Yanhai Liu

The mechanisms of frequency dispersion and attenuation of seismic waves in fluid-saturated coal are not yet fully understood, and traditional integer-order viscoelastic models using a single relaxation time struggle to accurately characterize the dynamic response across different frequencies. A new three-component physical fractal viscoelastic model, based on the self-similar fractal pore-fracture structure of saturated coal, decouples matrix and fluid dissipation through two independent Caputo fractional orders (α corresponding to the skeleton and β corresponding to the fluid), distinguishing it from the single-relaxation-time form of standard integer-order elements. Effective stress simultaneously modifies both the fractal orders (α, β) and the relaxation times (τ₂, τ₃), thereby increasing P-wave velocity and reducing the attenuation amplitude and its frequency dependence. In contrast, temperature and gas adsorption change only the relaxation time scale, leading to synchronous changes in wave velocity and attenuation in unison. The model provides petrophysical support for field-scale seismic dispersion inversion, fracture prediction, fluid identification, and sweet spot evaluation in coalbed methane reservoirs.

The foundation of successful mineral exploration is precise bauxite horizon demarcation and grade estimation. Although core analysis is the industry standard method, it is costly, labor-intensive, and has a relatively low processing capacity. To overcome these limitations, this study constructed an Extreme Gradient Boosting (XGBoost) classifier based on the logging parameters of natural gamma logging (GR), natural gamma spectroscopy logging (GGL), three-lateral logging (LL3), and compensated density logging (CDN) in order to achieve the automation of ore layer identification and grade prediction. The karst-type bauxite in Lvliang, Shanxi, was used to validate the research. The model was trained using the data from four wells in Shenjiazhuang. The trained model was directly applied to a blind well in Xingxian without parameter adjustment. Strong cross-site generalization was demonstrated by horizon recognition, which achieved 98.18% accuracy, 96.62% precision, 91.49% recall, and an F1 score of 93.99%. Based on the Al/Si ratio (A/S) and the content of Al₂O₃, the grade prediction classifies the samples into three grades: high-, medium-, and low-grade. The Mean Absolute Errors (MAEs) for the prediction of high- and medium-grade subsets of Al₂O₃ were 0.906 and 1.643, respectively, and those for A/S were 1.224 and 1.146, respectively. And the coefficient of determination (R²) for each grade level was greater than 0.8. These results support XGBoost’s field applicability and resilience for intelligent bauxite exploration.

The structure and evolution of coal are intricately linked to its properties at both nanometer and micrometer scales. The refinement of pores and fractures is crucial for assessing outburst risks, evaluating coalbed methane (CBM) reservoirs, and improving the CBM recovery efficiency. This study involved collecting four different coal-body structure samples from the Zhaozhuang colliery in the southern Qinshui Basin. We conducted laboratory analyses to determine the physical properties of the coal and employed X-ray computed tomography (CT) to quantitatively assess the pore size distribution (PSD), volume contribution, morphology, and connectivity across various coal structures. The evolution and comparative characteristics of these coal structures under different tectonic stresses is discussed. Results reveal that the hysteresis loop type of 3# anthracite primarily aligns with the H3 type as per IUPAC classification, featuring predominantly plate-like and wedge-shaped pores at the nanometer scale. A shape factor was introduced to quantitatively categorize the pore types, highlighting its sensitivity to the coal structure. Spherical and tubular pores are mainly present in the aperture range of 0-25 μm, while larger apertures appeared as prolate spheroids and flat fractures. Primary coal contains more spherical or tubular pores, whereas tectonic coal shows a prevalence of slit pores and flat fractures, suggesting that pores and fractures undergo progressive deformation, either breaking or elongating, under tectonic stress. The elastic property is affected by the multifactor of the increased pore volume and changes in pore morphology. Permeability is influenced by PSD and connectivity, demonstrating a quadratic positive correlation with porosity, aperture, and specific surface area. Granular coal exhibited the most favorable permeability characteristics for CBM extraction. The distribution trend of morphology and comprehensive data highlight an evolutionary pattern of different coal structures. The evolution of the coal structure is mainly shaped by brittle and ductile deformation mechanisms. Cataclastic coal is characterized by an increase in new fractures, and granular coal undergoes rapid new fracture formation and enhanced connectivity under strong stress conditions. Mylonitic coal develops under ductile deformation mechanisms. These insights into the properties of various coal structures can significantly enhance our understanding of the CBM recovery efficiency from both microscopic and mesoscopic perspectives.

