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Experimental study on the physical and mechanical properties of carbonatite rocks under high confining pressure after thermal treatment 热处理后高约束压力下碳酸盐岩物理和机械特性的实验研究
Pub Date : 2024-03-11 DOI: 10.1002/dug2.12079
Wendong Yang, Bingqi Wang, Jun Yao, Pathegama G. Ranjith, Xiang Zhang

Oil and gas exploration studies have been increasingly moving deeper into the earth. The rocks in deep and ultra-deep reservoirs are exposed to a complex environment of high temperatures and large geo-stresses. The Tarim oilfield in the Xinjiang Uygur Autonomous Region (Xinjiang for short), China, has achieved a breakthrough in the exploration of deep hydrocarbon reservoirs at a depth of over 9000 m. The mechanical properties of deep rocks are significantly different from those of shallow rocks. In this study, triaxial compression tests were conducted on heat-treated carbonatite rocks to explore the evolution of the mechanical properties of carbonatite rocks under high confining pressure after thermal treatment. The rocks for the tests were collected from reservoirs in the Tarim oilfield, Xinjiang, China. The experiments were performed at confining pressures ranging from atmospheric to 120 MPa and temperatures ranging from 25 to 500°C. The results show that the critical confining pressure of the brittle–ductile transition increases with increasing temperature. Young's modulus is negatively correlated with the temperature and positively correlated with the confining pressure. As the confining pressure increases, the failure mode of the specimens gradually transforms from shear fracture failure into “V”-type failure and finally into bulging failure (multiple shear fractures). With increasing temperature, the failure angle tends to decrease. In addition, an improved version of the Mohr‒Coulomb strength criterion with a temperature-dependent power function was proposed to describe the failure strength of carbonatite rocks after exposure to high temperature and high confining pressure. The surface of the strength envelope of this criterion is temperature dependent, which could reflect the strength evolution of rock under high confining pressures after thermal treatment. Compared with other strength criteria, this criterion is more capable of replicating physical processes.

石油和天然气勘探研究日益深入地球内部。深层和超深层油藏的岩石暴露在高温和大地应力的复杂环境中。中国新疆维吾尔自治区(简称新疆)塔里木油田在 9000 多米深的深层油气藏勘探方面取得了突破性进展。深部岩石的力学性能与浅部岩石有很大不同。本研究对热处理后的碳酸盐岩进行了三轴压缩试验,以探索热处理后碳酸盐岩在高约束压力下力学性质的演变。用于试验的岩石取自中国新疆塔里木油田的储层。实验在常压至 120 兆帕,温度为 25 至 500 摄氏度的条件下进行。结果表明,脆-韧性转变的临界约束压力随温度升高而增大。杨氏模量与温度呈负相关,与约束压力呈正相关。随着约束压力的增加,试样的破坏模式逐渐从剪切断裂破坏转变为 "V "型破坏,最后转变为鼓胀破坏(多剪切断裂)。随着温度的升高,破坏角呈减小趋势。此外,还提出了一种改进版的莫尔-库仑强度准则,该准则具有随温度变化的幂函数,用于描述碳酸盐岩暴露于高温和高约束压力后的破坏强度。该准则的强度包络面与温度有关,可反映岩石经热处理后在高约束压力下的强度演变。与其他强度标准相比,该标准更能复制物理过程。
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引用次数: 0
Opportunities and challenges for gas coproduction from coal measure gas reservoirs with coal-shale-tight sandstone layers: A review 从具有煤页岩密实砂岩层的煤层气储层中共同生产天然气的机遇与挑战:综述
Pub Date : 2024-03-11 DOI: 10.1002/dug2.12077
Wei Liang, Jianguo Wang, Chunfai Leung, Sianghuat Goh, Shuxun Sang

The extraction of coal measure gas has been shifted toward thin gas reservoirs due to the depletion of medium-thick gas reservoirs. The coproduction of coalbed gas, shale gas, and tight sandstone gas (called a multisuperposed gas system) is a key low-cost technology for the enhancement of natural gas production from thin gas reservoirs in coal measure. As an emerging engineering exploitation technology at its early stage of development, gas coproduction confronts various engineering challenges in hydraulic fracturing, bottom-hole pressure regulation, well network arrangement, and extraction sequence. The current understanding of the opportunities and challenges in the gas coproduction from the multisuperposed gas system is not comprehensive enough. In this case, the previous achievements in the field of gas coproduction should be urgently reviewed to provide valuable guidance and recommendations for further development. This review first discusses the regional and spatial distribution characteristics and possible reservoir combinations of gas reservoirs in coal measure. Then, the basic properties of different reservoirs, engineering challenges, and interlayer interference are comparatively analyzed and discussed. The current simulation models for gas coproduction and potential future research directions are further explored. The results indicate that the coupling effects of reservoir heterogeneity, interwell interference, and geological structure for increasing coproduction prediction accuracy should be included in future simulation models for gas coproduction. Careful investigation is required to explore the mechanisms and their further quantifications on the effects of interlayer interference in gas coproduction. The fractal dimension as a scale can play an important role in the characterization of the gas and water transport in different reservoirs. The machine learning methods have tremendous potential to provide accurate and fast predictions for gas coproduction and interlayer interference.

