Natural Resources Research最新文献

During the hydrocarbon charging period, reservoir pore size controls the formation mechanism and distribution law of a reservoir. In this work, we aimed to develop a porosity quantitative restoration model for tight sandstone reservoirs and reconstruct the historical process of hydrocarbon accumulation. The research methods employed were core description, X-ray diffraction, scanning electron microscopy, fluid inclusion, basin modeling, and stable carbon and oxygen isotope analysis. The findings revealed that the reservoir spaces in sandstones of the Yingcheng Formation comprise dissolution pores, microfractures and micropores, with the majority of core samples exhibiting average porosities and permeabilities of 3.6% and 0.7 mD (1 mD (millidarcy) = 9.869233 × 10⁻¹⁶ m²), respectively. The reservoir has experienced four main diagenetic effects, namely, early compaction, early cementation, middle dissolution and late cementation, and is currently in the mesodiagenesis B to telodiagenesis stage. Basin modeling revealed that the source rocks of the Shahezi Formation reached the hydrocarbon generation threshold at 107 Ma and reached the overmature stage at 89 Ma. The porosity evolution analysis revealed that the primary sedimentary porosity (({Phi }_{0})) is 36.6%. At the end of eodiagenesis A (({Phi }_{text{ea}})), the porosity stood at 12.2%; at the end of eodiagenesis B (({Phi }_{text{eb}})), it declined to 6.9%; following mesodiagenesis A (({Phi }_{text{ma}})), it reached 9.1 %; and after mesodiagenesis B – telodiagenesis (({Phi }_{text{mt}})), it was recorded at 4.8%. The history of natural gas charging indicated that the main charging period for natural gas was approximately 98.5–94.5 Ma. Therefore, the natural gas reservoirs of the Yingcheng Formation are classified as “hydrocarbon accumulation after sandstone densification”. The findings elucidate the accumulation process of tight sandstone gas and offer insights for applying these methods in other regions.

Elevated carbon monoxide (CO) concentrations within upper mine corners frequently surpass permissible safety thresholds, presenting significant health hazards to personnel and operational risks due to chronic exposure. To address this, molecular sieve and activated carbon adsorbents were synthesized via cuprous chloride (CuCl) impregnation. Characterization revealed that CuCl-loaded molecular sieve adsorbents exhibited a reduction in specific surface area, diminished pore volume, and an increase in average pore diameter. CuCl dispersion occurred predominantly as an effective monolayer on the carrier surface, indicative of optimal loading efficiency. Static adsorption experiments demonstrated superior CO elimination efficiency for the CuCl-modified molecular sieve, achieving a maximum capacity of 61.17%. Dynamic adsorption performance was optimized under conditions of central axial placement, a flow velocity of 1.0 m·s^–1, and an adsorbent mass of 600 g, yielding a peak elimination rate of 82 ppm·min^–1. Orthogonal testing identified the relative significance of operational parameters influencing dynamic performance, ranked as: adsorbent mass > adsorbent position > flow velocity. These findings elucidate fundamental structure–activity relationships and provide critical insights for advancing CO mitigation technologies in coal mine upper corners.

The increasing demand for mineral resources has spurred the exploration of deep-sea hydrothermal sulfide deposits rich in polymetallic elements. The complex terrains of hydrothermal fields pose challenges to geological mapping. This paper introduces a novel framework that combines semantic segmentation models with an image enhancement algorithm for intelligent mapping of mineralized zones in seabed. When tested in hydrothermal fields, the method achieved exceptional accuracy and efficiency. The performance of four segmentation models—Fast-SCNN, DeepLab V3 + , K-Net, and SegFormer—was evaluated utilizing high-resolution images. K-Net outperformed the other methods, with mean intersection-over-union of 76.86% and a global accuracy of 98.8%, with superior stability in underwater environments. Besides, image enhancement algorithms were employed to minimize blur, increase contrast, and correct color distortions caused by water interference, and the use of these algorithms improved recognition performance and robustness. In particular, when the unsupervised color correction method was used, the recognition accuracy increased by 3.63% and noise-related performance fluctuations were reduced by more than 50%. This method efficiently processes existing data and supports real-time recognition. Analyzing a 160-km video transect usually takes 181 hours; however, the K-Net model processed this video within 55.69 hours, a 69% reduction, while the Fast-SCNN model processed the video in only 1.66 hours. Validation tests in the study area confirmed the robustness of the proposed framework, which delineated multiple mineralized zones for targeted exploration. This method enables precise and quantitative mapping of seabed lithology distributions, bridging the gap between high-resolution imaging and large-scale mapping.

