tunnel support design and controlling instability risks in complex geological settings. Full article

Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current limitations in understanding post-fracturing production dynamics and the lack of efficient prediction methods severely constrain the evaluation of fracturing effectiveness and the adjustment of development plans. This study proposes a machine learning-based method for predicting post-fracturing productivity in multi-layer commingled production wells and validates its effectiveness using a key block from the PetroChina North China Huabei Oilfield Company. During the data preprocessing stage, the three-sigma rule, median absolute deviation, and density-based spatial clustering of applications with noise were employed to detect outliers, while missing values were imputed using the K-nearest neighbors method. Feature selection was performed using Pearson correlation coefficient and variance inflation factor, resulting in the identification of twelve key parameters as input features. The coefficient of determination served as the evaluation metric, and model hyperparameters were optimized using grid search combined with cross-validation. To address the multi-layer commingled production challenge, seven distinct datasets incorporating production parameters were constructed based on four geological parameter partitioning methods: thickness ratio, porosity–thickness product ratio, permeability–thickness product ratio, and porosity–permeability–thickness product ratio. Twelve machine learning models were then applied for training. Through comparative analysis, the most suitable productivity prediction model for the block was selected, and the block’s productivity patterns were revealed. The results show that after training with block-partitioned data, the accuracy of all models has improved; further stratigraphic subdivision based on block partitioning has led the models to reach peak performance. However, data volume is a critical limiting factor—for blocks with insufficient data, stratigraphic subdivision instead results in a decline in prediction performance. Full article

The development of low-permeability reservoirs offshore entails substantial investment and demands high production capacity for oil and gas. Consequently, the analysis and evaluation of key elements for integrated geological–engineering sweet spots have become essential. This study systematically establishes a coupled analysis methodology for geological and engineering parameters of low-permeability reservoirs, based on Offshore Oilfield A. A comprehensive evaluation framework for geological–engineering sweet spots is proposed, which applies grey relational analysis and the analytic hierarchy process. Twelve geological–engineering sweet spots were analysed, with corresponding parameter weightings determined. Geological sweet spots encompassed factors such as porosity, permeability, and oil saturation, and engineering sweet spots considered Young’s modulus, Poisson’s ratio, fracture factor, and brittleness index. Low-permeability reservoirs were categorised into Classes I, II, III, and IV by establishing indicator factors. Integrating seismic inversion and reservoir numerical simulation methods, we constructed an analysis model. This methodology resolves challenges in evaluating offshore low-permeability reservoirs, enabling rapid and precise sweet spot identification. It provides critical technological support for enhancing oil and gas production efficiency. Full article

Deep coalbed methane development faces technical challenges, such as high in situ stress and low permeability. The dynamic evolution of fractures after hydraulic fracturing and the flowback mechanism are crucial for optimizing productivity. This paper focuses on the inversion of post-fracturing fracture volume parameters and dynamic analysis of the flowback in deep coalbed methane wells, with 89 vertical wells in the eastern margin of the Ordos Basin as the research objects, conducting systematic studies. Firstly, through the analysis of the double-logarithmic curve of normalized pressure and material balance time, the quantitative inversion of the volume of propped fractures and unpropped secondary fractures was realized. Using Pearson correlation coefficients to screen characteristic parameters, four machine learning models (Ridge Regression, Decision Tree, Random Forest, and AdaBoost) were constructed for fracture volume inversion prediction. The results show that the Random Forest model performed the best, with a test set R² of 0.86 and good generalization performance, so it was selected as the final prediction model. With the help of the SHAP model to analyze the influence of each characteristic parameter, it was found that the total fluid volume into the well, proppant intensity, minimum horizontal in situ stress, and elastic modulus were the main driving factors, all of which had threshold effects and exerted non-linear influences on fracture volume. The interaction of multiple parameters was explored by the Partial Dependence Plot (PDP) method, revealing the synergistic mechanism of geological and engineering parameters. For example, a high elastic modulus can enhance the promoting effect of fluid volume into the well and proppant intensity. There is a critical threshold of 2600 m³ in the interaction between the total fluid volume into the well and the minimum horizontal in situ stress. These findings provide a theoretical basis and technical support for optimizing fracturing operation parameters and efficient development of deep coalbed methane. Full article

