Lijian Zhou
ABSTRACT
In order to make better use of the correlation between few lithological features and reduce the model complexity of convolutional neural network, this paper proposes a lithology recognition method based on a multi-scale residual one-dimensional convolutional neural network. Firstly, according to the logging data, acoustic, density, gamma ray, deep lateral resistivity, shallow lateral resistivity, photoelectric absorption cross-sectional index, p-wave velocity and shear wave velocity are selected as lithological characteristics. Due to the large difference between features and the existence of abnormal data, the Laida criterion, least squares moving average filtering and z-score standardization are used for preprocessing. Then, borrowing from the multi-scale idea of inception structure in GoogLeNet and the residual idea of ResNet, a multi-scale residual structure (MsR) is constructed, and further a multi-scale residual one-dimensional convolutional neural network (MsRNet) is constructed with MsR. Finally, lithology identification is performed by MsRNet. Through experiments with a block in Henan Oilfield, it is proved that this method has a higher lithology recognition rate than the lithology recognition methods including the k-nearest neighbor model, the product-based neural network and direct one-dimensional convolutional neural network.
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Index Terms
- Lithology Identification Based on Multi-scale Residual One-dimensional Convolutional Neural Network
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