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测井曲线识别岩性实例研究
发表时间：2020-10-10 浏览量：1061 下载量：205
| 全部作者： | 李亮，陈同俊，张雅雯，姜思雨 |
| 作者单位： | 中国矿业大学资源与地球科学学院；中国矿业大学计算机科学与技术学院 |
| 摘 要： | 研究以安徽芦岭煤矿L44井为研究对象，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，并采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软件K-means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细砂岩、泥岩、砂泥互层、煤层四种岩性，取得了较好的效果。 |
| 关 键 词： | 固体地球物理学；测井曲线；小波多尺度分析；SPSS；K-means聚类分析；识别岩性 |
| Title： | Identification of lithology using well logging: a case study |
| Author： | LI Liang, CHEN Tongjun, ZHANG Yawen, JIANG Siyu |
| Organization： | School of Resources and Geosciences, China University of Mining and Technology; School of Computer Science and Technology, China University of Mining and Technology |
| Abstract： | Taking well L44 of Luling coal mine in Anhui province as the research target, the lithology of the selected strata was identified by using the characteristics that different lithologies have different logging responses. 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 SPSS 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. |
| Key words： | solid earth geophysics; well logging; wavelet multi-scale analysis; SPSS; K-means clustering analysis; identify lithology |
| 发表期数： | 2020年9月第3期 |
| 引用格式： | 李亮，陈同俊，张雅雯，等. 测井曲线识别岩性实例研究[J]. 中国科技论文在线精品论文，2020，13（3）：357-363. |
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