Natural Resources Research最新文献

Coal and gas desorption, as a major form of gas energy release, is a key factor in triggering coal and gas outbursts. Therefore, studying the physical characteristics during coal and gas desorption is essential for understanding the development process of coal and gas outbursts. Based on gas dynamics during coal particle gas desorption, this study established a connection between gas desorption and infrasound signals, elaborating on the generation mechanism of infrasound signals during coal particle gas desorption and validating the feasibility of the theory through experimental data, thereby demonstrating the spontaneous occurrence of subsonic tremors during coal particle gas desorption. Combining observational data, it was found that the peak value of infrasound signals generated during desorption experiments is correlated positively with the initial pressure; while, the dominant frequency of infrasound signals is influenced by the proportion of intergranular pores and fractures within the experimental vessel. To further validate the theory of subsonic generation, a mathematical model describing pressure oscillations within intergranular pores, thereby explaining the mechanism of subsonic tremors, was established. The model confirms that the generation and characteristics of infrasound signals are controlled by the parameters of intergranular pores in coal samples. The model effectively simulates changes in the characteristics of infrasound signal tremors during desorption under different conditions, confirming that the physical properties of intergranular pores are crucial factors influencing the generation of infrasound signals and their characteristics during coal and gas desorption.

The surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. This study employed a machine learning-based framework, integrating multidimensional datasets to create prospectivity maps for lateritic Ni–Co deposits within a specific Lachlan Orogen segment. The framework generated a variety of data-driven models incorporating geological (rock units, metamorphic facies), structural, and geophysical (magnetics, gravity, radiometrics, and remote sensing spectroscopy) data layers. These models ranged from comprehensive models that use all available data layers to fine-tuned models restricted to high-ranking features. Additionally, two hybrid (knowledge-data-driven) models distinguished between hypogene and supergene components of the lateritic Ni–Co mineral systems. The study implemented data augmentation methods and tackled imbalances in training samples using the SMOTE–GAN method, addressing common machine learning challenges with sparse training data. The study overcame difficulties in defining negative training samples by translating geological and geophysical data into training proxy layers and employing a positive and unlabeled bagging technique. The prospectivity maps revealed a robust spatial correlation between high probabilities and known mineral occurrences, projecting extensions from these sites and identifying potential greenfield areas for future exploration in the Lachlan Orogen. The high-accuracy models developed in this study utilizing the Random Forest classifier enhanced the understanding of mineralization processes and exploration potential in this promising region.

Deep coalbed methane (CBM) extraction shows that coal body structure (CBS) influences the original pore and permeability conditions of a coal reservoir and that it has a significant effect on CBM production. CBS prediction by using logging curves has become an important aspect in CBM reservoir engineering. In this study, we identified vertical development of CBS in coal seam 8 of the Benxi Formation in 23 wells in the Ordos Basin based on core observation. Moreover, logging curves of all coal seams were collected to study the correlation between different logging parameters and CBS, and the logging curve parameters were then optimized. Principal component analysis was used to make a comprehensive evaluation of CBS. Subsequently, factors such as structural curvature, coal seam depth, thickness and sedimentary environment were explored to investigate the main controlling factors of CBS in the Benxi Formation of Mizhi area. The results were as follows. (1) The CBS of the target coal reservoir includes primary structured coal, fragmented structured coal and mylonite coal. As the damage degree of coal structure became stronger, the volume change of micro-pores was significantly stronger than that of the meso-pore volume, and the methane adsorption capacity gradually enhanced, which is more conducive to methane adsorption. (2) Natural potential, natural gamma, acoustic time difference, compensated neutron and density logging curves of different coal structures were quite different. The identification of CBS by using dual logging parameters had poor performance. The accuracy of coal structure recognition based on principal component analysis was better. (3) The identification results of logging curves indicate that mylonite coal was widely developed in the northeast of the Mizhi area, which is related to the larger structural curvature, resulting in an increased degree of coal seam deformation. (4) The development of mylonite coal in the central and eastern regions is due to the widespread development of intertidal gray flat facies in the area. The top and bottom floors are mainly composed of limestone and mudstone, and there are two layers of interbedded gangue in the thick coal seams. Therefore, the strong heterogeneity inside the coal seams and the similar mechanical properties of the top and bottom rocks lead to the development of mylonite coal in this area.

The stress wave propagation and energy evolution of coal and rock masses under complex stress states hold significant implications for the efficient extraction of deep resources and the prevention and management of dynamic disasters. To investigate the propagation characteristics of stress waves and the energy dissipation in raw coal under true triaxial conditions, this study employed the self-constructed true triaxial split Hopkinson pressure bar test system in conjunction with a scanning electron microscope. Dynamic and static combined impact tests were conducted on raw coal samples. The findings indicate that σ₂ and σ₃ under true triaxial prestress strengthen the sample's resistance, facilitating stress wave propagation but hampering energy conversion. Both σ₂ and σ₃ enhance transmission stress and strain, increasing from 11.0 MPa and 0.53 × 10⁻⁴ in sample tr#1 to 16.3 MPa and 0.78 × 10⁻⁴ in sample tr#5. Reflected energy constitutes the largest proportion of incident energy, followed by dissipation energy, with transmission energy being the smallest. Moreover, two inflection points in the change rate of energy ratio were observed in sample tr#2 (initial increase stage of intermediate principal stress) and sample tr#4 (initial increase stage of minimum principal stress). The spectrum of the stress wave exhibited an initial increase followed by a decrease, and the peak value of the reflected wave spectrum was an order of magnitude greater than that of the transmission wave. The frequency at which the transmission wave spectrum reached the peak point and the stationary phase was lower. The macroscopic failure degree of the sample exhibited a gradual weakening trend under the influence of σ₂ and σ₃. The micro-crack fracture pattern shifted from river-like cracks to steplike cracks, eventually forming herringbone macroscopic fractures, indicating that the coal body failure under stress waves was attributed to brittle fracture.

