The edge-guided FPN model for automatic stratigraphic correlation of well logs

Abstract

The stratigraphic correlation of well logs is a fundamental and important topic in seismic well log interpretation. As the number of well logs increases, the automatic stratigraphic correlation of well logs is difficult and time-consuming. With the development of the deep learning (DL) models, several of them have been applied to solve the stratigraphic correlation. However, most of these DL models do not consider the multi-scale features of well logs, which makes against with the accurate and automatic stratigraphic correlation. In this study, by utilizing the multi-scale Feature Pyramid Network (FPN) model, we propose an edge-guided FPN (EFPN) model for the automatic stratigraphic correlation. The proposed EFPN model first extracts the multi-scale features of well logs, which benefits for the automatic stratigraphic correlation. Then, the EFPN model integrates the multi-scale features via the concat operation, where a side output is added for the further edge guidance. Moreover, we propose a hybrid loss function by combining the edge loss and the cross entropy loss for the automatic stratigraphic correlation. Based on the well logs from Daqing Oilfield, China, we first divide the well logs into the training data set and the blind test data set. After training the EFPN model, we make detailed comparisons with the widely used SegNet model. The numerical results show that the proposed EFPN model could obtain the stratigraphic correlation of well logs more accurately than the SegNet model, especially for the stratigraphic sequence boundaries and the boundaries of well logs.