计算机技术与发展2024，Vol.34Issue(7)：199-206,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0095
基于小样本数据的储层渗透率预测方法
A Method for Reservoir Permeability Prediction Based on Small Sample Data
摘要
Abstract
A single hidden layer feedforward neural network(SLFNN)is designed to realize nonlinear regression in order to overcome the shortcomings of complicated processing steps in reservoir permeability calculation by Timur/Coates and SDR formulas.The SLFNN contains a hidden layer with a nonlinear activation function,two linear fully connected layers and a dropout layer.In order to prevent the learning process from falling into local optimum and over-fitting caused by small sample data set,Adam optimizer,ReLU activation function,Kaiming He's uniform distribution weight initialization method and cosine annealing hot restart learning rate adjustment algorithm are used.The number of neurons in the hidden layer,initial learning rate,and inactivation probability of neurons in the dropout layer are determined by using the 5-fold cross validation method based on the small sample data composed of the NMR logging and core from different layers of four production wells from A to D in an oilfield as the training set and validation set.Finally,taking the data of Well E in the same block as the test set,the four models of SLFNN,RFR,SVR and XGBR are used to compare the permeability prediction results.The experimental results show that the MAE and R2 of the SLFNN model are better than those of the other three models under the test set,which indicates that the SLFNN model is effective for the prediction of reservoir permeability.关键词
核磁共振测井/储层渗透率/何恺明权重初始化/模型评价/相关性系数Key words
nuclear magnetic resonance logging/reservoir permeability/Kaiming He weight initialization/model evaluation/correlation coefficient分类
信息技术与安全科学引用本文复制引用
李鹏飞,李鹏举,张强,王辉..基于小样本数据的储层渗透率预测方法[J].计算机技术与发展,2024,34(7):199-206,8.基金项目
国家自然科学基金(42002138) （42002138）
黑龙江省自然科学基金(LH2022F008) （LH2022F008）
