导高不确定条件下涌水量计算模型确定方法
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摘要
高家堡煤矿4煤层顶板分布多个含水层,其中洛河组含水层富水性强且洛河组上下段无明显隔水层,并且导水裂缝带高度也难以观测,所以涌水量计算模型的确定至关重要。根据洛河组的出水水源分析,建立了分段进水非完整井模型,井壁进水非完整井模型和越流模型。采用三种模型对104工作面涌水量进行预测计算并绘制涌水量曲线图。通过与104工作面实测涌水量曲线图对比,确定了分段进水非完整井模型最为合适。通过已采工作面的实测涌水量进行验证,证明该模型是可靠的,对今后矿井涌水量的计算具有借鉴意义。
Multiple aquifers,specially the Luohe formation aquifer with good water yield property are overlying the No.4 coal seam in Gaojiapu coal mine,there is no obvious aquifer in the upper and lower sections of the Luohe formation,and it is difficult to observe height of water conducting fractured zone,so the determination of the water inflow calculation model is crucial.According to the water output data of Gaojiapu coal mine,in the case of high conductivity uncertainty of the Luohe formation aquifer,analyzing partially penetrating well model for water inflow into the shaft wall under two conditions and overflow model by comparing with the actual water inflow from the mine,it is determined that the partially penetrating well model of segmented water is most suitable.The model is proved to be reliable by verifying the measured water inflow of worked surfaces.It can be used for reference to the calculation of mine water inflow in the future.
Multiple aquifers,specially the Luohe formation aquifer with good water yield property are overlying the No.4 coal seam in Gaojiapu coal mine,there is no obvious aquifer in the upper and lower sections of the Luohe formation,and it is difficult to observe height of water conducting fractured zone,so the determination of the water inflow calculation model is crucial.According to the water output data of Gaojiapu coal mine,in the case of high conductivity uncertainty of the Luohe formation aquifer,analyzing partially penetrating well model for water inflow into the shaft wall under two conditions and overflow model by comparing with the actual water inflow from the mine,it is determined that the partially penetrating well model of segmented water is most suitable.The model is proved to be reliable by verifying the measured water inflow of worked surfaces.It can be used for reference to the calculation of mine water inflow in the future.
引文
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[2]郑世书,陈江中,刘汉湖.专门水文地质学[M].徐州:中国矿业大学出版社,1999:46-56.
[3]侯文高,曹始友,高旭波.新安煤矿首采工作面顶板岩层富水性探测及涌水量评价[J].中国矿业,2016,25(S2):270-272.HOU Wengao,CAO Shiyou,GAO Xubo.Water detection and water inflow evaluation of first coalface roof in Xin'an coal mine[J].China Mining Magazine,2016,25(S2):270-272.
[4]徐龙,葛晓光.水文地质比拟法的系统处理模型[J].煤炭学报,1995,20(4):346-350.XU Long,GE Xiaoguang.Systematic processing model of hydrogeological comparison method[J].Journal of China Coal Society,1995,20(4):346-350.
[5]刘基,王强民,杨建.基于Visual Modflow的矿井涌水量模拟和动态预测研究[J].煤矿安全,2018,49(3):190-193.LIU Ji,WANG Qiangmin,YANG Jian.Mine inflow simulation and dynamic prediction based on Visual Modflow[J].Safety in Coal Mines,2018,49(3):190-193.
[6]虎维岳.矿井水害防治理论与方法[M].北京:煤炭工业出版社,2005:102-108.
[7]虎维岳,闫丽.对矿井涌水量预测问题的分析与思考[J].煤炭科学技术,2016,44(1):13-18,38.HU Weiyue,YAN Li.Analysis and consideration on prediction problems of mine water inflow volume[J].Coal Science and Technology,2016,44(1):13-18,38.
[8]潘志.矿井涌水量的数学模型与预测[J].宁夏大学学报:自然科学版,1998,19(3):229-232.PAN Zhi.Mathematical model and prediction of mine water inflow[J].Journal of Ningxia University:Natural Science Edition,1998,19(3):229-232.
[9]王皓.基于灰色模型的煤矿涌水量预测研究[J].中国煤炭地质,2014,26(3):35-38.WANG Hao.Coal mine water inflow prediction based on grey system mode[J].Coal Geology of China,2014,26(3):35-38.
[10]姜素,孙亚军,杨兰.基于BP神经网络方法的矿井涌水量预测[J].中国煤田地质,2007,19(2):38-40.JIANG Su,SUN Yajun,YANG Lan.Prediction of mine water inflow based on BP neural network method[J].Coal Geology of China,2007,19(2):38-40.
[11]徐慧,牟义,邱浩,等.基于层次分析法的井筒涌水量预测效果评价[J].中国矿业,2018,27(7):126-129.XU Hui,MOU Yi,QIU Hao,et al.Evaluation of water inflow prediction in wellbore based on the analytic hierarchy process[J].China Mining Magazine,2018,27(7):126-129.
[12]许进鹏,鹿存金,张学如,等.基于水质水量的导水裂缝带高度分析计算[J].采矿与安全工程学报,2018,35(6):1248-1252.XU Jinpeng,LU Cunjin,ZHANG Xueru,et al.Analysis and calculation of height of water conducting fractured zone based on water quality and inflow[J].Journal of Mining and Safety Engineering,2018,35(6):1248-1252.