应用卷积神经网络识别测井相

选用东海F气田的砂质辫状河三角洲的自然伽马数据作为训练数据构建深度卷积神经网络，并首次用于测井相识别。选用四种自然伽马曲线形态作为特征，将数值转变为图像形式，首先对图像做标准化、添加噪声、旋转和转灰度等处理，再对数据增强与扩充，建立训练和测试数据集；然后，训练卷积神经网络建立测井相识别模型，并在训练过程中加入了Dropout、局部响应归一化和L₂正则化等策略限制了模型的复杂程度，提高了模型泛化能力；针对测井信息中不同级次沉积单元响应叠加带来的自动识别难题，使用不同尺度的小波基函数及极值分割处理和切分测井数据，最终有效划分了不同尺度沉积单元。通过与其他分类算法对比，验证了所提方法具有较好的测井相识别效果。

Log facies recognition based on convolutional neural network

Natural gamma data of sandy braided river delta sedimentary environments in the Gasfield F,the East China Sea is selected as the training data to construct a deep convolutional neural network,which is used for log facies identification for the first time.Four kinds of gamma ray (GR) curve shapes are selected as characteristics,and their values are converted into the image form.Several processing steps are carried out for these images such as normalization,noise addition,rotation,and grayscale turning,and then this image data set is enhanced and expanded.In this way,training and test data sets are established.After that,a convolutional neural network is trained and used to establish the log facies identification model.During the training process,dropout,local response normalization,and L2 regularization are added to limit the complexity of the model and improve the generalization ability of the model.To automatically identify superposed deposition units of different grades in logging information,different scales of wavelet basis function and extreme value segmentation are used to classify different-scale deposition units of logging data.The comparison with other algorithms demonstrates that the proposed method achieves better log facies identification.