由于中厚气藏的枯竭,煤层气的开采已转向薄层气藏。煤层气、页岩气和致密砂岩气共生(称为多叠加气系)是提高煤层薄气藏天然气产量的一项低成本关键技术。作为一项处于发展初期的新兴工程开采技术,天然气共生在水力压裂、井底压力调节、井网布置和开采顺序等方面面临着各种工程挑战。目前,人们对多叠加天然气系统联合采气的机遇和挑战的认识还不够全面。在这种情况下,亟需回顾以往在天然气共生领域取得的成就,为进一步发展提供有价值的指导和建议。本综述首先讨论了煤层气储层的区域和空间分布特征以及可能的储层组合。然后,对不同储层的基本性质、工程挑战和层间干扰进行了比较分析和讨论。进一步探讨了当前的瓦斯共生模拟模型和未来潜在的研究方向。结果表明,在未来的天然气共生模拟模型中,应包括储层异质性、层间干扰和地质结构的耦合效应,以提高共生预测的准确性。需要仔细研究层间干扰对天然气共生影响的机制及其进一步量化。分形维度作为一种尺度,可在表征不同储层的气水运移中发挥重要作用。机器学习方法在准确、快速预测气体共生和层间干扰方面具有巨大潜力。
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引用次数: 0
Study on creep deformation and energy development of underground surrounding rock under four-dimensional support 四维支撑下地下围岩的蠕变变形与能量开发研究
Pub Date : 2024-03-05 DOI: 10.1002/dug2.12078
Zhanguo Ma, Junyu Sun, Peng Gong, Pengfei Yan, Nan Cui, Ruichong Zhang

There is an urgent need to develop optimal solutions for deformation control of deep high-stress roadways, one of the critical problems in underground engineering. The previously proposed four-dimensional support (hereinafter 4D support), as a new support technology, can set the roadway surrounding rock under three-dimensional pressure in the new balanced structure, and prevent instability of surrounding rock in underground engineering. However, the influence of roadway depth and creep deformation on the surrounding rock supported by 4D support is still unknown. This study investigated the influence of roadway depth and creep deformation time on the instability of surrounding rock by analyzing the energy development. The elastic strain energy was analyzed using the program redeveloped in FLAC3D. The numerical simulation results indicate that the combined support mode of 4D roof supports and conventional side supports is highly applicable to the stability control of surrounding rock with a roadway depth exceeding 520 m. With the increase of roadway depth, 4D support can effectively restrain the area and depth of plastic deformation in the surrounding rock. Further, 4D support limits the accumulation range and rate of elastic strain energy as the creep deformation time increases. 4D support can effectively reduce the plastic deformation of roadway surrounding rock and maintain the stability for a long deformation period of 6 months. As confirmed by in situ monitoring results, 4D support is more effective for the long-term stability control of surrounding rock than conventional support.

深部高应力巷道的变形控制是地下工程的关键问题之一,亟需制定最佳解决方案。此前提出的四维支护(以下简称四维支护)作为一种新型支护技术,可将巷道围岩置于三维压力下的新型平衡结构中,防止地下工程中围岩失稳。然而,巷道深度和蠕变变形对 4D 支护围岩的影响尚不清楚。本研究通过分析能量发展,研究了巷道深度和蠕变变形时间对围岩失稳的影响。使用在 FLAC3D 中重新开发的程序对弹性应变能量进行了分析。数值模拟结果表明,4D 顶板支护与传统边撑相结合的支护模式非常适用于巷道深度超过 520 米的围岩稳定性控制。随着巷道深度的增加,4D 支护能有效抑制围岩塑性变形的面积和深度。此外,随着蠕变变形时间的增加,4D 支护还能限制弹性应变能的累积范围和速率。4D 支撑能有效减少巷道围岩的塑性变形,并能在 6 个月的长变形期内保持稳定。现场监测结果证实,4D 支护比常规支护更能有效控制围岩的长期稳定性。
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引用次数: 0
Application of integrated geophysical techniques in geothermal exploration in Binhai County, Jiangsu Province 综合地球物理技术在江苏省滨海县地热勘探中的应用
Pub Date : 2024-03-05 DOI: 10.1002/dug2.12075
Juncheng Wang, Shiyin Gao, Jianguo Wang, Lin Li, Xiaoyan Gong, Jinbao Su

Integrated geophysical technology is a necessary and effective means for geothermal exploration. However, integration of geophysical technology for large-scale surveys with those for geothermal reservoir localization is still in development. This study used the controlled source audio-frequency magnetotelluric method technology for large-scale exploration to obtain underground electrical structure information and micromotion detection technology to obtain underground wave velocity structure information. The combination of two detection technologies was used for local identification of geothermal reservoirs. Further, auxiliary correction and inversion constraint were implemented through the audio magnetotelluric sounding technology for maximum authenticity restoration of the near- and transition-field data. Through these technology improvements, a geothermal geological model was established for the Binhai County of Jiangsu Province in China and potential geothermal well locations were identified. On this basis, a geothermal well was drilled nearly 3000 m deep, with a daily water volume of over 2000 m3/day and a geothermal water temperature of 51°C at the well head. It is found that predictions using the above integrated geophysical exploration technology are in good agreement with the well geological formation data. This integrated geophysical technology can be effectively applied for geothermal exploration with high precision and reliability.

