Machine learning seismic reservoir prediction method based on virtual sample generation

• 作者: Kai-Heng Sang Xing-Yao Yin Fan-Chang Zhang
• 来源期刊: 石油科学（英文版）
• 年，卷（期）: 2021,（6）
• 所属期刊栏目: Petroleum Geology & Geophysics
• 页码范围: 1662-1674
• 页数: 13页
• 语种: 英文

摘要

Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this short-coming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.