基于地质统计学反演的薄煤层识别研究  

Research on thin coal seam identification based on geostatistical inversion

作　　者：贺斌 晏豪 HE Bin;YAN Hao(Daliuta Coal Mine,China Energy Shendong Coal Group Co.,Ltd.,Yulin,Shaanxi 719300,China)

机构地区：[1]国能神东煤炭集团有限责任公司大柳塔煤矿,陕西省榆林市719300

出　　处：《中国煤炭》2024年第S01期336-342,共7页China Coal

摘　　要：为提高薄煤层厚度解释精度,构建含多层薄煤层的地质模型,并正演地震剖面。通过模拟一个地质模型来验证地质统计学反演的可靠性,并对模型进行正演模拟,对得到的叠后地震数据进行测井约束反演和地质统计学反演以得到对比。着重于地质统计学反演,分析了反褶积一致性处理的影响。结果表明:适当的反褶积处理可以提高薄煤层的反演精度,高测井密度可以增加地质统计学反演的精度和改善一致性。在恰当的反褶积处理和高信噪比高测井密度地震数据的前提下,通过地质统计学反演可以探明最薄为0.8 m厚度的煤层。To improve the accuracy of interpreting the thickness of thin coal seams,a geological model containing multiple layers of thin coal seams was constructed,and seismic profiles were forward simulated.The reliability of geostatistical inversion was validated by simulating a geological model,and forward modeling was performed on the model.The obtained post stack seismic data was compared through logging constrained inversion and geostatistical inversion.Focusing on geostatistical inversion,the impact of deconvolution consistency processing was analyzed.The results indicated that appropriate deconvolution processing could improve the inversion accuracy of thin coal seams,and high logging density could increase the accuracy and improve consistency of geostatistical inversion.Under the premise of appropriate deconvolution processing and high signal-to-noise ratio logging density seismic data,the thinnest coal seam with a thickness of 0.8 m could be identified through geostatistical inversion.

关 键 词：薄煤层 地质统计学反演 正演模拟 高精度反演 反褶积 

分 类 号：P631[天文地球—地质矿产勘探]