综合地球物理技术是地热勘探的必要和有效手段。然而,大尺度勘探地球物理技术与地热储层定位地球物理技术的整合仍处于发展阶段。本研究在大尺度勘探中使用了可控源音频磁法技术,以获取地下电结构信息;在微动探测技术中使用了可控源音频磁法技术,以获取地下波速结构信息。两种探测技术相结合,用于地热储层的局部识别。此外,还通过音频磁极探测技术实现了辅助校正和反演约束,最大限度地还原了近场和过渡场数据的真实性。通过这些技术改进,建立了中国江苏省滨海县的地热地质模型,并确定了潜在的地热井位置。在此基础上,钻探了一口近 3000 米深的地热井,日出水量超过 2000 立方米/天,井口地热水温度为 51°C。结果发现,利用上述综合地球物理勘探技术进行的预测与井内地质构造数据十分吻合。这种综合地球物理技术可有效地应用于地热勘探,具有高精度和高可靠性。
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引用次数: 0
Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm 利用机器学习算法开发隧道应力推导监测点优化模型
Pub Date : 2024-03-03 DOI: 10.1002/dug2.12076
Xuyan Tan, Weizhong Chen, Luyu Wang, Wei Ye

Monitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long-term structural stability. Typically determined by empirical or semi-empirical methods, the limited number of monitoring points and coarse monitoring schemes pose huge challenges in terms of capturing the complete mechanical state of the entire structure. Therefore, with the aim of optimizing the monitoring scheme, this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm. Initially, clustering experiments were performed on a numerical data set to determine the typical positions of structural mechanical responses. Subsequently, supervised learning methods were applied to derive the data information across the entire surface by using the data from these typical positions, which allows flexibility in the number and combinations of these points. According to the evaluation results of the model under various conditions, the optimized number of monitoring points and their locations are determined. Experimental findings suggest that an excessive number of monitoring points results in information redundancy, thus diminishing the deduction capability. The primary positions for monitoring points are determined as the spandrel and hance of the tunnel structure, with the arch crown and inch arch serving as additional positions to enhance the monitoring network. Compared with common methods, the proposed model shows significantly improved characterization abilities, establishing its reliability for optimizing the monitoring scheme.

监测水下盾构隧道的机械行为对确保其长期结构稳定性至关重要。通常情况下,监测点数量有限且监测方案粗糙,这对捕捉整个结构的完整力学状态构成了巨大挑战。因此,为了优化监测方案,本研究采用机器学习算法为整体结构的应力分布引入了一个空间演绎模型。首先,对数值数据集进行聚类实验,以确定结构力学响应的典型位置。随后,应用监督学习方法,利用这些典型位置的数据推导出整个表面的数据信息,从而灵活地确定这些点的数量和组合。根据模型在各种条件下的评估结果,确定了监测点的优化数量和位置。实验结果表明,过多的监测点会导致信息冗余,从而削弱推断能力。监测点的主要位置被确定为隧道结构的拱脊和拱顶,拱顶和寸拱作为增强监测网络的附加位置。与普通方法相比,该模型的表征能力明显提高,为优化监测方案提供了可靠依据。
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引用次数: 0
Experimental study on the influence of content and fineness of fly ash on the mechanical properties of grouting slurries 粉煤灰含量和细度对灌浆泥浆机械性能影响的实验研究
Pub Date : 2024-02-26 DOI: 10.1002/dug2.12070
Hua Jiang, Handong Zhang, Xiaoyan Zhang, Jinxun Zhang, Yusheng Jiang

Excavation gaps around the front shield can be generated during shield construction, resulting in significant ground settlement. Traditional synchronous grouting slurries are unsuitable for filling these gaps during tunneling under existing subway lines. To address this issue, experiments are conducted on mix characteristics and hardening properties of slurries with variations in fineness and contents of fly ash. The experimental and computed tomography scan results yield the following findings: (1) fly ash with high fineness can effectively reduce the early strength of slurries and enhance their injectability. This improves the filling effect on micropores in the slurry and ultimately enhances the final hardening strength. (2) Fineness of fly ash controls the process of slurry hydration. The higher the fineness of fly ash, the more visible the exothermic hydration of slurry and the earlier the highest temperature peak appears. (3) Fly ash with high fineness can effectively increase the density and consolidation rate of slurries, resulting in greater improvement in slurry strength, particularly when the ratio of fly ash to cement (mf/mc) is 0.75. (4) Fly ash with high fineness can effectively decrease the likelihood of appearance of pores in the slurry, optimize the pore structure, and enhance the strength of slurries after consolidation.

