Natural Resources Research最新文献

{"title":"Deep Learning-Driven Analysis of Petrophysical Dynamics in Pay Zone Quality and Reservoir Characterization","authors":"Changsheng Deng, Yongke Wang, Weiwei Mi, Xiaofei Xie, Xining Sun, Hamzeh Ghorbani","doi":"10.1007/s11053-025-10490-1","DOIUrl":"https://doi.org/10.1007/s11053-025-10490-1","url":null,"abstract":"<p>Precise characterization of reservoir rocks, particularly regarding porous media properties such as porosity, pore throat permeability, and fluid saturation, is essential for efficient hydrocarbon extraction and management. Traditionally, these properties have been assessed through core sampling and well log analysis. However, the data obtained from point-by-point measurements using these methods are often not generalizable to the entire reservoir's porous media due to the inherent heterogeneity of reservoir rocks, spatial variability, and limited sampling intervals, resulting in significant uncertainty in extrapolation. Recent advancements in data-driven techniques offer promising solutions to overcome these limitations, enhancing the predictive accuracy and interpretive power of petrophysical data. This study investigated the application of leading deep neural network algorithms to model the complex relationships between petrophysical characteristics and porous media properties derived from core samples. Using a dataset comprising 3549 records from three wells in a Middle Eastern oilfield, the research demonstrated the effectiveness of long short-term memory (LSTM) models in capturing nonlinear patterns often overlooked by traditional methods. Principal components analysis (PCA) was used for feature reduction, highlighting key parameters such as medium resistivity (RES-MED), compressional-wave velocity (<i>V</i>p), and the reservoir quality index (RQI) as significant factors influencing reservoir quality. The LSTM model outperformed conventional models, achieving exceptional accuracy with MAE = 0.0001, RMSE = 0.0091, and <i>R</i>² = 0.9856. These findings underscore the potential of machine learning/deep learning models to reduce reliance on labor-intensive core sampling, streamline reservoir characterization, and provide more efficient, cost-effective methodologies for evaluating reservoir quality and optimizing hydrocarbon recovery.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"26 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coal Pore Structure Evolution Under Drying–Wetting Cycle","authors":"Yikang Liu, Haiyan Wang, Huiyong Niu, Shuwen Xing, Gongda Wang, Zhenxing Zhou, Yanxiao Yang, Xiaolu Liu","doi":"10.1007/s11053-025-10481-2","DOIUrl":"https://doi.org/10.1007/s11053-025-10481-2","url":null,"abstract":"<p>The process of dry–wet cycling in coal mining areas exerts a more pronounced degrading effect on coal pores compared to prolonged water immersion, and it enhances the tendency of coal spontaneous combustion. To investigate the fractal characteristics of coal during dry–wet cycling and the evolutionary changes in its overall pore structure, various pore properties were analyzed using scanning electron microscopy, CO₂ adsorption, low-temperature N₂ adsorption, and mercury intrusion porosimetry. The results revealed that with post dry–wet cycling, coal exhibited increased porosity and rougher surface texture. Notably, the apparent porosity of coal after secondary dry–wet cycling reached 24.68. While the type of coal pores remained unchanged across different aperture ranges, there was noticeable increase in cumulative pore volume within the 100–220 nm and 1000 nm aperture segments. Moreover, the primary drying–wetting cycle coal demonstrated the highest cumulative pore-specific surface area and volume within the 0–100 nm pore size range. Interestingly, the drying–wetting cycle did not lead to the formation of micropores in the &lt; 2 nm section; instead, it facilitated the gradual transformation of micropores into mesopores and increased the likelihood of their further evolution into macropores. These findings provide a valuable theoretical basis for the prevention and control of drying–wetting cycle of coal spontaneous combustion disasters and environmental pollution caused by the strategy and increasing the tendency of coal spontaneous combustion. The research results provide theoretical guidance for preventing and controlling water-immersed air-dried coal.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"35 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Coupled Impact of Wettability and Pore Structure on Gas and Water Production in Coal Reservoirs","authors":"Jingyu Wang, Shuheng Tang, Songhang Zhang, Zhaodong Xi, Yang Chen, Jianxin Li, Zhaoxiang Zheng, Xiaoyu Sun, Yanqing Wang","doi":"10.1007/s11053-025-10487-w","DOIUrl":"https://doi.org/10.1007/s11053-025-10487-w","url":null,"abstract":"<p>Efficient development of coalbed methane is crucial for optimizing energy structure, ensuring energy security, and achieving carbon emission reduction targets. This study investigated the combined influence of wettability and pore structure on gas and water production in coal seams. Coal samples from three regions were characterized using low-temperature CO₂ and N₂ adsorption, high-pressure mercury injection, and contact angle tests to determine their pore