石油地球物理勘探2019，Vol.54Issue(2)：447-455,前插7,10.DOI:10.13810/j.cnki.issn.1000-7210.2019.02.024
XGBoost算法在致密砂岩气储层测井解释中的应用
XGBoost algorithm applied in the interpretation of tight-sand gas reservoir on well logging data
闫星宇 1顾汉明 1肖逸飞 2任浩 1倪俊1
作者信息
- 1. 中国地质大学(武汉)地球物理与空间信息学院,湖北武汉 430074
- 2. 地球内部多尺度成像湖北省重点实验室,湖北武汉 430074
- 折叠
摘要
Abstract
Conventional single-model machine learning methods used in tight-sand gas reservoir interpretation on well logging data have the multi-solution problem.To overcome this problem,we use the XGBoost algorithm. Based on logging data in the Area A,different types of well logging data are used as input variables,and a regression prediction model is established by XGBoost algorithm. The porosity and permeability in this area are predicted.The optimization of various parameters in XGBoost algorithm is also discussed.The classification prediction model established by XGBoost algorithm predicts reservoir types in the area.Based on our prediction results,the XGBoost algorithm achieves a better porosity & permeability prediction and tight-sand gas reservoir identification in the area compared with the random forest method and vector-supported machine algorithms.关键词
致密砂岩气储层/机器学习/XGBoost算法/测井解释Key words
ight-sand gas reservoir/machine learning/ XGBoost algorithm/well logging data interpretation分类
天文与地球科学引用本文复制引用
闫星宇,顾汉明,肖逸飞,任浩,倪俊..XGBoost算法在致密砂岩气储层测井解释中的应用[J].石油地球物理勘探,2019,54(2):447-455,前插7,10.