在盾构施工过程中,前端盾构周围可能会产生开挖缝隙,导致地面大幅沉降。传统的同步注浆浆液不适合在现有地铁线路下的隧道掘进过程中填补这些缝隙。为解决这一问题,我们对不同细度和粉煤灰含量的泥浆的混合特性和硬化性能进行了实验。实验和计算机断层扫描结果得出以下结论:(1) 高细度粉煤灰可有效降低泥浆的早期强度并提高其可注入性。这可以改善浆液中微孔的填充效果,最终提高最终硬化强度。(2) 粉煤灰细度控制泥浆水化过程。粉煤灰细度越高,泥浆水化放热越明显,最高温度峰值出现得越早。(3) 细度高的粉煤灰能有效提高泥浆的密实度和固结率,使泥浆强度得到较大提高,特别是当粉煤灰与水泥的比例(mf/mc)为 0.75 时。(4) 高细度粉煤灰可有效降低浆体中出现孔隙的可能性,优化孔隙结构,提高浆体固结后的强度。
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引用次数: 0
A meso-damage-based constitutive model for yellow sandstone under dry–wet cycles 干湿循环条件下基于中观损伤的黄砂岩构成模型
Pub Date : 2024-02-25 DOI: 10.1002/dug2.12071
Zhe Qin, Runchang Zhang, Weizheng Mao, Jihuan Han, Zhiwen Li, Sunhao Zhang

The mechanical properties of rocks weaken under dry–wet cycles. This weakening may significantly modify the safety reserve of underground caverns or reservoir bank slopes. However, meso-damage has not been carefully studied based on micromechanical observations and analyses. Therefore, in this study, meso-damage of a yellow sandstone is investigated and a meso-damage-based constitutive model for dry–wet cycles is proposed. First, computed tomography scanning and uniaxial compression tests were conducted on yellow sandstones under different dry–wet cycles. Second, the evolution of rock mesostructures and the damage mechanism subjected to dry–wet cycles were simulated using the discrete element method with Particle Flow Code in 2 Dimensions (PFC2D) software. Third, a constitutive model was proposed based on the meso-statistical theory and damage mechanics. Finally, this constitutive model was verified with the experimental results to check its prediction capability. It is found that the radius and number of pore throats in the sandstone increase gradually with the number of dry–wet cycles, and the pore structure connectivity is also improved. The contact force of sandstone interparticle cementation decreases approximately linearly and the continuity of the particle contact network is continuously broken. The meso-deformation and strength parameters show similar declining patterns to the modulus of elasticity and peak strength of the rock sample, respectively. This meso-damage-based constitutive model can describe well the rock deformation in the initial pressure density stage and the damage stage under the coupling effect of dry–wet cycles and loads.

岩石的机械特性在干湿循环下会减弱。这种削弱可能会极大地改变地下溶洞或水库岸坡的安全储备。然而,基于微观力学观察和分析的中观破坏尚未得到仔细研究。因此,本研究对黄砂岩的中观损伤进行了研究,并提出了基于中观损伤的干湿循环构成模型。首先,对不同干湿循环条件下的黄砂岩进行了计算机断层扫描和单轴压缩试验。其次,利用离散元方法和二维粒子流代码(PFC2D)软件模拟了干湿循环条件下岩石介观结构的演变和损伤机制。第三,在介观统计理论和损伤力学的基础上提出了一个构成模型。最后,用实验结果验证了这一构成模型,以检验其预测能力。研究发现,随着干湿循环次数的增加,砂岩中孔隙节理的半径和数量逐渐增加,孔隙结构的连通性也得到改善。砂岩颗粒间胶结接触力近似线性下降,颗粒接触网络的连续性不断被打破。中观变形和强度参数分别与岩样的弹性模量和峰值强度呈现类似的下降模式。在干湿循环和荷载的耦合作用下,这种基于中观破坏的构造模型能够很好地描述岩石在初始压力密度阶段和破坏阶段的变形。
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引用次数: 0
Numerical study on local scour characteristics around submarine pipelines in the Yellow River Delta silty sandy soil under waves and currents 波浪和水流作用下黄河三角洲淤泥质砂土中海底管道周围局部冲刷特性的数值研究
Pub Date : 2024-02-19 DOI: 10.1002/dug2.12068
Peng Yu, Ruigeng Hu, Jike Zhang, Qi Yang, Jieru Zhao, Lei Cao, Chenghao Zhu

Due to their high reliability and cost-efficiency, submarine pipelines are widely used in offshore oil and gas resource engineering. Due to the interaction of waves, currents, seabed, and pipeline structures, the soil around submarine pipelines is prone to local scour, severely affecting their operational safety. With the Yellow River Delta as the research area and based on the renormalized group (RNG) k-ε turbulence model and Stokes fifth-order wave theory, this study solves the Navier–Stokes (N–S) equation using the finite difference method. The volume of fluid (VOF) method is used to describe the fluid-free surface, and a three-dimensional numerical model of currents and waves–submarine pipeline–silty sandy seabed is established. The rationality of the numerical model is verified using a self-built waveflow flume. On this basis, in this study, the local scour development and characteristics of submarine pipelines in the Yellow River Delta silty sandy seabed in the prototype environment are explored and the influence of the presence of pipelines on hydrodynamic features such as surrounding flow field, shear stress, and turbulence intensity is analyzed. The results indicate that (1) local scour around submarine pipelines can be divided into three stages: rapid scour, slow scour, and stable scour. The maximum scour depth occurs directly below the pipeline, and the shape of the scour pits is asymmetric. (2) As the water depth decreases and the pipeline suspension height increases, the scour becomes more intense. (3) When currents go through a pipeline, a clear stagnation point is formed in front of the pipeline, and the flow velocity is positively correlated with the depth of scour. This study can provide a valuable reference for the protection of submarine pipelines in this area.

