Due to the invisibility and complexity of the underground spaces, monitoring the propagation and filling characteristics of the grouting slurry post the water–sand mixture inrush in metal mines is challenging, which complicates engineering treatment. This research investigated the propagation law of cement-sodium silicate slurry under flowing water conditions within the caving mass of a metal mine. First, based on borehole packer test results and borehole TV images, the fractured strata before grouting were classified into four types: cavity, hidden, fissure, and complete. Second, an orthogonal experimental design was employed to evaluate the impact of four key factors—stratigraphic fragmentation, water flow rate, grouting flow rate, and water-cement ratio—on the efficacy of grouting within a caving mass at the site. The results indicate that the factors influencing grouting efficacy are ranked in the following order of importance: stratigraphic fragmentation > water flow rate > water–cement ratio > grouting flow rate. Ultimately, five propagation filling modes—pure slurry, big crack, small crack, small karst pore, and pore penetration—were identified by examining the propagation filling characteristics of slurry in rock samples, incorporating microscopic material structure analysis through scanning electron microscopy and energy spectrum analysis. The findings of this study provide valuable insights into selecting engineering treatment parameters and methodologies, serving as a reference for preventing and controlling water–sand mixture inrush in metal mines, thereby enhancing treatment efficacy and ensuring grouting success.
{"title":"Field investigation of grout propagation within a caving mass under flowing water conditions in a metal mine","authors":"Baofu Wu, Guilei Han, Zhiqi Wang, Jiabin Shi, Hongjiang You, Asrullah","doi":"10.1002/dug2.70001","DOIUrl":"https://doi.org/10.1002/dug2.70001","url":null,"abstract":"<p>Due to the invisibility and complexity of the underground spaces, monitoring the propagation and filling characteristics of the grouting slurry post the water–sand mixture inrush in metal mines is challenging, which complicates engineering treatment. This research investigated the propagation law of cement-sodium silicate slurry under flowing water conditions within the caving mass of a metal mine. First, based on borehole packer test results and borehole TV images, the fractured strata before grouting were classified into four types: cavity, hidden, fissure, and complete. Second, an orthogonal experimental design was employed to evaluate the impact of four key factors—stratigraphic fragmentation, water flow rate, grouting flow rate, and water-cement ratio—on the efficacy of grouting within a caving mass at the site. The results indicate that the factors influencing grouting efficacy are ranked in the following order of importance: stratigraphic fragmentation > water flow rate > water–cement ratio > grouting flow rate. Ultimately, five propagation filling modes—pure slurry, big crack, small crack, small karst pore, and pore penetration—were identified by examining the propagation filling characteristics of slurry in rock samples, incorporating microscopic material structure analysis through scanning electron microscopy and energy spectrum analysis. The findings of this study provide valuable insights into selecting engineering treatment parameters and methodologies, serving as a reference for preventing and controlling water–sand mixture inrush in metal mines, thereby enhancing treatment efficacy and ensuring grouting success.</p>","PeriodicalId":100363,"journal":{"name":"Deep Underground Science and Engineering","volume":"4 2","pages":"222-240"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dug2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lingxiang Wei, Dongjun Guo, Junyuan Ji, Zhilong Chen, Xiaohua Zhou, Mingming Liu, Xingxing Zhao, Hongjun Zheng, Lei Cai
CO2-enhanced oil recovery (CO2-EOR) is an economically viable carbon capture, utilization, and storage (CCUS) technique that is widely practiced and greatly contributes to the achievement of carbon-neutral cities. However, studies on CO2-EOR source–sink matching involving different emission sources, different carbon capture rates, and stepwise CO2 pipeline construction are scarce. Considering four types of carbon sources, including coal-fired power, iron and steel, cement, and chemical plants, with different CO2 capture rates (85%, 90%, 95%, and 100%, respectively), and using a five-phased construction plan with a 25-year build-up period, we developed a method for quantifying carbon emissions from different sources, calculating the effective storage of carbon in CO2-EOR and optimizing CO2-EOR source–sink matching to reduce project costs. Using the Subei Basin in the Jiangsu Province, China, as a case study, we calculated the theoretical CO2-EOR storage to be 1.7408 × 108 t and the effective CO2-EOR storage to be 0.435 × 108 t. We analyzed the completion rate of transportation pipelines, the number of connected carbon sources, and the mass of CO2 stored, as well as the cost-effectiveness and sensitivity. Implementation of CO2-EOR effectively reduced the total cost of source–sink matching in the five-stage 25-year construction approach. The reduction of CO2 capture rates had no effect on the value of oil repelling. The capture cost significantly affected the total cost of source–sink matching, and the impacts of the carbon sources on the total cost were in the order coal-fired power > iron and steel > cement > chemical plants. This study provides an innovative tool for evaluating the CO2 storage potential of CO2-EOR and provides an important framework for implementing CO2-EOR and planning CCUS projects in the Subei Basin and similar regions.
