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测井曲线识别岩性实例研究
首发时间：2019-05-27
摘要：在矿产资源勘探中，岩性的识别与划分是一项既基础又重要的环节。测井数据包含了丰富的地质信息，能够间接的表现地下介质岩性信息，实际工作中，测井数据由于受到泥浆、井径和仪器等测量因素的影响，常规的岩性识别划分存在效率低和准确率低等缺陷。本文以安徽芦岭煤矿l44井为研究目标，利用岩性不同，测井响应不同的特点，识别所选层段的岩性。为了提高识别的准确性，利用小波多尺度分析方法，提取信号低频分量和中频分量，用低频分量和中频分量重构曲线，达到剔除噪声的效果，再以低频分量和中频分量为输入，利用spss（statistical package for the social science）软件k-均值聚类分析法划分识别岩性。通过识别，可以明显区分目标层有砂岩、泥岩、砂泥互层、煤层四种岩性，取得了较好的效果。
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identification of lithology using well logs : a case study
abstract：in the exploration of mineral resources, the identification and division of lithology is a basic and important link. log data contains rich geological information and can indirectly represent the lithology information of underground media. in practice, due to the influence of mud, well diameter, instrument and other measurement factors on log data, conventional lithology identification and classification has the defects of low efficiency and low accuracy. this paper takes well l44 of luling coal mine in anhui province as the research target, and identifies the lithology of the selected interval by using the characteristics of different lithology and different logging response. in order to improve the accuracy of identification, the wavelet multi-scale analysis method was used to extract the low-frequency and intermediate-frequency components of the signal. the low-frequency and intermediate-frequency components were used to reconstruct the curve to achieve the effect of eliminating noise. then the low-frequency and intermediate-frequency components were used as the input, and the software k-means clustering analysis method was used to classify and identify lithology. through identification, the target strata can be distinguished into sandstone, mudstone, sand-mud interbed and coal seam, and good results have been achieved.
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