海底管道具有高可靠性和成本效益,被广泛应用于近海石油和天然气资源工程。由于波浪、海流、海床和管道结构的相互作用,海底管道周围的土壤容易发生局部冲刷,严重影响管道的运行安全。本研究以黄河三角洲为研究区域,基于重正化群(RNG)k-ε湍流模型和斯托克斯五阶波理论,采用有限差分法求解纳维-斯托克斯(Navier-Stokes)(N-S)方程。采用流体体积(VOF)法描述无流体表面,建立了海流与波浪-海底管道-软沙海底的三维数值模型。利用自建的波流水槽验证了数值模型的合理性。在此基础上,本研究探讨了黄河三角洲淤泥质砂质海床原型环境中海底管线的局部冲刷发展及其特征,分析了管线的存在对周围流场、剪应力、湍流强度等水动力特征的影响。结果表明:(1) 海底管道周围的局部冲刷可分为三个阶段:快速冲刷、缓慢冲刷和稳定冲刷。最大冲刷深度出现在管道正下方,冲刷坑的形状不对称。(2) 随着水深的减小和管道悬浮高度的增加,冲刷变得更加剧烈。(3) 当水流通过管道时,管道前方会形成明显的停滞点,且流速与冲刷深度呈正相关。本研究可为该地区海底管道的保护提供有价值的参考。
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引用次数: 0
Offshore high-grade limemud resources of west coast of India: Economic potential and industrial applications 印度西海岸近海高品位石灰岩资源:经济潜力和工业应用
Pub Date : 2024-01-07 DOI: 10.1002/dug2.12064
Anil Kumar Adukadukkam, Rachna Pillai, Mamatha Puthiyathara Kanakamma

Limestone is one of the essential raw materials in the cement, paint, steel, ceramic, glass, chemical, pharmaceutical, paper, and fertilizer industries. In India, only 8% of the limestone resources are placed under the reserve category, of which 97% is of cement grade. Thus, India depends on imports to bridge the demand-supply gap of steel, blast furnace, and chemical-grade limestone. Efforts of Geological Survey of India (GSI) to locate alternate sources for limestone led to the discovery of enormous quantities of carbonate minerals called limemud from the continental shelf margin of the west coast of India. GSI carried out systematic studies to explore the nature of the disposition, quality, quantity, and suitability of the offshore limemud for various industrial applications. A preliminary estimate of resources using high-resolution subbottom profiling and sediment core sample studies established the occurrence of more than 172 billion tonnes of high-grade (The content of CaCO3 is greater than 80 wt%) limemud in 0.4–28.0 m thick stratified sediment layers spread over an area of 18 000 km2. Chemical, physical, mineralogical, beneficiation, and agglomeration studies found the offshore limemud as a potential replacement for limestone in the cement, filler, blast furnace, steel melting shop, lime production, paint, and Grade-I steel industries. An assessment of mining and transportation costs indicates that the offshore limemud (USD 5–6/ton) is more cost-effective than that imported from other countries (USD16-18/ton). With several advantageous factors like low impurity, mode of occurrence in overburden-free stratified form, fine-grained slurry nature, and shallow water depth, sustainable mining of offshore limemud could be a future reality with controllable technological, economic, and environmental challenges.