{"title":"Evaluation of the CO2 storage potential in CO2-enhanced oil recovery: A case study of the Subei Basin, Jiangsu Province, China","authors":"Lingxiang Wei, Dongjun Guo, Junyuan Ji, Zhilong Chen, Xiaohua Zhou, Mingming Liu, Xingxing Zhao, Hongjun Zheng, Lei Cai","doi":"10.1002/dug2.12150","DOIUrl":"https://doi.org/10.1002/dug2.12150","url":null,"abstract":"<p>CO<sub>2</sub>-enhanced oil recovery (CO<sub>2</sub>-EOR) is an economically viable carbon capture, utilization, and storage (CCUS) technique that is widely practiced and greatly contributes to the achievement of carbon-neutral cities. However, studies on CO<sub>2</sub>-EOR source–sink matching involving different emission sources, different carbon capture rates, and stepwise CO<sub>2</sub> pipeline construction are scarce. Considering four types of carbon sources, including coal-fired power, iron and steel, cement, and chemical plants, with different CO<sub>2</sub> capture rates (85%, 90%, 95%, and 100%, respectively), and using a five-phased construction plan with a 25-year build-up period, we developed a method for quantifying carbon emissions from different sources, calculating the effective storage of carbon in CO<sub>2</sub>-EOR and optimizing CO<sub>2</sub>-EOR source–sink matching to reduce project costs. Using the Subei Basin in the Jiangsu Province, China, as a case study, we calculated the theoretical CO<sub>2</sub>-EOR storage to be 1.7408 × 10<sup>8</sup> t and the effective CO<sub>2</sub>-EOR storage to be 0.435 × 10<sup>8</sup> t. We analyzed the completion rate of transportation pipelines, the number of connected carbon sources, and the mass of CO<sub>2</sub> stored, as well as the cost-effectiveness and sensitivity. Implementation of CO<sub>2</sub>-EOR effectively reduced the total cost of source–sink matching in the five-stage 25-year construction approach. The reduction of CO<sub>2</sub> capture rates had no effect on the value of oil repelling. The capture cost significantly affected the total cost of source–sink matching, and the impacts of the carbon sources on the total cost were in the order coal-fired power > iron and steel > cement > chemical plants. This study provides an innovative tool for evaluating the CO<sub>2</sub> storage potential of CO<sub>2</sub>-EOR and provides an important framework for implementing CO<sub>2</sub>-EOR and planning CCUS projects in the Subei Basin and similar regions.</p>","PeriodicalId":100363,"journal":{"name":"Deep Underground Science and Engineering","volume":"4 4","pages":"739-761"},"PeriodicalIF":5.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dug2.12150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>This special issue of <i>Deep Underground Science and Engineering</i> (DUSE) showcases pioneering research on the transformative role of machine learning (ML) and Big Data in deep underground engineering. Edited by guest editors Prof. Asoke Nandi (Brunel University of London, UK), Prof. Ru Zhang (Sichuan University, China), Prof. Tao Zhao (Chinese Academy of Sciences, China), and Prof. Tao Lei (Shaanxi University of Science and Technology, China), this issue highlights the innovative applications of ML technique in reshaping structural safety, tunneling operations, and geotechnical investigations.</p><p>As underground engineering challenges grow in complexity, ML and Big Data have become indispensable tools for improving prediction accuracy, optimizing operational efficiency, and ensuring the long-term safety and sustainability of infrastructure. By leveraging vast datasets, automating critical processes, and predicting complex engineering outcomes, these technologies are enabling smarter, more reliable engineering practices that drive both performance and resilience.</p><p>The contributions to this special issue illustrate the diverse and impactful applications of ML and Big Data in deep underground engineering. One article introduces ALSTNet, an advanced data-driven model that integrates long- and short-term time-series data using autoencoders to predict tunnel structural behaviors. When applied to strain monitoring data from the Nanjing Dinghuaimen tunnel, ALSTNet outperforms traditional models, offering promising potential for early disaster prevention in real-world engineering scenarios. Another study presents two robust ML models—Gene Expression Programming (GEP) and a Decision Tree-Support Vector Machine (DT-SVM) hybrid algorithm—to assess pillar stability in deep underground mines. Validated with 236 case histories, these models demonstrate exceptional accuracy and provide valuable tools for project managers to evaluate pillar stability during both the design and operational phases of mining projects. Yet another study demonstrates the use of fuzzy C-means clustering combined with ML models in Tunnel Boring Machine (TBM) operations. This innovative approach enhances prediction accuracy, providing more reliable insights for TBM tunneling processes and boosting efficiency in underground excavation projects.</p><p>Several other papers focus on optimizing monitoring systems for underground structures. One contribution presents a low-cost micro-electromechanical systems (MEMS) sensor designed to monitor tilt and acceleration in underground structures. Aided by ML algorithms, this sensor facilitates real-time monitoring and early warning capabilities, thereby significantly improving safety during underground construction. Another paper introduces a ML-based optimization model for underwater shield tunnels, showing how strategically placed monitoring points—such as at the spandrel and arch crown—can improve the accuracy of stress distribution
