Natural Resources Research最新文献

Multi-well extraction is a prevalent technique in coalbed methane (CBM) recovery projects. Although numerous studies have extensively explored aspects such as well spacing, the degree of multi-well pumping, and well count, the dynamics of fracture microstructure evolution in proximity to wells—particularly in inter-well regions—remain inadequately understood in relation to the effects of multi-well mining project. This research delved into the multi-well extraction methodology employed in CBM recovery endeavors, aiming to elucidate the development of the fracture microstructure network. We introduce a novel, interdisciplinary, and integrative research framework that amalgamates the multi-field coupling effects observed during the multi-well extraction process with fractal theory. This model has been validated, and it facilitates the examination of changes in fracture micro-evolution subjected to multi-well extraction. Additionally, this study investigated alterations in fracture characteristics, seam stress, and CBM pressure within sensitive zones (i.e., inter-well spaces and adjacent areas) under varying extraction pressures. Following a 180-day extraction period, the findings indicate a significant reduction in gas pressure by 83.9% for the extraction wells and the nearby areas, alongside a decrease in fracture network length by 10.94% and density by 5.04%. Compared to existing models for assessing multi-well CBM extraction, our interdisciplinary model demonstrates considerable analytical superiority. Notably, when the fractal parameters D_f and D_Tf, which characterize fracture density and tortuosity quantitatively, increase from 1.2 to 1.8, the residual gas pressure is reduced further by 11.6% and increased further by 3.9%, respectively.

Deep learning methods have demonstrated remarkable success in recognizing geochemical anomalies for mineral exploration. Typically, these methods identify anomalies by reconstructing the geochemical background, which is marked by long-distance spatial variability, giving rise to long-range spatial dependencies within geochemical signals. However, current deep learning models for geochemical anomaly recognition face limitations in capturing intricate long-range spatial dependencies. Additionally, concerns emerge from the uncertainty associated with preprocessing in existing deep learning models, which involve generating interpolated images and topological graphs to represent the spatial structure of geochemical samples. In this paper, we present a novel end-to-end method for geochemical anomaly extraction based on the Transformer model. Our model utilizes self-attention mechanism to adequately capture both global and local interconnections among geochemical samples from a holistic perspective, enabling the reconstruction of geochemical background. Moreover, the self-attention mechanism allows the Transformer model to directly input free-form geochemical samples, eliminating the uncertainty associated with the employment of prior interpolation or graph generation typically required for geochemical samples. To align geochemical data with Transformer's architecture, we tailor a specialized data organization integrating learnable positional encoding and data masking. This enables the ingestion of entire geochemical data into the Transformer for anomaly recognition. Capitalizing on the flexibility afforded by the attention mechanism, we devise a contrastive loss for training, establishing a self-supervised learning scheme that enhances model generalizability for anomaly recognition. The proposed method is utilized to recognize geochemical anomalies related to Au mineralization in the northwest Jiaodong Peninsula, Eastern China. By comparison with anomalies identified by models of graph attention network and geographically weighted regression, it is demonstrated that the proposed method is more effective and geologically sound in identifying mineralization-associated anomalies. This superior performance in geochemical anomaly recognition is attributed to its ability to capture long-range dependencies within geochemical data.

The increased attention given to batteries has given rise to apprehensions regarding their availability; they have thus been categorized as essential commodities. Cobalt (Co), copper (Cu), lithium (Li), nickel (Ni), and molybdenum (Mo) are frequently selected as the primary metallic elements in lithium-ion batteries. The principal aim of this study was to develop a computational algorithm that integrates geostatistical methods and machine learning techniques to assess the resources of critical battery elements within a copper porphyry deposit. By employing a hierarchical/stepwise cosimulation methodology, the algorithm detailed in this research paper successfully represents both soft and hard boundaries in the simulation results. The methodology is evaluated using several global and local statistical studies. The findings indicate that the proposed algorithm outperforms the conventional approach in estimating these five elements, specifically when utilizing a stepwise estimation strategy known as cascade modeling. The proposed algorithm is also validated against true values by using a jackknife method, and it is shown that the method is precise and unbiased in the prediction of critical battery elements.

