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我国深部开采服役井筒风险分析及监测预警技术研究进展
引用本文:宋朝阳.我国深部开采服役井筒风险分析及监测预警技术研究进展[J].金属矿山,2019,48(3):10-20.
作者姓名:宋朝阳
作者单位:1. 煤炭科学研究总院建井研究分院,北京100013;2. 北京科技大学土木与资源工程学院,北京 100083; 3. 北京中煤矿山工程有限公司,北京 100013
基金项目:* “十三五”国家重点研发计划项目(编号:2016YFC0600801),天地科技股份有限公司科技创新创业资金专项面上项目(编号:2018-TD-MS011)。
摘    要:深地资源开发是我国深地探索的重要方向之一,服役井筒是进入深部地层的必由通道。由于井筒所处岩层及多相地质体分布具有区位差异、服役环境复杂等特征,致使井筒安全服役的风险增高,因此,服役井筒风险分析和监测预警已成为当前亟待解决的问题。在分析井筒风险判识与监测预警技术研究现状和存在问题的基础上,围绕保障井筒安全服役这一最终目标,以多尺度、多灾源协同致灾机理,井筒变形失稳理论,工程风险判识及监测预警技术等为基础,并合理借鉴和吸收其他地下工程动力灾害防控领域的研究成果,提出了多尺度分源监测预警思路,凝练了需要解决的关键科学问题和主要研究方向。针对影响井筒安全服役的应力源、腐蚀源和物性源,对于井筒开裂、腐蚀、偏斜等多源风险灾害,在“区域-采区-井筒-关键截面”尺度下,需要进一步研究的4个关键科学问题为:①开采扰动作用下区域地层应力-能量-物理参数耦合关系及其时空演化规律与多场耦合协同致灾机理;②服役井筒风险灾害多参量前兆信息智能判识与预警理论;③多尺度、多源前兆信息采集与信息集群共网传输技术体系;④服役井筒风险灾害判识方法与预警模型。通过上述4个科学问题的深入研究,有助于实现服役井筒风险在线实时监测、智能判识和准确预警的井筒安全服役状态评价新模式,为我国服役井筒结构安全管理和智能服务提供理论和技术支撑。

关 键 词:深部开采  服役井筒  风险判识  协同致灾机理  多尺度分源监测预警  多场耦合  

Study Progress in Risk Analysis and Monitoring and Early-warning Technique of Shafts under Service for Deep Mining
Song Zhaoyang.Study Progress in Risk Analysis and Monitoring and Early-warning Technique of Shafts under Service for Deep Mining[J].Metal Mine,2019,48(3):10-20.
Authors:Song Zhaoyang
Affiliation:1. Mine Construction Branch,China Coal Research Institute,Beijing100013,China;2. School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing100083,China; 3. Beijing China Coal Mine Engineering Company Ltd.,Beijing 100013,China
Abstract:The exploitation of deeply-buried resources is one of the important trends of deep exploration in China,shafts under service are the only way to enter the deep formation.Whereas,due to being constructed in strata and multi-facies geologic bodies characterized by location difference and complex service condition,shafts are facing increasing risk threatening their safety service.Therefore,risk analysis and monitoring and early-warning of shafts under service has been becoming a problem demanding prompt solution.Through the analysis of study progress and existing problems of risk analysis and monitoring and early-warning technique of shafts under service for deep mining in China,in order to achieve the ultimate aim of ensuring the safety service of shafts,based on the multi-scale and multi-source disaster synergism mechanism,shaft deformation instability theory,engineering risk evaluation and identification and monitoring and early-warning technique,and through rational reference and absorption of other underground engineering dynamic disaster prevention and control study results,the study ideal of multi-scale and multi-source monitoring and warning is proposed,besides that,key scientific problems and main study directions are summarized.Aiming at the stress source,corrosion source and physical property source that affect the safety service of shafts,for the shaft cracking,corrosion,deflection and other multi-source risk disasters,under the scale of “region-mining area-shafts-key sections”,the four key scientific problems should be further studied as followings:①the coupling relationship of regional stratum stress-energy-physical parameters and its spatiotemporal evolution under mining disturbance and multi-field coupling synergetic disaster mechanism;②intelligent judgment and early-warning theory of multi-parameter precursor information of shaft risk disaster in service;③multi-scale and multi-source precursory information collection and information cluster multi-network transmission technique system;④evaluation and identification method and early-warning model of shaft risk disaster in service.Based on further study of the above scientific problems,it is help for the formation of new evaluation model of shaft safety service status based on on-line monitoring,intelligent evaluation and identification,accurate early-warning of shaft safety risk in service,so as to provide theoretical and technical support for structural security management and intelligent service of the shafts in service in China.
Keywords:Deep mining  Shaft in service  Risk evaluation and identification  Disaster synergism mechanism  Multi-scale and multi-source monitoring and warning  Multi-field coupling
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