石灰石是水泥、油漆、钢铁、陶瓷、玻璃、化工、制药、造纸和化肥行业的重要原材料之一。在印度,只有 8%的石灰石资源属于储备类别,其中 97% 属于水泥级。因此,印度依靠进口来弥补钢铁、高炉和化工级石灰石的供需缺口。印度地质调查局(GSI)努力寻找石灰石的替代来源,结果在印度西海岸大陆架边缘发现了大量被称为石灰泥的碳酸盐矿物。印度地质调查局开展了系统研究,探索近海石灰岩泥的性质、质量、数量以及是否适合各种工业应用。利用高分辨率海底剖面和沉积物岩芯样本研究对资源进行了初步估算,确定在 18 000 平方公里的区域内,0.4-28.0 米厚的分层沉积层中存在超过 1 720 亿吨高品位(CaCO3 含量大于 80%)的石灰泥。化学、物理、矿物学、选矿和团聚研究发现,近海石灰泥有可能替代石灰石用于水泥、填料、高炉、钢铁熔炼车间、石灰生产、油漆和一级钢铁工业。对开采和运输成本的评估表明,近海石灰石泥(5-6 美元/吨)比从其他国家进口的石灰石泥(16-18 美元/吨)更具成本效益。近海石灰泥具有杂质少、以无覆盖层的分层形式存在、泥浆性质为细粒泥浆、水深较浅等优势,可持续开采近海石灰泥可能是未来的现实,但面临的技术、经济和环境挑战是可控的。
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引用次数: 0
Acknowledgment of reviewers
Pub Date : 2023-12-29 DOI: 10.1002/dug2.12074
<p>The editors of <i>Deep Underground Science and Engineering</i> wish to extend our deepest thanks to the following peer reviewers in 2022 and 2023. We are grateful to their contributions of their time and expertise to improve the quality of <i>Deep Underground Science and Engineering</i>. We have made every effort to ensure that all ad hoc reviewers are correctly listed. Our apologies are given to those whose names were unwittingly misspelled or omitted.</p><p>Saeed Aligholi</p><p>Matteo Antelmi</p><p>Wu Cai</p><p>Yajun Cao</p><p>Yonghua Cao</p><p>Jueliang Chen</p><p>Xin Chen</p><p>Xuxin Chen</p><p>Zhangpeng Chen</p><p>Haiyong Cheng</p><p>Jinguo Cheng</p><p>Xin Cui</p><p>Hongyu Duan</p><p>Lifeng Fan</p><p>Ning Fan</p><p>Zhihan Fan</p><p>Qian Fang</p><p>Gan Feng</p><p>Guohui Feng</p><p>Xianhui Feng</p><p>Guoqiang Fu</p><p>Jinwei Fu</p><p>Quan Gan</p><p>Xiaolu Gan</p><p>Rui Gao</p><p>Yubing Gao</p><p>Bin Gong</p><p>Fengqiang Gong</p><p>Wanli Guo</p><p>Xingsen Guo</p><p>Jianyong Han</p><p>Zhenyu Han</p><p>Bowen Hu</p><p>Dawei Hu</p><p>Ran Hu</p><p>Wanrui Hu</p><p>Xiongyu Hu</p><p>Xunjian Hu</p><p>Takuya Ishibashi</p><p>Yinlin Ji</p><p>Lishuai Jiang</p><p>Quan Jiang</p><p>Shu Jiang</p><p>jiangang Jiao</p><p>Viroon Kamchoom</p><p>XIangsong Kong</p><p>Pinnaduwa H. S. W. Kulatilake</p><p>Vinod Kushvaha</p><p>Guohui Lei</p><p>Biao Li</p><p>Diyuan Li</p><p>Jingfa Li</p><p>Jinlong Li</p><p>Peibo Li</p><p>Qi Li</p><p>Wai Li</p><p>Xiaoshuang Li</p><p>Xinggao Li</p><p>Yingchun Li</p><p>Yong Li</p><p>Wei Liang</p><p>Hang Lin</p><p>Kunyue Ling</p><p>Hejuan Liu</p><p>Jianfeng Liu</p><p>Kai Liu</p><p>Richeng Liu</p><p>Xiang Liu</p><p>Xige Liu</p><p>Xin Liu</p><p>Zaobao Liu</p><p>Zihan Liu</p><p>Wenbo Lu</p><p>Yimin Lu</p><p>Zhao Lu</p><p>Ning Luo</p><p>Qing Ma</p><p>Tianshou Ma</p><p>Mohamed Malki</p><p>Piotr Malkowski</p><p>T. Matsumoto</p><p>Fanzhen Meng</p><p>ShengJun Miao</p><p>Fanlu Min</p><p>Pinqiang Mo</p><p>Dongjiang Pan</p><p>Jun Peng</p><p>Wei Qiao</p><p>Peng Qiu</p><p>Sheng Ren</p><p>Yubin Ren</p><p>Shengjie Rui</p><p>Alireza Salmachi</p><p>Payam Shafigh</p><p>Pengfei Shan</p><p>Honglei Shen</p><p>Shaoshuai Shi</p><p>Xiangchao Shi</p><p>Guangyao Si</p><p>Yang