Middle-deep geothermal reservoirs, rich in energy, experience deep burial, high temperature, and intense three-dimensional stresses, causing noticeable elastic–plastic rock deformation under high confining pressure. However, existing researches primarily focused on elastic–plastic properties under various confining pressures, overlooking the impact of high temperature on granite’s behavior. To address this, we conducted compression experiments at seven temperature points (25–600 °C) under varying confining pressures (0–15 MPa). The results reveal that increasing confining pressure prolongs the plastic yielding stage, linearly enhances compressive strength, and shifts rupture mode from brittle to expansion shear damage. Conversely, under constant confining pressure, compressive strength decreases with rising temperature, accompanied by more intricate artificial cracks. Rock cohesion, internal friction angle, and wave velocity decrease due to increased thermal damage micro-cracks. Heat treatment over 500 °C significantly increases porosity and pore throat radius, explaining heightened plasticity in hot dry rocks. These findings offer theoretical and technical insights for understanding elastic–plastic fracture mechanisms during hydraulic fracturing in middle-deep geothermal reservoirs and enhancing heat recovery efficiency.

To improve the efficiency of coalbed methane and recoverability of reservoirs, enhanced fracturing technology is usually required to improve the low porosity and permeability status of coal reservoirs. As a feasible method for strengthening permeability, microwave–LN₂ freeze–thaw (MLFT) cycles modify the microscopic pore structure of coal through the coupled effect of temperature stress changes, phase change expansion, and fatigue damage. ¹H nuclear magnetic resonance combined with fractal dimension theory was used to characterize quantitatively the pore system and geometric features of coal. The geometric fractal model constructed using the T₂ spectrum indicates that the fractal dimensions D_p and D_e have high fitting accuracy, demonstrating that percolation and effective pores exhibit good fractal characteristics. D_p and D_e are correlated negatively and positively, respectively, with the cyclic parameters. The relevance analysis shows that the NMR fractal method can reflect the pore–fracture heterogeneity of coal, which has a significant effect on the percentage of fluid migration space. This study reveals that MLFT cycles have significant enhancement effects on promoting the extension of multi-type pores structures within the coal matrix, as well as the connectivity and permeability of cracks.

The characteristics of coal desorption strain play a crucial role in coal permeability, coalbed methane (CBM) recovery, and the prevention of outbursts. This study developed an improved thermomechanical diffusion–seepage (TMDS) coupling model to investigate the strain evolution during the gas desorption process in coal. The model considers the time-varying diffusion coefficient, the Klinkenberg permeability effect, and the impact of moisture on adsorption, amending the traditional coal deformation equation and coal permeability model. Utilizing this model, the study explored the mechanism, contribution, and spatiotemporal evolution of desorption strain, while analyzing quantitatively the effects of gas types and TMDS parameters on the dynamics of desorption strain. The results demonstrate that desorption strain consists of fracture pressure, matrix pressure, desorption action, and temperature effects, with desorption action being the predominant factor. The impact of gas type, especially CO₂, on desorption strain is significant, with CO₂ enhancing CH₄ desorption strain more than N₂. Additionally, the study explored the sensitivity of desorption strain to TMDS parameters, revealing that gas pressure, permeability, and Langmuir pressure significantly impact desorption strain. Desorption strain can serve as an indicator for predicting and evaluating the risk of outbursts, and the injection of low-temperature liquid nitrogen could help reduce this risk. This research provides insights for further understanding the desorption mechanism in gas-bearing coal, improving CBM recovery, and preventing disasters.

Aiming to solve the problems of unclear pore structure, unknown fluid storage and seepage pattern, and inaccurate fluid recoverability evaluation under in situ high pressure of thermal storage, a new method based on in situ high pressure nuclear magnetic resonance displacement test is proposed for evaluating the seepage capacity and recoverability of geothermal fluid under in situ stress. Based on the study of in situ pore structure and movable water content under different displacement pressures, a new prediction method for the recoverable heat of geothermal reservoir fluids is established. This study finds that significant changes in the pore structure of the samples are observed in the in situ test environment. The pore volumes of macropores and mesopores decrease significantly, while the influence of stress on transition pores and micropores is relatively small. Movable water content increases as a logarithmic function with increase in displacement pressure. Considering in situ stress and fluid mobility, the recoverable heat of geothermal fluids predicted under the new assessment methodology is 27.26% of the static predicted resource. Through the establishment of the above model, accurate prediction of recoverable resources can be realized under different in situ stress.

