重庆理工大学学报2024，Vol.38Issue(15)：164-172,9.DOI:10.3969/j.issn.1674-8425(z).2024.08.019
融合SMGC-ECAs-Resnet的致密砂岩岩相识别方法研究
Study on lithofacies identification method of tight sandstone with fusion SMGC-ECAs-Resnet
摘要
Abstract
The task of lithofacies identification and division is important in the exploration and evaluation of tight reservoirs.Currently,the deep learning model usually only extracts single-scale lithofacies features,which fails to obtain multi-scale information and does not fully adapt to the influence of the morphological characteristics of logging curves on lithofacies identification.Based on deep learning and Resnet50-based network,a multi-scale feature extraction module SMGC (Strip-pooling and Multi-scale Group Convolution) is designed and developed.An improved ECAs (Efficient Channel Attention Strengthen) attention module is added to propose a lithofacies identification method of SMGC-ECAs-Resnet tight sandstone logging curve.Fuyu oil layer in Sanzhao sag of Songliao Basin is taken as an example.First,the image data set is built by preprocessing the logging curve data.Then,the SMGC-ECAs-Resnet network model is employed to identify and obtain the classification results.Finally,the validity of the model is verified by comparative and robustness experiments.Our experiments show the proposed SMGC-ECAs-Resnet network reaches the optimal 91.9％ in lithofacies recognition accuracy demonstrating its effectiveness in logging identification of complex tight sandstone lithofacies.关键词
深度学习/多尺度/注意力机制/致密砂岩/岩相识别Key words
deep learning/multi-scale/attention mechanism/tight sandstone/lithofacies identification分类
计算机与自动化引用本文复制引用
田枫,王鑫,刘芳,刘宗堡,刘涛,唐莎莎,刘悦,张世祺..融合SMGC-ECAs-Resnet的致密砂岩岩相识别方法研究[J].重庆理工大学学报,2024,38(15):164-172,9.基金项目
国家自然科学基金面上项目(42172161) （42172161）
黑龙江省省属本科高校基本科研业务费项目(2022TSTD-03) （2022TSTD-03）
黑龙江省自然科学基金项目(LH2021F004) （LH2021F004）
黑龙江省哲学社会科学研究规划年度项目(22EDE389) （22EDE389）
