物探化探计算技术2026，Vol.48Issue(1)：36-46,11.DOI:10.12474/wthtjs.20250115-0001
基于GA-1D CNN算法的页岩孔隙度预测方法研究
Research on shale porosity prediction method based on GA-1D CNN algorithm
摘要
Abstract
Porosity is a key indicator for evaluating reservoir quality,and obtaining continuous porosity data through experimental testing is extremely expensive.Accurate prediction of porosity based on well log data is therefore crucial for reservoir characterization.However,traditional neural networks suffer from complex parameter tuning and are unable to fully learn the complex nonlinear relationship between well log curves and porosity.In this study,we propose a one-dimensional convolutional neural network(1D CNN)model optimized by a genetic algorithm(GA).First,we analyze the correlations between porosity and various well log parameters—density,acoustic time,shale content,uranium,and potassium—using the Pearson correlation coefficient,which are found to be-0.80,0.72,-0.36,0.43,and-0.34,respectively.Based on these results,the GA-1D CNN model is constructed for porosity prediction in the Longmaxi Formation shale reservoir of Well Y1 in the X area of southern Sichuan,and its performance is compared with that of conventional CNN,GRU,LSTM,and BP models.The results show that:(1)GA optimization enhances the model's global search capability,accelerates convergence,and improves prediction accuracy;(2)the GA-1D CNN converges after 100 training iterations,performing best on both the training and test sets;(3)on the test set of Well Y1,the R²,MAE,and RMSE are 97.98%,0.1292,and 0.2948,respectively,outperforming the other models.This method reduces the risk of overfitting and demonstrates great potential for application in reservoir parameter prediction.关键词
龙马溪组页岩/孔隙度预测/遗传算法/种群进化/1D卷积神经网络Key words
Longmaxi Formation shale/porosity prediction/genetic algorithms/population evolution/1D convolutional neural network分类
天文与地球科学引用本文复制引用
LIU Jiajie,XU Chuan,XIE Xinhui,LI Yong,ZENG Yangfan..基于GA-1D CNN算法的页岩孔隙度预测方法研究[J].物探化探计算技术,2026,48(1):36-46,11.基金项目
2023年度国家资助博士后研究人员计划B档资助(GZB20230089) （GZB20230089）