Effective management of surface emissions from abandoned gob methane (AGM) is crucial for mitigating greenhouse gas emissions and ensuring public safety. An important geochemical characteristic of near-surface AGM migration is the potential presence of abnormal component concentrations in near-surface soil gas over abandoned coal gobs. To investigate this phenomenon, a surface geochemical survey was conducted based on four survey lines in the Dongyi and Dongsan abandoned gob groups in the Xinghua Coal Mine, Jixi Basin, in China. The gas chromatography technique was used to analyze the concentrations of methane, carbon dioxide, ethane, propane, and butane in the collected 43 soil gas samples. The results revealed a significant anomaly of soil gas concentrations, particularly methane and carbon dioxide anomalies, in the near-surface soil over abandoned gobs. The background concentration for methane was determined to be 7.49 ppm, with an anomalous threshold set at 10 ppm based on a statistical analysis and an iterative method. This threshold could be confirmed by examining the coupling and decoupling relationship between methane, carbon dioxide, and C_2–3 as well. A spatial correlation between regions exhibiting anomalous methane and carbon dioxide concentrations and the positions of gob areas, abandoned surface wells, and faults was observed. Abandoned and sealed coalbed methane surface wells and faults near gas-rich gob areas have the potential to act as conduits for AGM leakage to the surface. Furthermore, concentrations of methane, carbon dioxide, and C_2–3 in soil gas over abandoned coal gobs were significantly higher compared to areas unaffected by mining activities. This suggests that elevated concentrations of methane, carbon dioxide, and C_2–3 in soil gas may originate from underground AGM.

Graph-based models have been utilized for mineral prospectivity mapping (MPM), and they have demonstrated excellent performance owing to their adaptable graph structure, which is conducive to comprehensively considering the spatial anisotropy of mineralization compared with pixel- or image-based models. However, widely used graph-based models cannot fully consider the relationship between geological entities and mineralization. A heterogeneous graph is a type of graph structure containing rich heterogeneous information, allowing the consideration of various relationships and the assignment of suitable attributes to various types of nodes. Nodes in heterogeneous graphs can fully integrate heterogeneous information based on specific relations (i.e., edges). This study introduced a novel method for constructing heterogeneous graphs for MPM. The nodes in the graph consist of different types of geological entities, and the edges (relations) represent the links between the geological entities. The constructed heterogeneous graph cannot only effectively express the spatial anisotropy of mineralization but also consider the shape of geological entities and the relationships among geological entities, which is beneficial for modeling complex ore-forming geological processes. This heterogeneous graph was then trained using graph neural networks to obtain a mineral prospectivity map for southwestern Fujian Province, China. In addition, the proposed graph construction method demonstrated higher feasibility and accuracy in MPM compared to conventional graph construction method and convolutional neural networks.

Multicomponent seismic data can be used to predict unconventional reservoirs; however, this is a challenging task. Although machine learning (ML), particularly deep learning, can be used in this regard, its accuracy in reservoir prediction depends largely on the amount of data available for training and the complexity of the architecture. This study attempted to address this problem using transfer learning (TL) and a compact convolutional neural network with a self-attention mechanism (SACNN). We developed a framework for unconventional reservoir prediction by expanding the data samples and optimizing model performance. First, the synthetic data for both oil and gas reservoirs were used as the source data; their effectiveness was tested using the SACNN model. Subsequently, a real dataset was obtained by optimizing the real multicomponent seismic attributes. The TL dataset was constructed by transferring synthetic gas reservoir data to real dataset. Finally, the constructed SACNN model was used to predict the gas-bearing distribution in tight sandstone gas reservoirs. The results showed the superiority of the proposed model over conventional ML models, with lower error in the unconventional reservoir distribution prediction. Moreover, the proposed model exhibited superior prediction performance (R² = 0.9731) on the testing dataset compared to models trained solely on synthetic (R² = 0.9389) and real (R² = 0.9627) data. Moreover, uncertainty analysis showed that the proposed model is robust and efficient. The proposed framework provides a basis for constructing data-driven models for energy conversion and utilization.