Fracture Prediction from Conventional Well Logs Using GMM-Based Sample Augmentation and an iTransformer-LSTM Network

Natural fractures serve as important storage spaces and primary flow channels in hydrocarbon reservoirs. However, fracture identification using conventional well logs remains challenging due to complex logging responses and the limited number of fracture samples. Logging responses exhibit two important characteristics: different logging variables show complex nonlinear relationships, while fracture-related anomalies tend to vary continuously within local depth intervals. Motivated by these characteristics, an intelligent fracture prediction method based on a hybrid iTransformer-LSTM network combined with Gaussian Mixture Model (GMM)-based sample augmentation is proposed. The iTransformer module is used to capture inter-feature dependencies among different logging variables, while the LSTM module further models sequential variations along the depth direction. In addition, GMM-based sample augmentation is applied to the training fracture samples to learn their multivariate distribution and generate statistically consistent synthetic samples, thereby alleviating the influence of limited fracture samples. The proposed method was evaluated on two wells from the C7 target interval. On the WELL1 test set, it achieved an accuracy of 0.714±0.038, a Macro-F1 score of 0.708±0.093, and an AUC of 0.816±0.042. On the independent WELL2 blind well, it achieved an accuracy of 0.816±0.093, a Macro-F1 score of 0.502±0.034, an AUC of 0.734±0.016, and a fracture recall of 0.813±0.163. These results suggest potential for fracture identification under highly imbalanced conditions.