Natural Resources Research最新文献

{"title":"An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping","authors":"Zhiqiang Zhang, Gongwen Wang, Emmanuel John M. Carranza, Jingguo Du, Yingjie Li, Xinxing Liu, Yongjun Su","doi":"10.1007/s11053-024-10349-x","DOIUrl":"https://doi.org/10.1007/s11053-024-10349-x","url":null,"abstract":"<p>The uncertainty inherent in three-dimensional (3D) mineral prospectivity mapping (MPM) encompasses (a) mineral system conceptual model uncertainty stemming from geological conceptual frameworks, (b) aleatoric uncertainty, attributable to the variability and noise due to multi-source geoscience datasets collection and processing, as well as 3D geological modeling process, and (c) epistemic uncertainty due to predictive algorithm modeling. Quantifying the uncertainty of 3D MPM is a prerequisite for accepting predictive models in exploration. Previous MPM studies were centered on addressing the mineral system conceptual model uncertainty. To the best of our knowledge, few studies quantified the aleatoric and epistemic uncertainties of 3D MPM. This study proposes a novel uncertainty-quantification machine learning framework to qualify aleatoric and epistemic uncertainties in 3D MPM by the uncertainty-quantification random forest. Another innovation of this framework is utility of the accuracy–rejection curve to provide a quantitative uncertainty threshold for exploration target delineation. The Bayesian hyperparameter optimization tunes the hyperparameters of the uncertainty-quantification random forest automatically. The case study of 3D MPM for exploration target delineation in the Wulong gold district of China demonstrated the practicality of our framework. The aleatoric uncertainty of the 3D MPM indicates that the 3D Early Cretaceous dyke model is the main source of this uncertainty. The 3D exploration targets delineated by the uncertainty-quantification machine learning framework can benefit subsurface gold exploration in the study area.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"202 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140953583","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":"A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field","authors":"Umar Ashraf, Hucai Zhang, Hung Vo Thanh, Aqsa Anees, Muhammad Ali, Zhenhua Duan, Hassan Nasir Mangi, Xiaonan Zhang","doi":"10.1007/s11053-024-10350-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10350-4","url":null,"abstract":"<p>The most crucial elements in the oil and gas sector are predicting subsurface lithofacies utilizing geophysical logs for reservoir characterization and sweet spot assessment procedures. Nevertheless, accurately predicting payable lithofacies in a complex heterogeneous geological setting, such as the lower goru formation, poses considerable difficulty because conventional methods fall short in delivering highly accurate outcomes. Hence, this research proposes an advanced cost and time-saving data intelligence strategy using multiple classifiers to predict lithofacies with maximum accuracy that will aid in sweet spot evaluation in oil and gas fields globally. Geophysical log data of five wells from a mature gas field were used. The targeted reservoir formation was classified into seven facies types. We evaluated the performance of seven different models: support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DTr), naive Bayes (NB), adaptive boosting (AB), and ensemble (an integrated SVM, KNN, RF, and DTr classifier). RF and ensemble classifiers predicted the lithofacies with accuracies of 97.5 and 97.3%, respectively. Their efficacy in lithofacies prediction with high accuracy renders them as valuable tools in the domain of sweet spot evaluation. The proposed digital intelligence strategy could help operators identify drilling sites based on in-depth reservoir characterizations.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"153 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925119","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":"Thermodynamic Characteristics and Kinetic Mechanism of Bituminous Coal in Low-Oxygen Environments","authors":"LiFeng Ren, Fan Tao, TengFei Weng, QingWei Li, Xin Yu, XiaoWei Zhai, Teng Ma","doi":"10.1007/s11053-024-10352-2","DOIUrl":"https://doi.org/10.1007/s11053-024-10352-2","url":null,"abstract":"<p>Coal is a crucial energy source globally, but it poses environmental challenges due to high temperatures and harmful missions during combustion. This study investigates bituminous coal's oxidation combustion in low-oxygen environments using thermogravimetry and differential thermogravimetry tests. We explore the thermal behavior and kinetic properties of three coal samples during combustion. Our findings reveal that, as oxygen