The utilization of horizontal wells to generate cavities and induce stress release is a potent technique for increasing deep coalbed methane (CBM) production. The evolution of pore-fracture structure (PFS) during stress release is crucial for the efficient development of deep CBM. Therefore, in this study, the unloading–seeping test system, nuclear magnetic resonance and X-ray computed tomography scanning technology were combined, and a conceptual model depicting the tensile rupture conditions and permeability evolution mechanism induced by the coupling effect of unloading–seeping was formulated. The results show that the evolution of PFS in deep coal reservoirs primarily depends on the fracture mechanism of compression–tension stress conversion, which manifests as rapid fractures propagation and contraction of micropores and mesopores. As for shallow coal reservoirs, the evolution of PFS is mainly decided by the non-uniform rebound of coal matrix, with its impact on the PFS limited to expansion and rebound of the pore system. Therefore, the increase in deep coal permeability under the stress release cannot be solely attributed to “stress release–coal expansion–permeability increase.” Rather, the coupling effect of unloading–seeping induces the transformation of tensile–compressive stress, resulting in the formation of macro- and microfractures which is the key factor controlling its evolution. However, the formation of fractures can also result in instantaneous collapse and closure of mesopores, making it difficult for CBM adsorbed in micropores to be produced through mesopores. Therefore, to prevent the sudden closure of a mesoporous system, the rapid generation of large caves on the coal seam roof should be avoided.

High-mature organic-rich shale (HMORS) has substantial resource potential, and its reservoir heterogeneity is essential for shale gas resource evaluation and exploration. In this research, to characterize quantitatively the complex pore structure of HMORS in detail, we conducted monofractal and multifractal analyses using N₂ adsorption–desorption data from the Lower Permian (LP) HMORS in the Lower Yangtze South Yellow Sea, which is a prospective target for shale gas exploration. We also aimed to discuss the correlation, controlling factors, and application effects, to provide a new scientific analytical tool for characterizing the pore structure heterogeneity (PSH) of HMORS. The upper, middle, and lower sublayers of the LP are dominated by siliceous shale, clay shale (ClS), and clay shale and clay-mixed shale (ClS–ClMS), respectively. The monofractal dimensions D1 and D2 calculated by the Frenkel–Halsey–Hill model were not notably correlated, indicating that they are independent. The D1 of H3-type HMORS was significantly higher than its D2, while D1 and D2 of the H2 type were similar, indicating that slit-shaped pores have higher surface roughness than the internal structural complexity, whereas ink-bottle pores do not differ substantially. The monofractal study revealed that the overall PSH of HMORS is controlled primarily by calcareous minerals, and that of the ClS is also influenced by total organic carbon. The multifractal analysis revealed that the low-probability measure areas controlled the full-size pore size distribution heterogeneity of HMORS. The monofractal model can characterize ClS–ClMS with ink-bottle pores, and the multifractal model can characterize ClS with slit-shaped pores. In addition, D1 and the multifractal parameters were not significantly correlated [a_-10- a₁₀, Hurst index (H), a₀- a₁₀ and a_-10- a₀], whereas D2 correlated negatively with a₀-a₁₀, which had opposite a_-10-a₀ and H, indicating that the pore connectivity of the internal PSH of HMORS can be improved. Compared to monofractal analysis, the multifractal model has enhanced applicability in characterizing the PSH of HMORS quantitatively, which is of great significance for the study of widely developed HMORS with huge shale gas exploration potential in South China.

As the intensity and depth of coal mining grow year by year, coal seam gas pressure increases and stope structures become more complex, which can easily cause coal and gas outburst. During the process of coal and gas outburst, a large amount of coal is broken and ejected, seriously threatening the safety of workers and coal mine production. Therefore, a multifunctional coal and gas outburst physical simulation test system was used to carry out three outburst tests under different gas pressures to study the particle size distributions and fragmentation characteristics of the ejected coal. The results showed that the relative intensity of outburst increased with gas pressure, but the increase rate decreased. Gas pressure also played a role in promoting the coal crushing. For the crushing product, the R–R (Rosin–Rammler) distribution model with high COD (coefficient of determination) was used to calculate the comminution energy at 0.35 MPa, while the fractal distribution model with high COD was used at 0.85 MPa and 2.00 MPa. When gas pressure increased, the basic shape of the R–R model curve remained unchanged, the probability density curve of fractal model changed from concave to nearly straight and then to convex and the basic shape of the cumulative distribution curve of fractal model remained constant. The values of α (uniformity coefficient) and x_e (characteristic particle size) impacted on the R–R model and the values of D_f (fractal dimension) and x_max (maximum particle size) impacted on the fractal model. Within a certain error range, the comminution energy could be approximated. The comminution energy increased with gas pressure, and the potential energy of crushing product decreased with the value of the n related to the crushing mechanism. There was a strong linear relationship between relative intensity of outburst and comminution coefficient. The combination of experiments and machine learning provided a new direction for outburst prediction and prevention at coal mine sites.