structure and wettability. Relative permeability experiments were conducted to elucidate the impact of these parameters on gas and water seepage. A virtual vertical well was established based on typical reservoir characteristics of the Qinshui Basin, China. Simulations using the determined wettability and pore structure parameters of the three coal samples were performed to evaluate the influence of these factors on gas and water production in coal reservoirs. The results demonstrated that, as wettability weakened, the water saturation at the isotonic point (<i>S</i>_wx) decreased, while the relative permeability at the isotonic point (<i>K</i>_r(<i>S</i>_wx)) increased. The bound water saturation (<i>S</i>_wc) decreased, while the gas phase relative permeability at bound water saturation (<i>K</i>_rg(<i>S</i>_wc)) increased. The gas–water seepage \"triangle area\" shifted leftward and expanded. The amount of relative permeability loss was lower. Furthermore, both daily gas and water production significantly increased with decreasing wettability. Compared to hydrophilic reservoirs, hydrophobic reservoirs exhibited higher and earlier water production peak, while the gas production peak was higher but occurred later. For reservoirs with well-developed small aperture pores (<i>d</i> &lt; 100 nm), the gas–water production varied more significantly with wettability. By integrating the analysis of wettability and pore structure, reservoirs with developed small aperture pores and strong hydrophilicity were identified as promising targets for wettability modification. This research, using both experimental and simulation methods, provides insights into the effects of wettability and pore structure on fluid flow and production in coal reservoirs at both core and reservoir scales, providing a basis for improving CBM recovery through wettability modification.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"66 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Total Dissolved Solids in Groundwater Using Machine Learning Models","authors":"Sumita Gulati, Anshul Bansal, Ashok Pal","doi":"10.1007/s11053-025-10480-3","DOIUrl":"https://doi.org/10.1007/s11053-025-10480-3","url":null,"abstract":"<p>Accurate forecasting of water quality is pivotal for compelling pollution control and enhanced water management practices. This study predicted total dissolved solids in groundwater samples from West Bengal, India, using data sourced from the Central Pollution Control Board for the span 2020–2022. The parameters include temperature, pH, conductivity, biological oxygen demand, nitrate-N + nitrite-N, fecal coliform, total coliform, fluoride, and arsenic. Employing a diverse set of machine learning models including seven regression models, three support vector machines (SVMs), three artificial neural networks (ANNs), and an adaptive neuro-fuzzy inference system (ANFIS), the study evaluated model performance using root mean square error (RMSE), coefficient of determination (R²), mean square error (MSE), and mean absolute error (MAE). The assessment revealed that the ANN trained with Bayesian regularization emerged as the most effective, boasting the lowest errors (RMSE = 0.00147, MSE = 0.0005, MAE = 0.0112) and the highest R² (0.97), ensuring superior precision. Additionally, ANN trained with Levenberg–Marquardt and ANFIS exhibit commendable performance, showcasing minimal errors and high R² values. Among the non-ANN models, boosted tree displayed a lower RMSE (0.08246) and a higher R² (0.62), while a linear SVM demonstrated balanced performance with RMSE of 0.0877 and R² of 0.57.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"38 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Modeling of Permeability Sensitivities Based on Characteristics of Heterogeneous Coal Structure Reservoirs","authors":"Rui Wang, Kun Zhang, Guofu Li, Liangwei Xu","doi":"10.1007/s11053-025-10489-8","DOIUrl":"https://doi.org/10.1007/s11053-025-10489-8","url":null,"abstract":"<p>Reservoir sensitivities play a crucial role in affecting the production efficiency of coalbed methane, relating to adsorption/desorption dynamics, pore–fracture interactions, and pressure characteristics within the reservoir. This study examined changes in permeability sensitivity within a heterogeneous coal structure reservoir by continuously sampling seven coal sections from roof to floor. The samples underwent low-temperature N₂ and isothermal adsorption analysis of CH₄, as well as microscopic observation experiments. Based on the results, a permeability sensitivity evaluation model was proposed. The study revealed that the No. 3 coal seam in the area maintained its integrity, primarily consisting of intact and cataclastic coal structures. The isothermal adsorption experiments and N₂ adsorption analysis indicated that the highest Langmuir <i>V</i>_<i>L</i> (24.03 cm³/g), specific surface area, and mesopore volumes were found in the middle part of the coal seam. Cataclastic coal, influenced by tectonic deformations, formed more micropores than the intact coal. The coal seam exhibited complex vertical variations in macroscopic and microscopic fractures, including differences in aperture, frequency, spacing, connectivity, and mineralization. The model also simulated changes in permeability sensitivities, considering effective stress and volumetric changes in the coal matrix during gas desorption. It was found that the middle part of the coal seam exhibited the most significant stress sensitivity and the highest permeability loss ratio. The study concludes that attention should be focused on the middle part of the coal seam in the Gaohe and adjacent coal fields for the stimulation of the coalbed methane (CBM) reservoir and prevention of gas outburst. This research is instrumental in determining CBM production design and evaluating development risks.