面向智能开采的地质模型构建技术研究与应用
Research and application of geological model construction technology for intelligent mining
康健 1张博成 1杨歌2
作者信息
- 1. 国能神东煤炭集团有限责任公司锦界煤矿管理处,陕西神木 719319
- 2. 北京龙软科技股份有限公司,北京 100190
- 折叠
摘要
煤矿智能化开采是未来行业的发展方向,构建工作面的透明地质模型则是基础.当前精细化物探技术精度不高,采用定向钻、人工测量等方法花费大量人力物力,构建的透明工作面煤层空间位置还不够准确,尚无法满足智能开采对基础工作面地质信息的需求.为此,提出利用三维地震数据解释成果融合槽波勘探、钻孔地质雷达、定向钻、生产及钻探揭露的地质信息进行多源数据融合再解释的地质模型构建技术.该技术在中国神华能源股份有限公司神东煤炭分公司锦界煤矿得到成功应用,将工作面三维地质模型的精度从"10米级""米级"提升到"亚米级".基于此项技术,锦界煤矿综采工作面实现了智能截割,大力推进了工作面无人化、少人化的目标.
Abstract
Intelligent mining of coal mine is the development direction of the future industry,and the construction of transparent geological model of working face is the basis.However,the precision of the current fine geophysical prospecting technology is not high,and the use of directional drilling,manual measurement and other methods costs a lot of manpower and material resources.The spatial position of the coal seam of the transparent working face constructed is not accurate enough to meet the needs of intelligent mining for geological information of the basic working face.Therefore,the geological model construction technology of multi-source data fusion and reinterpretation was pro-posed by using the interpretation results of 3D seismic data to fuse the geological information revealed by trough wave exploration,drilling geological radar,directional drilling,production and drilling.The technology was successfully applied in Jinjie Coal Mine of Shendong Coal Branch of China Shenhua Energy Co.,Ltd.,and the precision of the three-dimensional geological model of the working face was upgraded from"ten-meter level"and"meter level"to"sub-meter level".Based on this technology,the fully-mechanized mining face of Jinjie Coal Mine has realized intelligent cutting,and vigorously promoted the goal of unmanned and less human working face.
关键词
智能化开采/透明地质模型/槽波勘探/地质雷达/定向钻/多源数据融合Key words
intelligent mining/transparent geological model/trough wave exploration/geological radar/directional drilling/multi-source data fusion引用本文复制引用
出版年
2024
NETL
NSTL
万方数据