The elastic characteristics of different coal structures (primary, cataclastic, granulated, and mylonitized) are important for coalbed problems such as methane exploration and mining. However, due to various factors influencing coal elastic parameters, there is still little understanding about them. In this study, we selected four samples with different coal structures and measured their elastic parameters at a seismic frequency band (4–300 Hz) under varying confining pressure (room pressure up to 20 MPa). The results show that coal elastic wave velocities are frequency-dependent under room-dry conditions. These results contrast with the previous understanding that rock dispersion phenomena are caused by fluids. The dispersion of elastic wave velocities decreases with increasing confining pressure and increases as the coal structure becomes more fragmented. Under room pressure, the P-wave velocity in primary coal and mylonitized coal exhibited 5.91% and 12.04% increases from 4 to 300 Hz, respectively. However, at 20 MPa, these increases were reduced to 2.78% and 4.75%. The fractal viscoelastic model, which is grounded in the staggered interlayer structure of coal matrix, effectively describes the dispersion characteristics of coal. This reveals that the fractal structure of the coal may be used to describe the coal viscoelastic characteristics. We found that frictional dissipation and the viscoelastic components, such as clay and organic matter, caused the dispersion. The difference in compliant pore content is the primary reason for the different dispersion in various coal structures in this study. This research may provide a new basis for studying the elastic wave dispersion of different coal structures.

Well-logging-constrained impedance inversion is an effective process for predicting the thickness and bifurcation of coal seams. Wavelet changes in a complex region achieve the best match between the inverse and source wavelets, affecting the accuracy of the inversion solution and the ability to obtain accurate inverted acoustic impedance (AI) data. We have conducted the joint inversion of wavelet and AI data using iterative methods, which combined the conjugate-gradient (CG) method and particle-swarm-optimization (PSO) algorithm. The Marmousi AI model was used to prove the reliability of the method. The CG-PSO algorithm achieved excellent results compared with the statistical wavelet pickup method. The wavelet obtained by the CG-PSO algorithm is preferred for inversion operations. We applied a new method to invert field data and predict the thickness and bifurcation of coal seams in the karst region. The results find that the wavelet spectrum obtained by the CG-PSO matches the spectrum map of the coal seam in the Yuwang colliery. We determined the distribution of the thickness and bifurcation of the 101 panel, Yuwang Colliery, Yunnan Province. The average error of the predicted coal thickness is 0.17 m (14.4%), which verifies the feasibility and effectiveness of the method. The method provides insights into the AI inversion of constrained waves in complex regions.

The refinement of nanoscale physical parameters of source rocks has benefitted from continuous innovations in technology and methods. Traditional methods for detecting reservoir physical properties are limited by the properties of the instruments. Atomic force microscopy (AFM) provides new methods and approaches for furthering our understanding and exploring the nanoscale world. Its wide range of applications and gradually developed multiscenario application modes make it an ideal tool for the characterization of nanoscale physical properties of source rocks (coal, shale, mudstone, sandstone, etc.). We highlight the advantages of AFM in regard to performing nondestructive 3D imaging and mechanical property measurements with nanoscale resolution in any desired environment (air, vacuum, liquid). The limitations of AFM applied to source rocks are also summarized. The process has great potential in micro/nanopore characterization, surface morphology characterization, in situ micromechanical property acquisition, wettability research, and so on. The gradual development and improvement of this new model will expand new methods, bring enlightenment to basic research on unconventional oil and gas resources, and provide a scientific basis for the exploration and development of unconventional oil and gas resources.