As the depth of coal mining increases, concealed fires from residual-coal spontaneous combustion in goaf pose a significant threat to underground mining safety. Preferred index gases are used to predict temperature of coal spontaneous combustion (CSC), providing ideas for an early warning system for concealed fires. Here, a new mathematical method of slope grey relation analysis (SGRA) is established and proved to be reasonable, the index gases obtained from experiments are calculated and screened according to the relation degree, and the coal temperature is predicted according to the screened index gases concentration and prediction model. The conclusions are as follows: The coal oxidation process is divided into a slow oxidation stage and a rapid oxidation stage according to the speed of oxygen consumption and gases generation, and the rapid oxidation stage approximates an exponential growth, and the trend of gases ratio changes shows an exponential growth in localized stages. Compared with index gases screened by other types of grey relation analysis, the index gases screened by SGRA accurately reflect the coal temperature, and the magnitude of the relation degree reflects the prediction accuracy. Although the SGRA has computational errors, when the relation degree of the screened index gases is greater than 0.93 in the slow oxidation stage and greater than 0.95 in the rapid oxidation stage, the prediction results can satisfy engineering applications, and the method is considered reliable. Based on SGRA and CSC prediction model, combined with artificial neural network learning, an early warning system for CSC is proposed, which is expected to accurately forecast the temperature of CSC and guarantee the safety of mine production.

The global imperative for a low-carbon energy transition is prompting significant shifts in the coal industry, driving the need to identify and analyze emerging research hot spots in coal-related research. Traditional methods that rely on domain knowledge to identify hot spots may have limitations, such as time costs and incomplete coverage. Moreover, a comprehensive analysis of coal-related research has yet to be conducted. Therefore, in this paper, a novel framework consisting of the semantic part and the word frequency part is proposed to analyze hot spots of coal-related research. Initially, a dataset consisting of 40,120 coal-related paper information from the Scopus database was constructed. Then, the novel framework was employed to analyze coal-related research. In the semantic part, bidirectional encoder representations from transformers and K-means algorithms were combined to conduct the hot spot analysis, and six hot spots are obtained. In the word frequency part, the bag-of-words and the latent Dirichlet allocation algorithms were combined to conduct hot spot analysis, and six hot spots were obtained. Finally, through the framework analysis, this study found that the 12 coal-related hot spots mainly revealed four main research directions: efficient coal utilization and resource recovery, carbon dioxide capture and emission reduction, environmental impact assessment and pollution control, and coal mine safety and geological modeling.

The primary objective of this study was to evaluate the effectiveness of three machine learning techniques in the confidence categorization of mineral resources within a copper deposit in Peru: extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). To achieve this, geostatistical and geometric datasets were employed to categorize mineral resources into measured, indicated, and inferred categories. The dataset included ordinary kriging estimates, kriging variance, average distances, the number of composites, the kriging Lagrangian, and geological confidence. This dataset was used to train the models, followed by the application of smoothing techniques to the initial classification results to ensure a spatially coherent representation of the deposit. The results indicate that the RF model achieved the highest overall accuracy (94%), categorizing 1403.70 million tons (Mt) as measured resources (average grade of 0.43%), 2230.58 Mt as indicated resources (average grade of 0.33%), and 2225.08 Mt as inferred resources (average grade of 0.31%). XGBoost classified a slightly higher tonnage of measured resources (1412.35 Mt) with average accuracy of 91%, while DNN excelled in inferred resources, classifying 2254.64 Mt with accuracy of 93%. Smoothing improved the transitions between categories, reducing discontinuities and providing a more coherent representation of the deposit. The study concluded that machine learning techniques are robust and accurate tools for mineral resource categorization, particularly in geologically complex deposits.

Coal deformation-induced by adsorption/desorption is dynamic and anisotropic, influenced by various factors, such as pressure, temperature, and gas type. This paper investigates the dynamic deformation of coal during the adsorption–desorption process and analyzes the anisotropic and hysteretic characteristics. Results show that maximum deformation is reduced by approximately half with every 10 °C increase above 40 °C, and nearly doubles with each 1 MPa pressure increase. The swelling of CO₂ at adsorption equilibrium is twice that of CH₄, and almost 4 × that of N₂. During desorption, shrinkage and desorption gas are approximately linear. Anisotropy coefficients increase initially, then decrease with adsorption, stabilizing around 2. During desorption, anisotropy coefficients generally decrease. The anisotropy coefficient of CO₂ is higher than that of CH₄ and N₂, and all show a tendency to increase with equilibrium pressure. Cumulative hysteresis deformation decreases with the increasing temperature, even reversing at higher temperatures. CO₂ exhibits significantly higher hysteresis than CH₄ and N₂. These findings offer valuable insights for engineering applications.

