Intelligent fracture identification and its geological significance in tight sandstone reservoirs.

Abstract: Natural fractures in tight sandstone reservoirs play an important role in hydrocarbon migration and accumulation. Fracture identification remains challenging due to the scarcity of labeled data and the complex logging responses of fractures. To address these problems, we propose a novel hybrid deep learning framework (CNN-Attention-BiLSTM). First, labeled fracture classification based on Full waveform sonic logs characteristics is employed to screen unlabeled data, replacing sampling algorithms for data balancing. This approach provides more fracture labels that align with authentic geological information. Subsequently, one-dimensional convolution is applied to construct multi-dimensional fracture logging response patterns that characterize fracture development. A Channel Self-Attention is introduced to assign optimal weights to response patterns across different dimensions, achieving an optimized pattern combination. A double-layer BiLSTM is then utilized to mitigate the impact of sedimentary cycles on logging identification, while capturing both short- and long-term dependencies of fracture responses across different network layers. The identification method is applied to the H1 member of the Lower Shihezi Formation in the Hangjinqi area, China. The test set accuracy is higher than 90%, and blind wells verification demonstrates an improvement of over 8% in accuracy compared to conventional methods. The identification results reveal that fractures are the most developed in H1-2, followed by H1-1 and H1-3, while H1-4 is the least developed layer. The fracture distribution pattern is evidently controlled by sedimentary rhythms, with fracture density decreasing in the order: interbedded sandstone and mudstone layers, thick sandstone and thick mudstone, thick mudstone and poorly developed sandstones. This trend is primarily attributed to the thickness of mechanical stratigraphy. Under equivalent tectonic stress conditions, thin sandstone layers are more prone to fracturing due to stress concentration, resulting in higher fracture density. Additionally, the proposed method deepens the correlation between the log response types of fractures and their development degree. It clarifies that fractures occur in varied patterns across different regions. In sandy conglomerates and gravel coarse sandstone intervals with high porosity and permeability, fractures tend to occur as single or multiple parallel fractures and are relatively less developed. fractures are more prevalent in the overlying and underlying intervals. Conversely, in tight sandstone intervals with poor porosity and permeability, the rock is more brittle, leading to the development of dense, interconnected fracture networks. And gas distribution shows correlate strongly with fracture-developed intervals. Therefore, it can be inferred that in intervals with high-quality sandstone reservoirs in the study area, fractures likely serve as vertical conduits connecting upper and lower gas-bearing zones, acting as preferential migration pathways. In contrast, within tight sandstone intervals, fractures primarily enhance matrix reservoir quality, thereby facilitating gas migration and accumulation. The intelligent fracture identification method proposed in this study can provide guidance for the migration, accumulation and efficient development of tight sandstone gas, Further, it can also offer a basis for the later carbon dioxide storage and the construction of underground gas storage of tight sandstone.

Keywords: Fracture identification; Tight reservoirs; Full waveform sonic logs; Conventional logs; Deep learning