Natural Resources Research最新文献

Weathered water-saturated coal (WWSC) reserves in oxygen-poor environments in a goaf are present in large amounts, dispersed and pose a high risk of spontaneous combustion (SC). To determine the thermodynamic behavior and disaster-causing tendency of WWSCs stored in oxygen-poor environments, WWSCs with different weathering cycles were prepared. The oxidative–thermal behaviors of WWSCs in atmospheres with different oxygen concentrations were analyzed by using thermogravimetric analysis–differential scanning calorimetry (TG–DSC), and systematic combustion thermodynamic analyses were carried out. The results showed that the weathering time and environmental oxygen concentration synergistically affected the conversion rate of WWSC, thus affecting the length of the reaction stage. The reaction and transformation ability of WWSC weathered for 27 days at the low-temperature stage was better; the heat production of WWSC with short-term weathering (O_15-3d) was higher in the oxygen-poor environment, with maximum heat release and heat flow of 15751.5 J and 15 W/g, respectively. Different coal temperature stages of the WWSCs have different reaction dynamic models; these included low temperature–first-order reaction model and high temperature–two-dimensional diffusion Valensi model. The treatment of high oxygen concentration–long weathering time and low oxygen concentration–short weathering time caused a decrease in the E, ΔH and ΔG of WWSC and an increase in the D_f and H_F of coal. The synergistic effect of weathering time and oxygen concentration led to the greater SC tendency of the water-saturated coal with high oxygen concentration–long weathering time and low oxygen concentration–short weathering time, and the risk of thermal disaster was high. Our research results provide an important theoretical basis for goaf fire prevention and resource and environmental protection in deep coal mining and goaf remining and other projects.

Free hydrocarbons are among the fundamental indicators of shale organic matter richness and potential for hydrocarbon generation. The traditional experimental analysis method based on rock pyrolysis is time-consuming and expensive. This study aimed to predict free hydrocarbons in the Qingshankou Formation shale of the Changling Depression in the Songliao Basin. Using 521 sets of logging data as input, a stacking ensemble model for predicting shale free hydrocarbons content was developed based on six base learner models including decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN), combined with meta model (linear regression). The performance analysis and ranking of models are based on three error evaluation metrics: coefficient of determination, root mean square error, and mean absolute error. The results indicated that model performance ranking from high to low was Stacking, RF, SVM, KNN, GBDT, ANN, and DT. The stacking ensemble model with the best performance was successfully applied to predict the free hydrocarbons curve on the connected well profile. Shapley additive explanations were used explain the best performing stacking ensemble model, and the results indicated that gamma ray log in the logging sequence contributed the most to the prediction of shale free hydrocarbons content. This study provides a model interpretation experience for predicting free hydrocarbons to help evaluate source rocks and select the “sweet spot” for shale oil.

Accurately characterizing reservoir petrophysical parameters and delineating lithofacies is challenging in heterogeneous formations. Traditional seismic interpretations may be uncertain, but probabilistic neural network (PNN) modeling and seismic inversion constrained by well log data have improved interpretation accuracy and reduced uncertainty in determining reservoir properties such as volume and distribution. It is necessary to determine reservoir assessment parameters precisely and conduct a thorough integrated study of promising blocks that hold paramount potential and will help reduce drilling risk and increase the recovery of oil and gas resources. This paper provides a comprehensive integrated approach to differentiate lithofacies within a gas-prone reservoir (Lower Goru Formation) and predict the potential for hydrocarbon resources in the Sinjhoro Block of Pakistan. This approach involves petrophysical analysis, rock physics, seismic attributes, seismic inversion, multi-attribute analysis, and PNN for estimating petrophysical parameters for source and reservoir rock evaluation. The trace-based 2D extracted attributes, such as pronounced root mean square amplitude anomalies within the Talhar Shale, indicate that hydrocarbon indicators are aligned with the seismic structure interpretation and are considered an appropriate tool for extracting information from poststack seismic data. The results obtained through an integrated approach effectively optimize lateral and vertical facies heterogeneities in target formations, enabling the precise prediction of reservoir parameter distributions. The petrophysical analysis results indicated the presence of gas sands in Basal Sands (hydrocarbon saturation = 53%) and Massive Sands (hydrocarbon saturation = 66%). The current findings demonstrate that the PNN method is the most accurate for estimating petrophysical parameters (volume of shale, total porosity, effective porosity, and water saturation), with a correlation of approximately 0.97–0.99, whereas multi-attribute regression analysis has a correlation of approximately 0.56–0.67. The well log analysis results revealed that the average total organic carbon content of the Talhar Shale in all the wells ranges 1.20–2.20%, its average porosity is 10–16%, its Poisson’s ratio is low (0.20–0.27), and its Young's modulus is high (05–08). Thus, the proposed methodology outlined in the current study has potential applicability in comparable geological settings across various basins in Pakistan and globally.

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.