Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

作     者：Jing-Jing Liu Jian-Chao Liu Jing-Jing Liu;Jian-Chao Liu

作者机构：School of Earth Science and ResourcesChang’an UniversityXi’an 710064China 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2022年第13卷第1期

页      面：350-363页

核心收录：

学科分类：081801[工学-矿产普查与勘探] 081802[工学-地球探测与信息技术] 081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程] 

基　　金：supported by the Fundamental Research Funds for the Central Universities(Grant No.300102278402) 

主　　题：Deep learning Convolutional neural networks LSTM Lithological-facies classification 3D modeling Class imbalance 

摘      要：The lithofacies classification is essential for oil and gas reservoir exploration and *** traditional method of lithofacies classification is based oncore calibration loggingand the experience of *** approach has strong subjectivity,low efficiency,and high *** uncertainty may be one of the key factors affecting the results of 3 D modeling of tight sandstone *** recent years,deep learning,which is a cutting-edge artificial intelligence technology,has attracted attention from various ***,the study of deep-learning techniques in the field of lithofacies classification has not been ***,this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks(CNN)and the excellent ability to describe time-dependent features of long short-term memory networks(LSTM)to conduct lithological facies-classification *** results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3%and the best classification effect compared to the CNN,LSTM or the three commonly used machine learning models(Support vector machine,random forest,and gradient boosting decision tree).In addition,the borderline synthetic minority oversampling technique(BSMOTE)is introduced to address the class-imbalance issue of raw *** results show that processed data balance can significantly improve the accuracy of lithofacies *** that,based on the fine lithofacies constraints,the sequential indicator simulation method is used to establish a three-dimensional lithofacies model,which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study *** to this comprehensive analysis,the proposed CNN-LSTM model,which eliminates class imbalance,can be effectively applied to lithofacies classification,and is expected to improve the reality of the geologic