{"title":"Machine learning and Big Data in deep underground engineering","authors":"Asoke K. Nandi, Ru Zhang, Tao Zhao, Tao Lei","doi":"10.1002/dug2.70004","DOIUrl":"https://doi.org/10.1002/dug2.70004","url":null,"abstract":"<p>This special issue of <i>Deep Underground Science and Engineering</i> (DUSE) showcases pioneering research on the transformative role of machine learning (ML) and Big Data in deep underground engineering. Edited by guest editors Prof. Asoke Nandi (Brunel University of London, UK), Prof. Ru Zhang (Sichuan University, China), Prof. Tao Zhao (Chinese Academy of Sciences, China), and Prof. Tao Lei (Shaanxi University of Science and Technology, China), this issue highlights the innovative applications of ML technique in reshaping structural safety, tunneling operations, and geotechnical investigations.</p><p>As underground engineering challenges grow in complexity, ML and Big Data have become indispensable tools for improving prediction accuracy, optimizing operational efficiency, and ensuring the long-term safety and sustainability of infrastructure. By leveraging vast datasets, automating critical processes, and predicting complex engineering outcomes, these technologies are enabling smarter, more reliable engineering practices that drive both performance and resilience.</p><p>The contributions to this special issue illustrate the diverse and impactful applications of ML and Big Data in deep underground engineering. One article introduces ALSTNet, an advanced data-driven model that integrates long- and short-term time-series data using autoencoders to predict tunnel structural behaviors. When applied to strain monitoring data from the Nanjing Dinghuaimen tunnel, ALSTNet outperforms traditional models, offering promising potential for early disaster prevention in real-world engineering scenarios. Another study presents two robust ML models—Gene Expression Programming (GEP) and a Decision Tree-Support Vector Machine (DT-SVM) hybrid algorithm—to assess pillar stability in deep underground mines. Validated with 236 case histories, these models demonstrate exceptional accuracy and provide valuable tools for project managers to evaluate pillar stability during both the design and operational phases of mining projects. Yet another study demonstrates the use of fuzzy C-means clustering combined with ML models in Tunnel Boring Machine (TBM) operations. This innovative approach enhances prediction accuracy, providing more reliable insights for TBM tunneling processes and boosting efficiency in underground excavation projects.</p><p>Several other papers focus on optimizing monitoring systems for underground structures. One contribution presents a low-cost micro-electromechanical systems (MEMS) sensor designed to monitor tilt and acceleration in underground structures. Aided by ML algorithms, this sensor facilitates real-time monitoring and early warning capabilities, thereby significantly improving safety during underground construction. Another paper introduces a ML-based optimization model for underwater shield tunnels, showing how strategically placed monitoring points—such as at the spandrel and arch crown—can improve the accuracy of stress distribution","PeriodicalId":100363,"journal":{"name":"Deep Underground Science and Engineering","volume":"4 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dug2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Hydrogen is recognized as a clean energy carrier that can decarbonize heavy industry and the aviation system. However, the infrastructure is not yet ready for a hydrogen economy and large-scale hydrogen storage is needed to balance the mismatch between supply and demand. Therefore, depleted gas fields have been proposed as suitable storage sites, given the presence of infrastructure and pipeline network for distribution and utilization. Attempts have been made to analyze the suitability of these reservoirs for hydrogen storage, with a focus on choosing higher temperature and salinity conditions to neutralize the effects of microbial activities as one of the main sources of hydrogen loss in the depleted gas reservoirs. However, thermochemical sulfate reduction (TSR) is activated at high temperatures and has a huge potential not only to consume hydrogen through abiotic reactions but also to generate a huge amount of <span></span><math>