Natural Resources Research最新文献

{"title":"A Novel Multifractal Method for Geochemical Element Distribution Analysis","authors":"Mengyu Zhao, Yi Jin, Jiabin Dong, Junling Zheng, Qinglin Xia","doi":"10.1007/s11053-024-10450-1","DOIUrl":"https://doi.org/10.1007/s11053-024-10450-1","url":null,"abstract":"<p>Accurately analysis of the multifractal characteristics of geochemical element distribution is crucial for identifying geochemical anomalies and meaningful element associations. However, the most commonly used multifractal method, i.e., the method of moments, may generate different multifractal spectra for a single element distribution due to variations in the range of moment orders. This is because multifractals and their control mechanisms are not well defined. Fractal topography provides a basis for defining multifractals and clarifies the physical meaning of the singularity index. Therefore, a multifractal analysis method based on fractal topography is proposed to generate a unified multifractal spectrum and give new insight into the singularity analysis of element distribution. The similarities and distinctions between the two methods were evaluated using the de Wijs model. The distributions of two multifractal spectra are shown to be fundamentally consistent. The novel method, nevertheless, utilizes fewer statistics and presents a simplified criterion for element enrichment or depletion. To demonstrate its application, Cu geochemical distribution in the Zhongdian area, China, was used as a case study. Based on the comparison results of the two approaches, the proposed novel approach proves beneficial for accurately characterizing the heterogeneity of geochemical element distribution while maintaining a consistent range of the singularity index. The singularity index distribution map at a fine scale provides a comprehensively detailed zonation of geochemical anomalies and, at different scales, it can effectively reveal and interpret the variation of element distribution.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917160","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":"Identifying Types and Key Features of Typical Production Performance of Coalbed Methane with Interpretable Residual Graph Convolutional Model","authors":"Yuqian Hu, Yuhua Chen, Jinhui Luo, Mingfei Xu, Heping Yan, Yunhao Cui, Chao Xu","doi":"10.1007/s11053-024-10448-9","DOIUrl":"https://doi.org/10.1007/s11053-024-10448-9","url":null,"abstract":"<p>The production of coalbed methane (CBM) wells is positively correlated with their production performance, and key features of typical production performance can be applied to determine the high production exploration targets. However, accurately classifying the production types of CBM wells and rationally identifying the key controlling factors among them are challenging due to the strong heterogeneity of CBM reservoirs. The data-driven “black-box” algorithms utilized in previous studies often suffer from limited interpretability due to a lack of sufficient domain theoretical foundation. This paper proposes an interpretable residual graph convolutional neural network model (I–RGCN) for classifying the production types and for identifying key features of typical production of CBM wells from spatial relationships and attribute data. This model constructs a topological graph structure based on the spatial correlations among wells and utilizes the dynamic time warping algorithm to assess the similarity of geological feature parameters among CBM wells, incorporating these as edge weights in the model for accurate classification of CBM production types. Subsequently, the GNNExplainer was used to rank the importance of features during the model's decision-making process. Final experiments conducted on datasets from the Fanzhuang–Zhengzhuang block within the Qinshui coalfield demonstrated that the I–RGCN achieves accuracy of &gt; 84% and F1 score of ~ 65%, and outperformed other baseline models and enhanced the interpretability of the results obtained. Thus, this paper offers a novel and interpretable research methodology for the classification of CBM production types and the identification of key features of the production performance of CBM.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"79 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887050","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":"Thermal Damage and Acoustic Emission Characteristics of High-Temperature Granite under Liquid Nitrogen Cooling","authors":"Yilei Yuan, Kun Zheng, Chaolin Wang, Yu Zhao, Jing Bi","doi":"10.1007/s11053-024-10446-x","DOIUrl":"https://doi.org/10.1007/s11053-024-10446-x","url":null,"abstract":"<p>Liquid nitrogen fracturing is an efficient stimulation technique for exploiting hot dry rock geothermal energy. Understanding the physical and mechanical damage characteristics of high-temperature reservoir rocks under liquid nitrogen cooling is crucial for the application of liquid nitrogen fracturing technology. Therefore, nuclear magnetic resonance technology, acoustic wave velocity measurement technique, acoustic emission (AE) technology, and 3D scanning technology were used to explore changes in the physical and mechanical properties of high-temperature granite under liquid nitrogen cooling from macroscopic and microscopic perspectives. Our research findings show that, as treatment temperature increased, the internal pore structure of the sample changed gradually, with decrease in proportion of micropores and increase in proportion of macropores. The number of pores of various sizes increased gradually. In particular, after treating the