Song</p><p>Haijian Su</p><p>Chuang Sun</p><p>Huan Sun</p><p>Behzad Tahmouresi</p><p>Lihai Tan</p><p>Xuhai Tang</p><p>Xu Tao</p><p>Teng Teng</p><p>Nitin Tiwari</p><p>Lluis Torres</p><p>Zeen Wan</p><p>Beifang Wang</p><p>Daobing Wang</p><p>Fuyong Wang</p><p>Guiling Wang</p><p>Haijun Wang</p><p>Hao Wang</p><p>Huachuan Wang</p><p>Jun Wang</p><p>Luqi Wang</p><p>Qi Wang</p><p>Shanyong Wang</p><p>Shimin Wang</p><p>Zhengzheng Wang</p><p>Louis Wong</p><p>Fei Wu</p><p>Wei Wu</p><p>Kaiwen Xia</p><p>Jing Xie</p><p>Weiqiang Xie</p><p>Jiang Xu</p><p>Jingmin Xu</p><p>Tianfu Xu</p><p>Yi Xue</p><p>Yan Yan</p><p>Duoxing Yang</p><p>Feng Yang</p><p>Wenbo Yang</p><p>Wendong Yang</p><p>Zhiwei Ye</p><p>Rui Yong</p><p>Yang Yu</p><p>Chaoyun Yu</p><p>Diao Yu</p><p>Pengliang Yu</p><p>Tiantang Yu</p><p>Chuanqing Zhang</p><p
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Our apologies are given to those whose names were unwittingly misspelled or omitted.&lt;/p&gt;&lt;p&gt;Saeed Aligholi&lt;/p&gt;&lt;p&gt;Matteo Antelmi&lt;/p&gt;&lt;p&gt;Wu Cai&lt;/p&gt;&lt;p&gt;Yajun Cao&lt;/p&gt;&lt;p&gt;Yonghua Cao&lt;/p&gt;&lt;p&gt;Jueliang Chen&lt;/p&gt;&lt;p&gt;Xin Chen&lt;/p&gt;&lt;p&gt;Xuxin Chen&lt;/p&gt;&lt;p&gt;Zhangpeng Chen&lt;/p&gt;&lt;p&gt;Haiyong Cheng&lt;/p&gt;&lt;p&gt;Jinguo Cheng&lt;/p&gt;&lt;p&gt;Xin Cui&lt;/p&gt;&lt;p&gt;Hongyu Duan&lt;/p&gt;&lt;p&gt;Lifeng Fan&lt;/p&gt;&lt;p&gt;Ning Fan&lt;/p&gt;&lt;p&gt;Zhihan Fan&lt;/p&gt;&lt;p&gt;Qian Fang&lt;/p&gt;&lt;p&gt;Gan Feng&lt;/p&gt;&lt;p&gt;Guohui Feng&lt;/p&gt;&lt;p&gt;Xianhui Feng&lt;/p&gt;&lt;p&gt;Guoqiang Fu&lt;/p&gt;&lt;p&gt;Jinwei Fu&lt;/p&gt;&lt;p&gt;Quan Gan&lt;/p&gt;&lt;p&gt;Xiaolu Gan&lt;/p&gt;&lt;p&gt;Rui Gao&lt;/p&gt;&lt;p&gt;Yubing Gao&lt;/p&gt;&lt;p&gt;Bin Gong&lt;/p&gt;&lt;p&gt;Fengqiang Gong&lt;/p&gt;&lt;p&gt;Wanli Guo&lt;/p&gt;&lt;p&gt;Xingsen Guo&lt;/p&gt;&lt;p&gt;Jianyong Han&lt;/p&gt;&lt;p&gt;Zhenyu Han&lt;/p&gt;&lt;p&gt;Bowen Hu&lt;/p&gt;&lt;p&gt;Dawei Hu&lt;/p&gt;&lt;p&gt;Ran Hu&lt;/p&gt;&lt;p&gt;Wanrui Hu&lt;/p&gt;&lt;p&gt;Xiongyu Hu&lt;/p&gt;&lt;p&gt;Xunjian Hu&lt;/p&gt;&lt;p&gt;Takuya Ishibashi&lt;/p&gt;&lt;p&gt;Yinlin Ji&lt;/p&gt;&lt;p&gt;Lishuai Jiang&lt;/p&gt;&lt;p&gt;Quan Jiang&lt;/p&gt;&lt;p&gt;Shu Jiang&lt;/p&gt;&lt;p&gt;jiangang Jiao&lt;/p&gt;&lt;p&gt;Viroon Kamchoom&lt;/p&gt;&lt;p&gt;XIangsong Kong&lt;/p&gt;&lt;p&gt;Pinnaduwa H. S. W. Kulatilake&lt;/p&gt;&lt;p&gt;Vinod Kushvaha&lt;/p&gt;&lt;p&gt;Guohui Lei&lt;/p&gt;&lt;p&gt;Biao Li&lt;/p&gt;&lt;p&gt;Diyuan Li&lt;/p&gt;&lt;p&gt;Jingfa Li&lt;/p&gt;&lt;p&gt;Jinlong Li&lt;/p&gt;&lt;p&gt;Peibo Li&lt;/p&gt;&lt;p&gt;Qi Li&lt;/p&gt;&lt;p&gt;Wai Li&lt;/p&gt;&lt;p&gt;Xiaoshuang Li&lt;/p&gt;&lt;p&gt;Xinggao Li&lt;/p&gt;&lt;p&gt;Yingchun Li&lt;/p&gt;&lt;p&gt;Yong Li&lt;/p&gt;&lt;p&gt;Wei Liang&lt;/p&gt;&lt;p&gt;Hang Lin&lt;/p&gt;&lt;p&gt;Kunyue Ling&lt;/p&gt;&lt;p&gt;Hejuan Liu&lt;/p&gt;&lt;p&gt;Jianfeng