Reservoir Prediction Based on Improved U-Net Convolutional Neural Network

Most of the traditional U-Net convolutional neural networks have the problem that the gradient of the deep network disappears. In this paper, a residual module is added to the U-Net convolutional neural network, and an improved U-Net convolutional neural network is proposed. The residual module guarantees the existence of the gradient of the U-Net convolutional neural network in the process of error back-propagation, which can alleviate the problem of gradient disappearance to a certain extent. Finally, the improved U-Net convolutional neural network is applied to the actual reservoir prediction. The actual data measurement shows that the improved U-Net convolutional neural network can achieve better results in lithology identification and "Sweet Point" prediction.