granite to a treatment of 600℃, there was a significant increase in the quantity of pores within the granite, primarily manifested by an increase in macropores. From 25 to 600℃, the compressive strength decreased from 160.79 to 68.44 MPa, a reduction of 57.44%; the tensile strength decreased from 11.13 to 6.02 MPa, a reduction of 45.91%. The fractal dimension of the fracture surface of Brazilian disk samples was calculated using the box-counting method, and the results indicated that an increase in treatment temperature would lead to an increase in roughness of the sample’s fracture surface. During the uniaxial compression tests, the AE parameter rise angle (<i>RA</i>) suddenly increased near the peak load. The straight line relationship (average frequency = 11RA + 60) was used to classify the AE signals generated during uniaxial compression of samples. With increase in treatment temperature, the shear signal increased gradually, which is highly consistent with the macroscopic failure characteristics of the samples.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886997","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":"Geochemical Characteristics and Hydrocarbon Generation and Expulsion of Oligocene Yacheng Formation Source Rocks in the Northern Yinggehai Basin","authors":"Yanan Wang, Zhipeng Huo, Gaowei Hu, Jianxiang Pei, Lin Wei, Lin Hu, XiaoFei Fu, Weihong Wang, Jianbo Gao, Jingshuang Luo, Jiansheng Li","doi":"10.1007/s11053-024-10439-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10439-w","url":null,"abstract":"<p>As natural gas exploration and research progress, the Yinggehai Basin has achieved notable advancements and discoveries regarding middle-deep strata in recent years. Due to insufficient exploration in the Northern Yinggehai Basin, the previous studies primarily concentrated on the Miocene reservoirs with lack of research attention on the Oligocene Yacheng Formation (E₃y) source rocks. A comprehensive evaluation of the E₃y source rocks, encompassing hydrocarbon generation, expulsion characteristics, and resource potential, is lacking, thereby hindering further exploration in the study area. Utilizing the geological and geochemical data of E₃y, the hydrocarbon generation and expulsion model was developed. For the first time, a division of hydrocarbon generation and expulsion amount into gas and oil amount is proposed, comprising generated and expelled gas amount, as well as generated and expelled oil amount. The findings suggest that the sedimentary facies of E₃y primarily consisted of delta plain swamp facies and neritic facies, influencing the development of coal-bearing source rocks and marine mudstones. The coal-bearing source rocks exhibited limited thicknesses and areas. During the early to late Oligocene, there was a gradual increase in thickness and area of the marine mudstone. The source rocks exhibit a notable presence of organic matter, with total organic carbon contents ranging from 19% to 95% for coal and 0.12% to 12.6% for mudstone, predominantly composed of type III, and display high maturity indicated by vitrinite reflectance values ranging from 2.0% to 5.0%. At 0.8%, R_o reached the hydrocarbon expulsion threshold, resulting in generated and expelled amounts of 677.73 × 10⁸ t and 441.14 × 10⁸ t, respectively. The generated gas amount, generated oil amount, expelled gas amount and expelled oil amount are 651.2 × 10⁸ t, 30.15 × 10⁸ t, 442.63 × 10⁸ t, and 5.95 × 10⁸ t, respectively. Employing the genetic method, the gas and oil resources of E₃y were estimated at 3.28 × 10⁸ t and 0.46 × 10⁸ t, respectively. This indicates that the Northern Yinggehai Basin presents favorable conditions for exploration.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886999","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":"Machine Learning Prediction of Deep Potential Ores and its Explanation Based on Integration of 3D Geological Model and Numerical Dynamics Simulation: An Example from Dongguashan Orefield, Tongling Copper District, China","authors":"Feihu Zhou, Liangming Liu","doi":"10.1007/s11053-024-10430-5","DOIUrl":"https://doi.org/10.1007/s11053-024-10430-5","url":null,"abstract":"<p>The complex geological architecture, complicated dynamics processes and nonlinear association in mineral systems are the major intrinsic hindrances to predictive mineral exploration. For effectively overcoming such difficulties to achieve credible prediction, 3D geological modeling, numerical dynamics simulation (NDS) and machine learning (ML) were applied to characterize the complex geological architecture, to replay the complicated dynamics processes and to predict mineralization-favor spaces by extracting nonlinear association of multi-features with mineralization in the Dongguashan orefield. The method of SHapley Additive exPlanations (SHAP) was used to explain the correlations between different features and mineralization in the predictive model. The results of the 3D geological modeling revealed that the orebodies are unevenly distributed around the intrusion and closely related to the features of the intrusion’s contact zone and wall rocks. The 3D distribution of resistivity can provide some evidence to infer underground geological architecture rather than a threshold to separate orebodies from wall rocks. The NDS results showed that dilation zones developed around the intrusion and within some beds, being closely associated with the known orebodies. By applying the most popular ML algorithm, random forest, and combining different geological, geophysical and dynamics features as evidence variables, eight ML models were run to predict potential orebodies. The predictive model performance on the test samples indicates that the integration of dynamics evidence with geological evidence significantly improves the predictive capacity of the ML model. The SHAP values demonstrate that volumetric strain is the most important feature, while the inclination of the contact zone has the greatest positive contribution to the predictions. The SHAP values of variable interactions indicate that complex intrusion contact zones and low-pressure, high-dilation areas are closely related to mineralization. The 3D ML prediction evidenced synthetically by geological, geophysical and geodynamical features demonstrates that there are substantial potential ores at depth of the northern east and southern east parts of the orefield.