concentration decreases, the combined combustion index of the coal samples also decreases during the oxygen-absorption stage. Additionally, the apparent activation energy of coal increases with its conversion rate (temperature). We observe a shift in the reaction mechanism from three-dimensional dissipation mode to two-dimensional as the oxygen concentration decreases. Notably, the activation energy initially rises and then decreases with increasing conversion (temperature) during the pyrolysis combustion stage, with a shortened phase of increased activation energy at lower oxygen concentrations. Furthermore, the kinetic mechanism transitions from stochastic nucleation and growth to one-dimensional phase-boundary mode with decreasing oxygen concentration. These insights enhance our understanding of coal oxidation combustion in low-oxygen environments, contributing to strategies for mitigating coal spontaneous combustion.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"52 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140914932","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":"Micro–Macro Behavior of CBM Extraction in Multi-well Mining Projects","authors":"Dayu Ye, Guannan Liu, Xiang Lin, Hu Liu, Feng Gao","doi":"10.1007/s11053-024-10347-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10347-z","url":null,"abstract":"<p>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 <i>D</i>_<i>f</i> and <i>D</i>_<i>Tf</i>, 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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"80 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140915030","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":"Identification of Geochemical Anomalies Using an End-to-End Transformer","authors":"Shuyan Yu, Hao Deng, Zhankun Liu, Jin Chen, Keyan Xiao, Xiancheng Mao","doi":"10.1007/s11053-024-10334-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10334-4","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"70 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140903034","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":"A Stepwise Cosimulation Framework for Modeling Critical Elements in Copper Porphyry Deposits","authors":"Milena Nasretdinova, Nasser Madani, Mohammad Maleki","doi":"10.1007/s11053-024-10337-1","DOIUrl":"https://doi.org/10.1007/s11053-024-10337-1","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"121 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140902978","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":"Evolution of Elastic–Plastic Characteristics of Rocks Within Middle-Deep Geothermal Reservoirs Under High Temperature","authors":"Qiuyan Wang, Daobing Wang, Bo Yu, Dongliang Sun, Yongliang Wang, Nai Hao, Dongxu Han","doi":"10.1007/s11053-024-10342-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10342-4","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140845138","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":"Modification of Microstructural and Fluid Migration of Bituminous Coal by Microwave–LN2 Freeze–Thaw Cycles: Implication for Efficient Recovery of Coalbed Methane","authors":"He Li, Xi Wu, Meng Liu, Baiquan Lin, Wei Yang, Yidu Hong, Jieyan Cao, Chang Guo","doi":"10.1007/s11053-024-10348-y","DOIUrl":"https://doi.org/10.1007/s11053-024-10348-y","url":null,"abstract":"<p>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 <i>T</i>_<i>2</i> spectrum indicates that the fractal dimensions <i>D</i>_<i>p</i> and <i>D</i>_<i>e</i> have high fitting accuracy, demonstrating that percolation and effective pores exhibit good fractal characteristics. <i>D</i>_<i>p</i> and <i>D</i>_<i>e</i> 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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140845013","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":"Desorption Strain Kinetics of Gas-Bearing Coal based on Thermomechanical Diffusion–Seepage Coupling","authors":"Chengmin Wei, Chengwu Li, Zhenfei Li, Mingjie Li, Min Hao, Yifan Yin","doi":"10.1007/s11053-024-10346-0","DOIUrl":"https://doi.org/10.1007/s11053-024-10346-0","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"61 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140845014","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":"A New Method for Evaluating the Recoverability of Geothermal Fluid Under In Situ Conditions Based on Nuclear Magnetic Resonance","authors":"Peng Zong, Hao Xu, Bo Xiong, Chaohe Fang, Shejiao Wang, Feiyu Huo, Jingjie Wu, Ding Liu, Fudong Xin","doi":"10.1007/s11053-024-10339-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10339-z","url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"18 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140845144","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}