石油地球物理勘探2025，Vol.60Issue(6)：1399-1408,10.DOI:10.13810/j.cnki.issn.1000-7210.20250096
基于CNN-Transformer的致密砂岩储层孔隙度参数预测研究
Prediction and research of porosity in tight sandstone reservoirs based on CNN-Transformer
摘要
Abstract
The accurate prediction of tight sandstone reservoir parameters is a key scientific issue and technical challenge in unconventional oil and gas exploration.Traditional prediction methods based on linear or nonlinear regression have limitations in characterizing the complex nonlinear relationship between logging curves and reservoir parameters,leading to insufficient prediction accuracy.This study takes the Chang 6 reservoir in the Tang 157 well area of the Ganguyi Production Plant in Yanchang Oilfield as an example.Based on logging data and core analysis porosity data,multi-source data fusion preprocessing is conducted,and a novel neural net-work architecture(CNN-Transformer Network)that integrates the core advantages of CNN and Transformer is innovatively proposed.The prediction performance of the CNN-Transformer model is comprehensively com-pared with that of traditional linear regression(LR),TCN-LSTM,GRU,and ResNet models using RMSE,MAE,and R2 metrics.Experimental results show that the prediction accuracy of the CNN-Transformer model reaches 96.7%,significantly outperforming the other comparative models.This model effectively captures the unique complex nonlinear mapping relationship between logging curves and porosity in tight sandstone reser-voirs,significantly improving the accuracy of reservoir parameter prediction and providing reliable technical sup-port for the efficient exploration and development decision-making of tight sandstone reservoirs.关键词
致密砂岩/储层参数/多源数据融合/深度学习/CNN-TransformerKey words
tight sandstone/reservoir parameters/multi-source data integration/deep learning/CNN-Transformer分类
天文与地球科学引用本文复制引用
PANG Zhenyu,LU Yuqing,XU Yingjin,CHEN Zhicong,CAI Zhenbo,PENG Mengting..基于CNN-Transformer的致密砂岩储层孔隙度参数预测研究[J].石油地球物理勘探,2025,60(6):1399-1408,10.基金项目
本项研究受江西省教育厅项目"基于深度学习的低渗致密储层参数模型研究"(GJJ200744)资助. （GJJ200744）
