Petro-Elastic Facies Classification in the Marcellus Shale by Applying Expectation Maximization to Measured Well Logs
Petro-Elastic Facies Classification in the Marcellus Shale by Applying Expectation Maximization to Measured Well Logs
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DOI：
10.1190/segam2014-0939.1
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发表时间：
2014-08
期刊：
影响因子：
--
通讯作者：
K. Schlanser;D. Grana;E. Campbell-Stone
中科院分区：
文献类型：
--
作者：
K. Schlanser;D. Grana;E. Campbell-Stone
摘要提出了一种新的基于测井的相分类方法，该方法使用了一种统计算法--期望最大化，以利用常用的有线测井对岩性相进行分类。这种方法在位于北美东部阿巴拉契亚盆地的非常规储油层马塞卢斯页岩中进行了测试。该方法依赖于高斯混合模型，假设每一相具有唯一的岩石弹性性质的高斯分布。这项技术与泥浆测井、测井解释和区域地层学的准确性进行了对比，产生了令人振奋的结果。
Summary A new methodology for a log-based facies classification using a statistical algorithm, Expectation Maximization, is proposed to classify lithologic-facies utilizing commonly available wireline logs. This method was tested in the Marcellus Shale, an unconventional reservoir located in the Appalachian Basin of eastern North America. The method relies on Gaussian mixture models with the assumption that each facies has a unique Gaussian distribution of petroelastic properties. The technique was checked against mud logs, well log interpretations, and regional stratigraphy for accuracy and produced promising results.