Prediction of oil and gas column height based on residual network

Aimed at the current situation that compared to the traditional geological methods， the oil and gas column height prediction technology is limited， and the prediction effect is not ideal， the study on the oil and gas column height prediction based on improved residual neural network was carried out. The model extracts the feature information from the structural feature data of traps extracted from the fault interpretation and reservoir dissection to estimate the oil and gas column height. The model turns the serial connection network in the original residual block into multiple parallel connection networks， which can be simultaneously convolved and re aggregated on multiple scales， and can extract features of different scales， and which becomes a sparse， high-performance network structure； at the same time， the skip connection structure in the network is retained， which alleviates the problem of gradient disappearance and network degradation caused by increasing the depth in the deep neural network. The integrity of information is protected by directly bypassing the input information to the output； additionally， the attention modules are added to the first and last layers of the model to capture some local information， so that the model can converge faster. The commonly used RF and BP neural networks in machine learning and the applications of CNN， GoogleNet， ResNet， and ResNet+Atten in deep learning in trap data are compared and analyzed. The experimental results show that the improved ResNet has more accurate results in predicting the height of oil and gas column.