Ensemble learning (EL) is a machine learning paradigm where multiple learning algorithms (base learners) are trained to solve the same problem. This study provides a comprehensive evaluation of widely used EL algorithms, including bagging, boosting, and stacking, highlighting their significant advantages in terms of accuracy and generalization of mineral prospectivity mapping (MPM). This study tested mapping of prospectivity for gold deposits in the Qingchengzi Pb–Zn–Ag–Au polymetallic district using single machine learning algorithms and EL algorithms. According to the critical and favorable geological factors for magmatic-related medium-temperature hydrothermal lode system for gold deposits, five targeting criteria were extracted from multi-source geoscience datasets (i.e., geological map, gravity and magnetic datasets, stream sediment geochemical datasets) for mineral prospectivity mapping. The receiver operating characteristic curve, the area under the curve, and learning curves were used to evaluate the performance of the tested single and ensemble machine learning algorithms. The results demonstrate that the stacking model, which combines multiple base models for hierarchical feature extraction, achieves the best predictive performance. The concentration–area fractal model was used to outline the prospective areas predicted by the EL algorithms, clarifying areas with very high prospectivity for gold mineralization in the study area.

To investigate fully the poroelastic effect on apparent permeability in coal micro/nanopores, a multi-mechanism apparent permeability model coupling the gas slippage effect and the poroelastic effect is hereby constructed on the strength of the lattice Boltzmann method. The contributions of the permeability of gas slippage, surface diffusion, and viscous flow were investigated. The results showed that the gas transport was controlled by surface diffusion in micro/nanopores with initial sizes of less than 10 nm. Under a low pore pressure, the contribution share of gas slippage permeability to the apparent gas permeability decreased exponentially as the pressure rose. When the pore pressure ascended, the dynamic apparent permeability ratio (i.e., the ratio of the apparent permeability affected by the poroelastic effect to the initial apparent permeability) was subjected to the slippage effect initially and dominated by the poroelastic effect later. Additionally, the slippage effect’s contribution to the apparent permeability ratio plunged under a lower pore pressure, but such decrease slackened as the pore pressure grew to a higher value. During coalbed methane (CBM) recovery in low-permeability coal seams, the slippage effect’s contribution to the CBM recovery production surges first, then falls slowly, and finally restores to a slow increase, and its contribution is enhanced in micro/nanopores with smaller average pore sizes.

With the continuous development of unconventional natural gas resources, the formation mechanisms of different types of gas reservoirs have become a hot topic of current research. The migration mechanisms of gas in various types of conductive media play a crucial role in studying the formation and distribution of different types of gas reservoirs. In studying natural gas migration, the pressure difference between the source and reservoir and buoyant force are generally considered the main driving forces for gas migration, while the resistance mainly comes from the capillary pressure of the reservoir. In studying capillary pressure, a circular shape is typically used as the basic model for pores or throats. The magnitude of the capillary pressure is inversely proportional to the radius of the pore or throat. However, this study conducted experiments on gas migration in circular pore models, fracture models, sandstone rock models, and pore-fracture dual models. The experimental results showed that the aspect ratio of the migration medium has an important impact on gas migration. In spaces with high aspect ratio, the gas can undergo deformation during migration, significantly reducing the capillary resistance it encounters, and under certain conditions, capillary pressure can also become a driving force for gas migration. In circular spaces, the buoyant rise of gas must satisfy the condition that connected free water can form above and below the gas column, and water can freely flow downward during the gas column's ascent. Otherwise, even if the buoyant force experienced by a continuous gas column of a certain height exceeds the capillary force of the pores, it is difficult for gas to migrate. In pores of reservoir rocks, gas often migrates in the form of bubbles, making it difficult to form a continuous gas phase, and so gas migration under buoyant force is relatively difficult. However, gas migration is easier in fractures and faults with high aspect ratio. Faults are important pathways for gas migration from deep to shallow layers, and they are also crucial for studying the correlation between shallow gas reservoirs and deep enriched gas reservoirs. This paper proposes that the aspect ratio of the migration space positively affects gas migration from the perspective of capillary pressure, improving the existing models of natural gas migration and accumulation. This is significant for understanding the formation mechanisms of different types of gas reservoirs. However, this study primarily focused on quantitative research. Further research is needed to explore the numerical relationship between the aspect ratio of pore spaces and capillary pressure, as well as the specific impacts of factors such as the density and viscosity of two-phase fluids on the experimental results and the evaluation methods of the aspect ratio of reservoir pores.