The efficient characterization of heterogeneous carbonate reservoirs remains a significant challenge due to complex depositional environments and diagenetic alterations. While traditional methods like electrofacies analysis and clustering techniques offer inherent benefits, they often yield incomplete or conflicting results if used solely. This paper suggests an integrated study using petrophysical, geological, and statistical analyses to improve reservoir characterization. The proposed approach was applied to a carbonate reservoir case study of a gas field in South Iran. Well-log data and core samples were employed for detailed petrographic and petrophysical analyses. Electrofacies analysis using multi-resolution graph-based clustering (MRGC) identified five distinct electrofacies. Clustering techniques, including K-means and Gaussian mixture models (GMMs), were applied to petrophysical data to delineate similar zones. The Silhouette coefficient was used to evaluate the quality of the clusters. Results showed strong correlation between electrofacies 5 and clusters 4 (from K-means) and 5 (from GMMs), implying the best reservoir properties. This integrated approach suggested a more accurate assessment of reservoir quality attributes (e.g., porosity and water saturation) and highlighted the importance of dolomitized ooid grainstone in controlling hydrocarbon accumulation. This study provides a comprehensive framework for efficiently characterizing heterogeneous carbonate reservoirs by combining petrophysical, geological, and statistical methods. This integrated approach, validated through its successful application in similar reservoir studies, enables a more accurate assessment of reservoir quality attributes such as porosity and water saturation. By leveraging the complementary strengths of these methods, the approach ensures a comprehensive understanding of reservoir heterogeneity and its impact on hydrocarbon accumulation. Additionally, it is beneficial for improving reservoir modeling, enhancing hydrocarbon recovery, and reducing exploration risks.

Hot dry rock undergoes cyclic temperature variation during an enhanced geothermal system (EGS) operation, resulting in variations in reservoir rock’s transport properties and subsequently influencing the heat extraction efficiency of EGS. Therefore, the subject of this study was to systematically investigate the effect of cyclic heat treatment on the transport properties of granite, commonly employed in EGS, through the analysis of P-wave velocity, density, and scanning electron microscopy images. Besides, the effect of changes in the granite transport properties on EGS operation was also comprehensively discussed. The results indicated that the cyclic heat treatment led to an increase in granite permeability and a reduction in thermal conductivity. These changes primarily occurred due to the initiation and propagation of microcracks within the granite. Notably, higher-temperature heat treatments exhibited a more pronounced impact on granite properties. Additionally, a significant shift in the granite properties was observed within 450–550 °C, serving as a threshold temperature in this study. Due to the Kaiser memory effect and the blocking effect of the pre-microcrack on the subsequent microcrack, the effect of heat treatment on the properties of granite mainly came from the first heat treatment. Finally, the relationship models between heat treatment temperature and transport properties damage factors were obtained by fitting literature data.

The deterioration of coal strength caused by geological conditions of high gas in deep mines and disturbance from mining operations is one of the elements that influence the incidence of dynamic disasters like gas outbursts and rock bursts. To study how gas pressure and cyclic loads interact to determine the mechanisms and phenomena of coal dynamics, the split Hopkinson pressure bar apparatus was used to perform cyclic impact test on coal samples to investigate the mechanical behavior of gas-bearing coal samples under cyclic dynamic load and gas pressures. The findings indicated that there are three stages in the stress–strain evolution of gas-bearing coal: linear elastic stage, plastic stage, and post-peak stress attenuation. As cycle time grows, the peak stress and attenuation stress of the coal samples decrease, while the maximum and peak strains exhibit a general increasing trend. Under the impact of dynamic load, the macroscopic damage form of the coal sample is mainly a macroscopic crack, and the microscopic examination revealed that the coal samples interior crystal was primarily a trans-granular fracture. By considering dynamic load, gas pressure, and number of cycles, the test results can be more accurately verified by the mechanical damage constitutive model. Finally, based on cyclic dynamic load and gas pressure, the proposed fatigue prediction model of gas-bearing coal can better anticipate coal samples dynamic load-bearing capability.