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"86 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884235","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":"Low-Temperature Oxidation Characteristics and Apparent Activation Energy of Pressurized Crushed Coal Under Stress Loading","authors":"Tingxiang Chu, Chunxi Wu, Boning Jiang, Tianru Zhu, Xi Zhang, Yuexia Chen, Lei Li","doi":"10.1007/s11053-024-10444-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10444-z","url":null,"abstract":"<p>With increasing mining depth, coal is more affected by ground and mining stresses. In order to study the characteristics and activation features of coal spontaneous combustion (CSC) under different stress conditions, experiments on low-temperature oxidation under six different stress conditions were conducted using a newly developed multi-field loading and permeability experimental device for stress-loaded and crushed coal. The experimental results showed that, with increase in axial stress from 0 to 15 MPa, the amounts of CO, CO₂, C₂H₄ generated and the rate of oxygen consumption all first followed an increasing trend, reached maximum at 9 MPa, and then a decreasing trend. In three temperature stages—I (20 ℃ &lt; <i>T</i> &lt; 80 ℃), II (90 ℃ &lt; <i>T</i> &lt; 120 ℃), and III (120 ℃ &lt; <i>T</i> &lt; 150 ℃)—all under increasing axial stress from 0 to 15 MPa, the apparent activation energy (AAE) followed a decreasing and then relatively increasing trend. The AAE in all three temperature stages reached a minimum of 9.60 kJ mol⁻¹, 60.57 kJ mol⁻¹, and 19.61 kJ mol⁻¹, respectively, at 9 MPa. Combining the characteristics of gas generation, oxygen consumption, and changes in AAE during the low-temperature oxidation of stress-bearing crushed coal, it was found that stress loading to a certain extent promotes the occurrence of CSC.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"33 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879957","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 Mapping and Differential Metal Endowment Between Two Greenstone Belts in the Canadian Superior Craton","authors":"J. R. Harris, J. Strong, P. Thurston, K. Nymoen, R. Haugaard, M. Naghizadeh, G. Tuba, P. Behnia, E. Grunsky, J. Ayer, R. Smith, R. Sherlock, A. Reza-Mokhtari","doi":"10.1007/s11053-024-10432-3","DOIUrl":"https://doi.org/10.1007/s11053-024-10432-3","url":null,"abstract":"<p>Mineral prospectivity maps were produced for gold in two greenstone belts in the Superior geological province in Ontario, Canada, as part of the Metal Earth Project in the Laurentian University, Sudbury, Ontario. These maps, created using the random forest machine learning algorithm, cover the well-endowed Matheson area, which is in the Abitibi sub-province, and the less fertile Dryden area, which is in the Wabigoon sub-province. Newly identified areas for follow-up gold exploration are associated with major faults and 3D geophysical data comprising resistivity, density and susceptibility data. In addition, observations not used in mineral prospectivity mapping based on magnetotelluric, seismic and isotopic data may in part describe why the Matheson greenstone belt is more fertile with respect to gold mineralization than the Dryden greenstone belt. These observations suggest that the Matheson area has major transcurrent faults associated with conductive zones that reach the surface, many of which are associated with deeply penetrating, vertical faults. The isotopic signature of the Matheson crust also suggests it is juvenile, whereas the Dryden area is older.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"77 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867105","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":"Enhancing Mine Blasting Safety: Developing Intelligent Systems for Accurate Flyrock Prediction through Optimized Group Method of Data Handling Methods","authors":"Xiaohua Ding, Mahdi Hasanipanah, Masoud Monjezi, Rini Asnida Abdullah, Tung Nguyen, Dmitrii Vladimirovich Ulrikh","doi":"10.1007/s11053-024-10445-y","DOIUrl":"https://doi.org/10.1007/s11053-024-10445-y","url":null,"abstract":"<p>Flyrock, the unintended projection of rocks during mining blasts, poses significant safety risks and potential damage. Predicting flyrock is essential for implementing safety measures, minimizing injuries, preventing equipment and structural damage, optimizing blast plans, reducing downtime, and saving costs. Accurate predictions mitigate hazards, improve operational efficiency, and ensure the safety of workers and surrounding infrastructure. This study explored and developed hybrid methods for predicting flyrock using the group method of data handling (GMDH). Four swarm-based algorithms—particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), and whale optimization algorithm (WOA)—were combined with GMDH