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial–Temporal Response Law and Main Controlling Factors of Temperature During Coal-and-Gas Outburst","authors":"Chaolin Zhang, Qiaozhen Jiang, Xiaofei Liu, Enyuan Wang, Jiabo Geng","doi":"10.1007/s11053-025-10482-1","DOIUrl":"https://doi.org/10.1007/s11053-025-10482-1","url":null,"abstract":"<p>One of the most common catastrophes that occur during the coal mining process is the coal-and-gas outburst. However, due to the complexity of this phenomenon, there are many variables affect it, and the degree of synergistic coupling of various factors is deep, and so the mechanism of its occurrence cannot be fully grasped so far. Existing research mainly focused on the temperature variation law of coal-and-gas outburst under a single influencing factor, which is limited in understanding of the temperature evolution law in the outburst process under the coupling of multiple influencing factors. In this study, the temperature change of the whole process of the outburst was monitored by carrying out physical simulation experiments under different conditions (gas pressure, in-situ stress and permeability). According to the experimental results and theoretical analysis, it was found that the temperature of the coal seam and the roadway following an outburst follows an evolutionary pattern of first rapid decrease, then rapid rise, and finally slow change in time. Then, the weights of three influencing factors were determined by the analytic hierarchy process—criteria importance through inter-criteria correlation (AHP–CRITIC) mixed weighting method, and it was concluded that the temperature evolution of coal-and-gas outburst was mainly controlled by gas pressure. Finally, further fitting was conducted to obtain the exponential variation of temperature drop peak and outburst propagation distance under various conditions of gas pressure, and the physical meanings of different fitting parameters were discussed. On this basis, the abnormal change of coal seam temperature can be detected, and the roadway temperature can be predicted, thereby studying the influence range of the two-phase flow and further evaluating the disaster-causing effect, and providing a new idea for the prediction and prevention of coal-and-gas outburst disaster.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"49 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty Quantification of Microblock-Based Resource Models and Sequencing of Sampling","authors":"Glen T. Nwaila, Emmanuel John M. Carranza","doi":"10.1007/s11053-025-10485-y","DOIUrl":"https://doi.org/10.1007/s11053-025-10485-y","url":null,"abstract":"<p>Spatial models are fundamental across the mineral value chain, forming the basis for exploration and extraction. Geodata science and increasingly bigger data permit alternatives to traditional mineral resource estimation methods, particularly in spatial data interpolation. Interpolation has been formulated as a machine learning (ML) task, providing new capabilities, such as automated deployment and remote real-time monitoring. However, a significant gap exists regarding how uncertainty propagates through ML workflows. This paper introduces an uncertainty propagation method to a ML-based interpolation method called microblocking that propagates epistemic uncertainty. Our method adheres to the data science framework and is fully ML-based. Epistemic uncertainty is the dominant uncertainty in geosciences, because data sparsity is created by both complex dynamics of physical systems and sampling limitations. Our uncertainty estimates are block-specific and can guide sampling and other activities. Biasing sampling toward blocks with high economic potential and high uncertainty enables the most cost-effective sequencing of sampling. A rapid, ML-based uncertainty quantification method provides a modern data-driven (feedback-based) framework to extraction guidance, built on big data, geodata science, and real-time mineral resource modeling. We compare our method with typical kriging uncertainty estimates and demonstrates that our results are more block-specific and broader in scope (more comprehensive). In an industry where financial stakes are significant, a thorough understanding of uncertainty can improve investor confidence. The method not only improves scientific rigor, but is also engineered to fit increasingly bigger data across the mineral value chain, and caters to the conservative nature of the mineral industry, where method validation occurs at a slower pace.