This study takes the Wushan open-pit mine, a typical open-pit mine in cold regions, as the engineering background. Based on the measured extreme temperature values of slope rock masses over one year, a freeze–thaw cycle testing scheme is designed. By conducting experiments under varying numbers of freeze–thaw cycles and burial depths, the degradation patterns of uniaxial compressive strength and tensile strength of the rock are revealed. The rock material constant $\mathrm{m}\mathrm{i}$, representing the rock’s hardness and brittleness, is calculated based on the experimental results. Furthermore, shear tests are carried out on rock masses containing through-going structural planes and infill materials to derive the variation patterns of cohesion and internal friction angle. A comprehensive analysis is conducted on the effects of freeze–thaw cycling and burial depth on rock mechanical properties and infill material parameters, leading to the construction of a spatial variability characterization model for mechanical parameters. Finally, the rock mass fracture coefficient ${\mathrm{K}}_{\mathrm{w}}$ and infill fracture coefficient $K_f$ are proposed to modify the Hoek–Brown failure criterion under freeze–thaw conditions, thereby providing theoretical support for slope stability analysis and engineering design in cold regions. Full article

With the continuous expansion of urban rail transit networks, construction safety of connecting passages—as critical weak links in underground structural systems—has become pivotal for project success. Although artificial ground freezing technology effectively addresses adverse geological conditions (e.g., high permeability and weak self-stability), it is influenced by multi-field coupling effects (temperature, stress, and seepage fields), which may trigger chain risks such as freezing pipe fractures and frozen curtain leakage during construction. This study deconstructed the freezing method workflow (‘drilling pipe-laying → active freezing → channel excavation → structural support’) and established a hierarchical evaluation index system incorporating geological characteristics, technological parameters, and environmental impacts by considering sandy soil phase-change features and hydro-thermal coupling effects. For weight calculation, the Analytic Hierarchy Process (AHP) was innovatively applied to balance subjective-objective assignment deviations, revealing that the excavation support stage (weight: 52.94%) and thawing-grouting stage (31.48%) most significantly influenced overall risk. Subsequently, a Bayesian network-based risk assessment model was constructed, with prior probabilities updated in real-time using construction monitoring data. Results indicated an overall construction risk probability of 46.3%, with the excavation stage exhibiting the highest sensitivity index (3.97%), identifying it as the core risk control link. These findings provide a quantitative basis for dynamically identifying construction risks and optimizing mitigation measures, offering substantial practical value for enhancing safety in subway connecting passage construction within water-rich sandy strata. Full article

Horizontal well multi-stage fracturing is the primary technology for deep shale gas development, but dense multi-cluster fractures are prone to non-uniform initiation and propagation, requiring real-time monitoring and interpretation techniques to adjust fracturing parameters. Although high-frequency water hammer pressure-monitoring technology shows diagnostic potential, the correlation mechanism between pressure response characteristics and multi-cluster fracture morphology remains unclear. This study utilized outcrop rock samples from the Longmaxi Formation shale to construct a long-injection-tube pipeline system and a 1 kHz high-frequency pressure acquisition system. Through a true triaxial fracturing simulation test system, it systematically investigated the effects of flow rate (50–180 mL/min) and fracturing fluid viscosity (3–15 mPa·s) on water hammer signal characteristics and fracture morphology. The results reveal that when the flow rate rose from 50 mL/min to 180 mL/min, the initiation efficiency of transverse fractures significantly improved, artificial fractures more easily broke through bedding plane limitations, and fracture height propagation became more complete. When the fracturing fluid viscosity increased from 3–5 mPa·s to 12–15 mPa·s, fracture height propagation and initiation efficiency significantly improved, but fewer bedding plane fractures were activated. The geometric complexity of fractures positively correlated with the water hammer decay rate. This research demonstrates a link between water hammer signal features and downhole fracture morphology, giving a theoretical basis for field fracturing diagnostics. Full article

As a representative high-performance construction material, ultra-high performance concrete (UHPC) is typically prepared using quartz sand and steel fibers. To alleviate the shortage of building materials in island and reef regions, this study employs coral sand for UHPC preparation and investigates the effects of different fibers on its mechanical properties. This study demonstrates that this approach mitigates brittle failure patterns and enhances the durability of structures. To investigate the enhancement effects of PE and steel fibers on the mechanical properties of coral sand ultra-high performance concrete (CSUHPC), 12 mix designs were formulated, including a plain (no fiber) reference group and PE fiber-reinforced, steel fiber-reinforced, and hybrid fiber combinations. Compressive tests, tensile tests, and three-point bending tests on pre-notched beams were conducted. Key parameters such as 28-day compressive strength, tensile strength, and flexural strength and toughness were measured. A multi-criteria evaluation framework was established to comprehensively assess the integrated performance of each group. The experimental results demonstrated that fiber incorporation significantly enhanced the compressive strength and fracture properties of CSUHPC compared to the plain reference group. Steel fiber-only reinforcement exhibited the most pronounced improvement in compressive strength and fracture properties, while hybrid fiber combinations provided superior tensile performance. Through the established multi-criteria evaluation framework, the optimal comprehensive performance was achieved with a 3% steel fiber dosage, achieving improvements of 0.93 times in compressive strength, 2.80 times in tensile strength, 1.84 times in flexural strength, 192.08 times in fracture energy, and 1.84 times in fracture toughness relative to the control group. Full article