to enhance prediction accuracy. Additionally, a k-fold cross-validation method was applied to the datasets to improve reliability. The accuracy of these methods was evaluated using various statistical functions, such as Nash–Sutcliffe coefficient and Willmott's index, along with R-squared correlation (R²) graphs, half-violin plots, and quantile–quantile plots. The R² values for the WOA–GMDH, ACO–GMDH, ABC–GMDH, and PSO–GMDH models were 0.99, 0.97, 0.96, and 0.96, respectively. The WOA–GMDH method yielded the most accurate results, demonstrating superior performance when combined with GMDH. Furthermore, the performance of the WOA–GMDH model was compared with models developed in the literature using the same database, confirming its effectiveness. Sensitivity analysis identified that, in WOA–GMDH modeling, the powder factor as the most significant parameter while the spacing parameter was the least significant. The ACO–GMDH method exhibited the narrowest uncertainty band; whereas, the PSO–GMDH method had the widest, indicating the highest level of uncertainty.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"31 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867279","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":"Measurement and Prediction of Blast-Induced Flyrock Distance Using Unmanned Aerial Vehicles and Metaheuristic-Optimized ANFIS Neural Networks","authors":"Hoang Nguyen, Nguyen Van Thieu","doi":"10.1007/s11053-024-10443-0","DOIUrl":"https://doi.org/10.1007/s11053-024-10443-0","url":null,"abstract":"<p>Flyrock from blasting in open-pit mining is one of the most dangerous occurrences that can cause accidents to workers, damage to machinery and equipment and even fatalities. Therefore, quick and reliable prediction of blast-induced flyrock distance (BIFRD) in open-pit mines is very crucial to ensure the safety of the surrounding environment. In this study, unmanned aerial vehicle (UAV) technology combined with advanced artificial intelligence techniques was used to predict BIFRD in open-pit mines and improve safety. UAV was used to record blasting operations and the resulting flyrock. The distance of the flyrock was then measured from the recorded video footage and was analyzed using the ProAnalyst software. Then, various metaheuristics-optimized ANFIS (adaptive neuro-fuzzy inference system) was developed to predict BIFRD. These networks were optimized using adaptive differential evolution with optional external archive (JADE), genetic algorithm (GA), fireworks algorithm (FWA), and artificial bee colony (ABC) algorithms and resulted to JADE–ANFIS, GA–ANFIS, FWA–ANFIS, and ABC–ANFIS models. A dataset with 204 blasting events was gathered and analyzed, and finally, only four input variables were used for developing these models, including spacing, weight charge, stemming, and powder factor. The results showed that JADE–ANFIS is the best with high accuracy (97.8%), good generalizability (MAPE of 1.1%), and reasonable training time for predicting BIFRD in this study. The other models performed poorly with accuracy ranging from 88.7 to 96.5% and MAPE ranging from 1.4 to 3.0%. Sensitivity analysis also showed that the length of stemming is the most affecting factor to flyrock distance in blasting and thus careful consideration should be given in designing blast patterns to control flyrock distance in open-pit mines.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"111 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867278","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":"Mechanism and Models of Nano-Confined Slip Flow of Shale Oil","authors":"Ren-Shi Nie, Jing-Shun Li, Jian-Chun Guo, Zhangxin Chen, Jingcheng Liu, Cong Lu, Fan-Hui Zeng","doi":"10.1007/s11053-024-10440-3","DOIUrl":"https://doi.org/10.1007/s11053-024-10440-3","url":null,"abstract":"<p>The flow of shale oil in nano-scale rock pores follows the slip flow regime, in which the flow velocity at the nanopore walls is not zero. The nano-scale effect of the boundary layer renders the slip flow effect in the nanopores non-negligible. In this study, the slip flow mechanism of shale oil in nanopores was reviewed. The nano-scale effect of the boundary layer renders the slip flow effect in the nanopores non-negligible. The slip length and flow enhancement factor are the primary parameters used to evaluate the slip effect. The main factors influencing the slip effect were then analyzed, including the fluid properties, nanopore properties, pressure gradient, and temperature. Additionally, three slip flow models for shale oil in circular, elliptical and slit nanopores were reviewed. Moreover, a modification method for the shape factor is introduced to evaluate the slip effect of irregular nanopores. The general conclusions regarding the mechanism and models of slip flow in shale oil are summarized as follows: (1) Slip flow of shale oil occurs predominantly in nanopores due to scale effects and stronger internal interaction forces among alkane molecules. (2) The influence of slip flow is more pronounced in organic nanopores than in inorganic nanopores. (3) Significant slip flow effects are observed with larger slip lengths and flow enhancement factors. (4) Our analytical models indicated that slip flow effects are more pronounced with smaller hydraulic diameters. (5) The effects of slip flow are more pronounced in nanopores with irregular geometric shapes. Lastly, recommendations for future research are proposed.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"74 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858474","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}