Liu&lt;/p&gt;&lt;p&gt;Kai Liu&lt;/p&gt;&lt;p&gt;Richeng Liu&lt;/p&gt;&lt;p&gt;Xiang Liu&lt;/p&gt;&lt;p&gt;Xige Liu&lt;/p&gt;&lt;p&gt;Xin Liu&lt;/p&gt;&lt;p&gt;Zaobao Liu&lt;/p&gt;&lt;p&gt;Zihan Liu&lt;/p&gt;&lt;p&gt;Wenbo Lu&lt;/p&gt;&lt;p&gt;Yimin Lu&lt;/p&gt;&lt;p&gt;Zhao Lu&lt;/p&gt;&lt;p&gt;Ning Luo&lt;/p&gt;&lt;p&gt;Qing Ma&lt;/p&gt;&lt;p&gt;Tianshou Ma&lt;/p&gt;&lt;p&gt;Mohamed Malki&lt;/p&gt;&lt;p&gt;Piotr Malkowski&lt;/p&gt;&lt;p&gt;T. Matsumoto&lt;/p&gt;&lt;p&gt;Fanzhen Meng&lt;/p&gt;&lt;p&gt;ShengJun Miao&lt;/p&gt;&lt;p&gt;Fanlu Min&lt;/p&gt;&lt;p&gt;Pinqiang Mo&lt;/p&gt;&lt;p&gt;Dongjiang Pan&lt;/p&gt;&lt;p&gt;Jun Peng&lt;/p&gt;&lt;p&gt;Wei Qiao&lt;/p&gt;&lt;p&gt;Peng Qiu&lt;/p&gt;&lt;p&gt;Sheng Ren&lt;/p&gt;&lt;p&gt;Yubin Ren&lt;/p&gt;&lt;p&gt;Shengjie Rui&lt;/p&gt;&lt;p&gt;Alireza Salmachi&lt;/p&gt;&lt;p&gt;Payam Shafigh&lt;/p&gt;&lt;p&gt;Pengfei Shan&lt;/p&gt;&lt;p&gt;Honglei Shen&lt;/p&gt;&lt;p&gt;Shaoshuai Shi&lt;/p&gt;&lt;p&gt;Xiangchao Shi&lt;/p&gt;&lt;p&gt;Guangyao Si&lt;/p&gt;&lt;p&gt;Yang Song&lt;/p&gt;&lt;p&gt;Haijian Su&lt;/p&gt;&lt;p&gt;Chuang Sun&lt;/p&gt;&lt;p&gt;Huan Sun&lt;/p&gt;&lt;p&gt;Behzad Tahmouresi&lt;/p&gt;&lt;p&gt;Lihai Tan&lt;/p&gt;&lt;p&gt;Xuhai Tang&lt;/p&gt;&lt;p&gt;Xu Tao&lt;/p&gt;&lt;p&gt;Teng Teng&lt;/p&gt;&lt;p&gt;Nitin Tiwari&lt;/p&gt;&lt;p&gt;Lluis Torres&lt;/p&gt;&lt;p&gt;Zeen Wan&lt;/p&gt;&lt;p&gt;Beifang Wang&lt;/p&gt;&lt;p&gt;Daobing Wang&lt;/p&gt;&lt;p&gt;Fuyong Wang&lt;/p&gt;&lt;p&gt;Guiling Wang&lt;/p&gt;&lt;p&gt;Haijun Wang&lt;/p&gt;&lt;p&gt;Hao Wang&lt;/p&gt;&lt;p&gt;Huachuan Wang&lt;/p&gt;&lt;p&gt;Jun Wang&lt;/p&gt;&lt;p&gt;Luqi Wang&lt;/p&gt;&lt;p&gt;Qi Wang&lt;/p&gt;&lt;p&gt;Shanyong Wang&lt;/p&gt;&lt;p&gt;Shimin Wang&lt;/p&gt;&lt;p&gt;Zhengzheng Wang&lt;/p&gt;&lt;p&gt;Louis Wong&lt;/p&gt;&lt;p&gt;Fei Wu&lt;/p&gt;&lt;p&gt;Wei Wu&lt;/p&gt;&lt;p&gt;Kaiwen Xia&lt;/p&gt;&lt;p&gt;Jing Xie&lt;/p&gt;&lt;p&gt;Weiqiang Xie&lt;/p&gt;&lt;p&gt;Jiang Xu&lt;/p&gt;&lt;p&gt;Jingmin Xu&lt;/p&gt;&lt;p&gt;Tianfu Xu&lt;/p&gt;&lt;p&gt;Yi Xue&lt;/p&gt;&lt;p&gt;Yan Yan&lt;/p&gt;&lt;p&gt;Duoxing Yang&lt;/p&gt;&lt;p&gt;Feng Yang&lt;/p&gt;&lt;p&gt;Wenbo Yang&lt;/p&gt;&lt;p&gt;Wendong Yang&lt;/p&gt;&lt;p&gt;Zhiwei Ye&lt;/p&gt;&lt;p&gt;Rui Yong&lt;/p&gt;&lt;p&gt;Yang Yu&lt;/p&gt;&lt;p&gt;Chaoyun Yu&lt;/p&gt;&lt;p&gt;Diao Yu&lt;/p&gt;&lt;p&gt;Pengliang Yu&lt;/p&gt;&lt;p&gt;Tiantang Yu&lt;/p&gt;&lt;p&gt;Chuanqing Zhang&lt;/p&gt;&lt;p","PeriodicalId":100363,"journal":{"name":"Deep Underground Science and Engineering","volume":"2 4","pages":"406-407"},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dug2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187321","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}
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Deep Underground Science and Engineering
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