Bidirectional Long Short-term Neural Network Based on the Attention Mechanism of the Residual Neural Network (ResNet–BiLSTM–Attention) Predicts Porosity through Well Logging Parameters

[Image: see text] Porosity is an integral part of reservoir evaluation, but in the field of reservoir prediction, due to the complex nonlinear relationship between logging parameters and porosity, linear models cannot accurately predict porosity. Therefore, this paper uses machine learning methods that can better handle the relationship between nonlinear logging parameters and porosity to predict porosity. In this paper, logging data from Tarim Oilfield are selected for model testing, and there is a nonlinear relationship between these parameters and porosity. First, the data features of the logging parameters are extracted by the residual network, which uses the “hop connections” method to transform the original data closer to the target variable. In addition, the residual blocks inside the residual network use jump connections, which alleviates the gradient vanishing problem caused by increasing depth in deep neural networks. The dynamic nature of data would necessitate LSTM in the first place. Then, a bidirectional long short-term network (BiLSTM) is used to predict the porosity of the extracted logging data features. Among them, the BiLSTM is composed of two independent reverse LSTMs, which can better solve the nonlinear prediction problem. In order to further improve the accuracy of the model, this paper introduces an attention mechanism to learn by weighting each of the inputs in proportion to their impact on the porosity. The experimental results also show that the data features extracted by the residual neural network can be better used as the input of the BiLSTM model.