Intelligent identification of sedimentary microfacies based on DMC-BiLSTM

Division of sedimentary microfacies is the basis of oil and gas exploration.This is usually performed manually by geologists based on their knowledge and experience.However, manual identification is time-consuming and subjective, and can lead to human bias.Deep-learning algorithms can solve complex nonlinear problems but they have not been applied to sedimentary microfacies identification thus far.In this study, a method for the intelligent identification of sedimentary microfacies based on feature structure (DMC) and a bidirectional long short-term memory network (BILSTM) is proposed.First, the original curve was reconstructed by trend decomposition and median filtering, and the spatial and temporal correlation clustering features were extracted by the k-means method from the reconstructed matrix and original curve features.Then, by inputting the characteristics of the original curve as well as those of the geological trend, median filtering, and clustering, the sedimentary microfacies at a given depth were predicted based on BILSTM.Compared with the long-short term memory network (LSTM) and time convolution network (TCN), the proposed method performs better and is more robust.Experimental results showed that sedimentary microfacies can be classified by the proposed method with a recognition accuracy of 91.69%.