Laminated bamboo (LB) represents a promising sustainable construction material, inheriting bamboo’s high strength, lightweight properties, and good ductility. However, the dimensional stability of mechanical performance—specifically size effects—remains a critical design challenge requiring systematic investigation. This study investigates the compression behavior of LB with tests of four specimen groups spanning volumes from 62,500 to 4,000,000 mm³ (25 × 25 × 100 mm to 100 × 100 × 400 mm). The research objectives encompass (i) characterizing compression behavior and failure mechanisms across different specimen scales, (ii) quantifying geometric and volumetric size effects on mechanical properties, (iii) evaluating theoretical frameworks for size effect prediction, (iv) developing progressive modeling approaches incorporating material heterogeneity, and (v) establishing design parameters for practical applications. Results demonstrate modest proportional size effects (1.60% strength reduction, 8.62% modulus reduction for 4× proportional scaling) but significant geometric optimization benefits, with cubic specimens achieving 15.78% higher strength and 25.11% greater modulus than equivalent-volume prismatic specimens. All specimens exhibited interfacial delamination failure with size-dependent crack propagation patterns. Theoretical analysis incorporates Weibull statistics, Bažant’s fracture mechanics, and Carpinteri’s fractal theory, with fracture energy modeling performing optimally. Three progressive modeling approaches achieve prediction accuracies ranging from 1.17% to 0.37% errors, with density-coupled modeling providing superior performance despite minimal density variations (COV = 9.27%). The research establishes size effect factors (0.86 for strength, 0.78 for modulus) and critical dimensions (125.64–126.14 mm), addressing critical gaps in LB size-dependent behavior. These parameters enable the development of reliable design methodologies for large-scale sustainable construction. Full article

To investigate the failure behavior of Ti6Al4V alloy under complex stress states, this study designed tensile specimens with different notches to achieve high, medium, and low stress triaxiality conditions. By adjusting the width of the notch spacing of the specimens, the failure mode can be transformed from tension-dominated fracture to shear stress-dominated fracture, which enables further examination of the damage model’s effectiveness. A shear-modified Gurson–Tvergaard–Needleman (GTN) model was employed to predict the failure behavior under various stress states. For calibrating the GTN parameters, a machine learning approach was adopted. Back propagation (BP) neural networks were used to construct surrogate models for predicting the fracture strains of three typical specimens, and genetic algorithms (GAs) were integrated for optimization, to minimize the discrepancy in fracture strains between experimental results and finite element analysis (FEA). Finally, an optimal set of parameters was determined. This set of parameters can effectively predict the failure behavior of all specimens, including not only the stress–strain curves, but also the failure modes (fracture locations). Full article

Aluminium metal matrix composites (AMMCs) incorporate aluminium alloys reinforced with fibres (continuous/discontinuous), whiskers, or particulate. These materials were engineered as advanced solutions for demanding sectors including construction, aerospace, automotive, and marine. Micro- and nano-scale reinforcing particles typically enable attainment of exceptional combined properties, including reduced density with ultra-high strength, enhanced fatigue strength, superior creep resistance, high specific strength, and specific stiffness. Microstructural, mechanical, and tribological characterizations were performed, evaluating input parameters like reinforcement weight percentage, applied normal load, sliding speed, and sliding distance. Fabricated nanocomposites underwent tribometer testing to quantify abrasive and erosive wear behaviour. Multiple investigations employed the Taguchi technique with regression modelling. Analysis of variance (ANOVA) assessed the influence of varied test constraints. Applied load constituted the most significant factor affecting the physical/statistical attributes of nanocomposites. Sliding velocity critically governed the coefficient of friction (COF), becoming highly significant for minimizing COF and wear loss. In this review, the reinforcement homogeneity, fractural behaviour, and worn surface morphology of AMMCswere examined. Full article

