摘要
随着测井技术的发展,测井数据量越来越大,传统方法在解决多种测井数据综合形成的大数据问题时遇到了困难。机器学习是人工智能领域的一个重要学科,其多种研究成果的途径是从海量数据中自动提取特征,并通过逐层特征变化进而解决复杂的分类或预测问题,可以完美应用在数据类型较多的测井解释中。本文对机器学习方法及其在地球物理测井评价中的应用进行归纳总结,并提出了展望。
With the development of logging technology, the logging data is getting bigger, and the traditional method can’t solve the big data problems of multiple logging data. Machine learning is an important subject in artificial intelligence. The way of its various research results is to automatically extract features from massive data. It can perfect application in well logging interpretation. This paper summarizes the machine learning method and its application in geophysical logging formation evaluation, and puts forward the prospect.
出处
《石油天然气学报》
CAS
2020年第2期27-38,共12页
Journal of Oil and Gas Technology
关键词
深度学习
机器学习
地球物理测井
岩性识别
储层评价
Deep Learning
Machine Learning
Geophysical Logging
Lithology Identification
Reservoir Evaluation