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"58 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mineral Prospectivity Modeling of Graphite Deposits and Occurrences in Canada","authors":"Steven E. Zhang, Christopher J. M. Lawley, Julie E. Bourdeau, Mohammad Parsa, Renato Cumani, Aaron Thompson","doi":"10.1007/s11053-024-10451-0","DOIUrl":"https://doi.org/10.1007/s11053-024-10451-0","url":null,"abstract":"<p>Exploration for graphite in Canada is of economic, strategic and governance priority. In this study, we aimed to develop a reliable prospectivity map for graphite in Canada. Our approach mitigated multiple sources of workflow-induced uncertainty by propagating uncertainty due to the selection of negative labels, machine learning algorithms, feature space dimensionality, and hyperparameter tuning metrics. By averaging an ensemble of de-correlated models, we produced a single-merged model that clearly represents propagated uncertainty through a consensus map and an uncertainty map. These maps adhere to the metrological convention of \"result plus/minus associated uncertainty\" and are intuitive to use. Our ensemble demonstrated robustness, quickly converging to the consensus model, suggesting that new mineral prospectivity mapping (MPM) products using the same data would unlikely perturb our consensus model’s coverage. We conducted a maximally double-blind study, avoiding geoscientific knowledge during model generation to ensure impartial post-hoc analysis and interpretation. Therefore, our MPM products complement geoscientific knowledge-based exploration, because the targeting information provided in our MPM products constitute a maximally independent source. Our MPM products showed excellent spatial variability, aligning with existing knowledge of graphite deposits in Canada, indicating that combining data-driven rigor with independent interpretation enhances the robustness of our MPM products. Consequently, we believe our MPM products could effectively guide regional exploration of natural graphite in Canada.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"78 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface Movement Law Caused by Continuous Mining: A Case Study of Loess Plateau Coal Mines","authors":"Junlei Xue, Fuquan Tang, Qian Yang, Tao Yuan, Jiakun Gao, Chao Zhu, Yu Su, Ting Ma","doi":"10.1007/s11053-025-10479-w","DOIUrl":"https://doi.org/10.1007/s11053-025-10479-w","url":null,"abstract":"<p>The surface movement law induced by continuous mining across multiple working faces is distinct compared to that of a single working face. It is essential to understand and analyze this law to ensure the safety of coal mining operations. This study employed a research method that integrates numerical simulation and theoretical analysis to define, for the first time, the concepts of the repeated mining subsidence ratio and seemingly full mining. The analysis of ground surface movement in multiple mine working faces revealed that: The ground surface in multiple mine working faces within the Loess Plateau coal mines experienced multiple movements, with the center of subsidence deviating from the center of the working face. In the direction of surface inclination, the subsidence followed a cyclic pattern as it approached full mining, with the center of subsidence shifting away from the center of the mining area and positioning itself atop the spacer coal pillar. Multiple mine working faces intensify surface deformation and prolong surface movement. Spacer coal pillars between adjacent mine working faces provide structural support to surface subsidence deformation. Surface movement deformation results from the combined effects of slope slippage and mining-induced subsidence. The findings of this study establish a foundation for further research on surface movement and deformation in multiple mine working faces in the Loess Plateau coal mines.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"183 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Classification of Mineral Resources: Moving Toward Overlapping Categories to Account for Geological, Economic, Metallurgical, Environmental, and Operational Criteria","authors":"Nadia Mery, Mohammad Maleki, Gabriel País, Andrés Molina, Alejandro Cáceres, Xavier Emery","doi":"10.1007/s11053-025-10470-5","DOIUrl":"https://doi.org/10.1007/s11053-025-10470-5","url":null,"abstract":"<p>A pivotal aspect in the evaluation of mining projects is the classification of mineral resources, which directly influences the definition of mineral reserves and significantly impacts mine planning and operational stages. However, the current classification methodologies often need specificity regarding the methods and parameters employed and heavily rely on the qualified/competent person’s judgment. This study addresses these gaps by proposing a pioneering fuzzy approach to assess grade and tonnage uncertainties. By allowing for overlapping resource categories and directly incorporating economic, geological, metallurgical, environmental, and operational criteria, we aim to provide tools for decision-making and for the final classification and public disclosure of mineral resources and reserves. We demonstrate the potential of our proposed methodology through an application to an iron ore deposit case study and through a detailed discussion on its uses, contributions, strengths, weaknesses, and on whether it complies with international reporting codes.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}