This study presents an integrated geomechanical modeling framework for predicting multi-scale fracture networks and their activity in the Longmaxi Formation shale reservoir, northern Luzhou region, southeastern Sichuan Basin—an area shaped by complex, multi-phase tectonic deformation that poses significant challenges for resource prospecting. The workflow begins with quantitative characterization of key mechanical parameters, including uniaxial compressive strength, Young’s modulus, Poisson’s ratio, and tensile strength, obtained from core experiments and log-based inversion. These parameters form the foundation for multi-phase finite element simulations that reconstruct paleo- and present-day stress fields associated with the Indosinian (NW–SE compression), Yanshanian (NWW–SEE compression), and Himalayan (near W–E compression) deformation phases. Optimized Mohr–Coulomb and tensile failure criteria, coupled with a multi-phase stress superposition algorithm, enable quantitative prediction of fracture density, aperture, and orientation through successive tectonic cycles. The results reveal that the Longmaxi Formation’s high brittleness and lithological heterogeneity interact with evolving stress regimes to produce fracture systems that are strongly anisotropic and phase-dependent: initial NE–SW-oriented domains established during the Indosinian phase were intensified during Yanshanian reactivation, while Himalayan uplift induced regional stress attenuation with limited new fracture formation. The cumulative stress effects yield fracture networks concentrated along NE–SW fold axes, fault zones, and intersection zones. By integrating geomechanical predictions with seismic attributes and borehole observations, the study constructs a discrete fracture network that captures both large-scale tectonic fractures and small-scale features beyond seismic resolution. Fracture activity is further assessed using friction coefficient analysis, delineating zones of high activity along fold–fault intersections and stress concentration areas. This principle-driven approach demonstrates how mechanical characterization, stress field evolution, and fracture mechanics can be combined into a unified predictive tool, offering a transferable methodology for structurally complex, multi-deformation reservoirs. Beyond its relevance to shale gas development, the framework exemplifies how advanced geomechanical modeling can enhance resource prospecting efficiency and accuracy in diverse geological settings. Full article

The abnormal pore pressures in ultra-deep wells in the Junggar Basin, China are constantly causing drilling incidents for both the drilling engineers and geologists. Formation pore-pressure is an important parameter in wellbore stability analysis, and accurate prediction of pore pressure before drilling is of great significance for effectively controlling wellbore instability. In this paper, the authors utilize seismic velocity inversion and rock mechanics prediction to evaluate the three pressure parameters, i.e., pore pressure, collapse pressure, and fracture pressure. Seismic data were inversed and the velocity model was constructed. Then, the layering models of the relationships between seismic velocity and logging data of the whole formation layers were constructed using seismic attributes and the corresponding acoustic logging data. Finally, the acoustic logging data, or interval transit time of ten corresponding formations, were predicted using layering models of seismic data. In an ultra-deep well, two abnormal highly pressurized sections were confirmed. This shows great potential for realizing real-time prediction of acoustic and density log data of undrilled formations in this area. Field applications confirm that the proposed method enhances prediction accuracy and computational efficiency compared to the Eston method. Two abnormal high-pressure zones were successfully identified in the Huqian-1 well, i.e., the Taxihe Formation (1.38 g/cm³) and the Anjihaihe Formation (1.50 g/cm³). Full article

The development of unconventional oil and gas resources highly relies on hydraulic fracturing technology, and the fracturing effect directly affects the level of oil and gas recovery. Carrying out fracturing evaluation is the main way to understand the fracturing effect. However, the current fracturing evaluation methods are usually carried out after the completion of fracturing operations, making it difficult to achieve real-time monitoring and dynamic regulation of the fracturing process. In order to solve this problem, an intelligent prediction method for fracture propagation based on the attention mechanism and Long Short-Term Memory (LSTM) neural network was proposed to improve the fracturing effect. Firstly, the GOHFER software was used to simulate the fracturing process to generate 12,000 groups of fracture geometric parameters. Then, through parameter sensitivity analysis, the key factors affecting fracture geometric parameters are identified. Next, the time-series data generated during the fracturing process were collected. Missing values were filled using the K-nearest neighbor algorithm. Outliers were identified by applying the 3-sigma method. Features were combined through the binomial feature transformation method. The wavelet transform method was adopted to extract the time-series features of the data. Subsequently, an LSTM model integrated with an attention mechanism was constructed, and it was trained using the fracture geometric parameters generated by GOHFER software, forming a surrogate model for fracture propagation. Finally, the surrogate model was applied to an actual fracturing well in Block Ma 2 of the Mabei Oilfield to verify the model performance. The results show that by correlating the pumping process with the fracture propagation process, the model achieves the prediction of changes in fracture geometric parameters and Stimulated Reservoir Volume (SRV) throughout the entire fracturing process. The model’s prediction accuracy exceeds 75%, and its response time is less than 0.1 s, which is more than 1000 times faster than that of GOHFER software. The model can accurately capture the dynamic propagation of fractures during fracturing operations, providing reliable guidance and decision-making basis for on-site fracturing operations. Full article