鉴定(生物学)
磁导率
计算机科学
过采样
Boosting(机器学习)
数据挖掘
人工智能
接收机工作特性
地质学
石油工程
模式识别(心理学)
机器学习
遗传学
植物
生物
膜
计算机网络
带宽(计算)
作者
Jingyao Lou,Xiaoyin Xu,Zhongxiang Zhao,Yang Li,Yonggang He
出处
期刊:Spe Journal
[Society of Petroleum Engineers]
日期:2023-09-01
卷期号:: 1-12
摘要
Summary Low-resistance reservoirs have been of great interest as a key topic in the study of low-permeability reservoirs. Accurate identification of fluid properties is a challenging problem in the effective exploration of such reservoirs. Logging data, as a common identification tool, can provide rich and highly accurate geological information. Here, we combine extreme gradient boosting (XGBoost) and MAHAKIL’s oversampling method for fluid property identification using logging data from low-permeability sandstone reservoirs. The MAHAKIL method is used to solve the class imbalance problem due to unbalanced training samples, and the data are fed into XGBoost to build a geological model with complex abstract feature weights related to fluid properties through multiple decision trees. We first demonstrate that MAHAKIL can improve the XGBoost model accuracy using four evaluation criteria, namely, the F1-score, recall, precision, and accuracy, among which the F1-score is most applicable to the classification problem of reservoir fluid prediction. Then, the receiver operating characteristic (ROC) and area under the curve (AUC) values are used to demonstrate that MAHAKIL improves the XGBoost model performance. Finally, the results and performance of this method and the traditional XGBoost model with unbalanced real data are compared. The results show that the proposed method is superior for fluid property identification in low-permeability sandstone reservoirs with imbalanced learning samples.
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