International Journal of Rock Mechanics and Mining Sciences最新文献

Geotechnical stability analyses of mine waste rock (WR) piles require the critical friction angle (ϕ_cr) of the coarse blasted rock. However, due to the presence of oversized rock clasts, shear strength can only be characterized on small samples prepared using grading scaling techniques, such as scalping. Thus, considering a testing device able to handle samples of characteristic size D, the material should be scaled down to a maximum particle size d_max given by the minimum sample aspect ratio α = D/d_max. However, a practical concern about how far the size scale can be reduced while keeping representative results remains a matter of debate in the geotechnical community. International standards do not agree on the minimum recommended α, and its effects on the mechanical behavior remain poorly understood. This paper aims to investigate the grading effects and sample size effects on ϕ_cr of WR materials using the scalping technique, to provide insights on the minimum recommended α. Triaxial tests were conducted on loose and dense samples of diameters D = 150 and 300 mm. Samples were scalped from field material having d_max = 75 mm, to allow a range of α from 4 to 30. Additionally, one of the world largest in-situ direct shear boxes (120 × 120 × 38 cm³) was developed to test the same WR material. The results show that scalping is an appropriate technique to assess the critical shear strength of WR. The minimum α for ϕ_cr assessment in triaxial testing is not sensitive to grading nor sample size, but it is affected by sample density. The aspect ratio was found to be α ≥ 12 and α ≥ 16 for loose and dense samples, respectively. This finding advocates that α values recommended by worldwide standards, such as ASTM D7181-20, might be too low and should be revisited after comprehensive testing.

It is of great importance to determine peak shear strength (PSS) of rock fractures, and data-driven criteria have showed advances in fitting capability in recent years. However, the generalization ability of existing data-driven criteria is limited by dataset size and fracture roughness characterization, which is negative to predictive power and robustness of models. Here we proposed a novel data-driven criterion to predict PSS of rock fractures, with high generalization ability on real experimental data. We first created large-scale low-fidelity dataset by discrete-element modeling, and small-scale high-fidelity dataset by laboratory direct shear tests. The numeric features include normal stress, mechanical properties (including PSS of intact and flat-fracture rock specimens), secondary properties (including internal friction angle, cohesion strength and basic friction angle), and the matrixed feature is topography data. We then established domain adaptation (DA) models for cross-domain knowledge transfer between the low- and high-fidelity datasets, and roughness features were automatically extracted by convolution kernels. The best DA-based model is weighting adversarial neural network, outranking other models by error indicator, and the average relative error on experimental data of new rock types is within 10.0 %. Finally, the sensitivity of input features is investigated, which further proves the promising potential of the developed data-driven PSS criterion of rock fractures in engineering practice.

Rock failure under external force is a process of energy conversion between the external environment and the rock system. This study aims to quantify rock damage and predict failure from an energy perspective. Infrared radiation (IR) and acoustic emission (AE) technologies were used to monitor the failure process of red sandstone during uniaxial loading experiments in real time. The energy evolution law during the rock failure process was analyzed. Based on the Stefan–Boltzmann law, a quantitative parameter, average cumulative radiation energy increment ($\Delta ACRE$), was proposed for IR indicators. A coupling mathematical model between elastic strain energy and $\Delta ACRE$ was derived. The correlation between cumulative AE energy and dissipated strain energy was also analyzed. Results reveal that the rock failure process can be divided into four stages according to energy evolution: compaction, elastic, elastic–plastic, and failure stages. The proposed $\Delta ACRE$ can serve as a basis for dividing these stages. A cubic polynomial relationship was found between $\Delta ACRE$ and elastic strain energy. AE cumulative energy and dissipated strain energy showed similar variation trends. Furthermore, based on $\Delta ACRE$, AE cumulative energy, and energy evolution theory, a failure prediction indicator ($IRAEER$) was proposed. This indicator can effectively identify precursor points of rock failure. A quantitative indicator for rock damage evolution under combined IR and AE action was created using $IRAEER$ as the characterization parameter of the rock damage variable, demonstrating high reliability. This research provides strong support for estimating rock states and guiding the design of rock engineering structures.

Fractures control fluid flow, solute transport, and mechanical deformation in crystalline media. They can be modeled numerically either explicitly or implicitly via an equivalent continuum. The implicit framework implies lower computational cost and complexity. However, upscaling heterogeneous fracture properties for its implicit representation as an equivalent fracture layer remains an open question. In this study, we propose an approach, the Equivalent Fracture Layer (EFL), for the implicit representation of fractures surrounded by low-permeability rock matrix to accurately simulate hydromechanical coupled processes. The approach assimilates fractures as equivalent continua with a manageable scale (≫1 μm) that facilitates spatial discretization, even for large-scale models including multiple fractures. Simulation results demonstrate that a relatively thick equivalent continuum layer (in the order of cm) can represent a fracture (with aperture in the order of μm) and accurately reproduce the hydromechanical behavior (i.e., fluid flow and deformation/stress behavior). There is an upper bound restriction due to the Young's modulus because the equivalent fracture layer should have a lower Young's modulus than that of the surrounding matrix. To validate the approach, we model a hydraulic stimulation carried out at the Bedretto Underground Laboratory for Geosciences and Geoenergies in Switzerland by comparing numerical results against measured data. The method further improves the ability and simplicity of continuum methods to represent fractures in fractured media.

Fracability evaluation for unconventional reservoir is critical to the selection of candidate zones for post-frac productivity and plays a key role in fracturing design. Historically, the prevailing models for assessing fracability have been largely relied on brittleness indices. Brittleness indices focus mainly on rock fracture characteristics and offers limited assessment of fracture surface area and the complexity of fracture network, which are more relevant to the practical production. We explored a new fracability evaluation model for unconventional reservoirs from the perspective of fracturing performance, which comprehensively characterizes the rock's ability to generate larger fracture surface areas, more shear fractures and complex fracture networks. The new fracability index considers both the physical processes of rock failure and fracture propagation, and is directly associated with the dynamic production capacities of reservoir. According to the analysis of energy conservation during hydraulic fracturing, we quantify the rock fracture surface area using the KGD and the PKN models. The ability of rock formation to generate shear fractures is mainly influenced by Poisson's ratio and mode II fracture toughness. Brittle mineral content and mineral heterogeneity are two vital criteria that significantly affect the complexity of fracture networks. Based on the logging and production data, this fracability model was applied to two types of unconventional reservoirs. Preliminary results show that this fracability model has an improved correlation with the pay zones and actual production, which is beneficial for optimizing fracturing strategies and identifying production sweet spots.