基于改进的U-Net网络的河道砂岩自动识别方法OA

Tight channel sands represent a significant reservoir type with high potential for hydrocarbon accumulation in continental basins.However,conventional methods often fall short in accurately characterizing the 3D distribution of these channel sands due to their multi-phase development,complex stacking relationships,and rapid lateral variations.To overcome this challenge,this study proposes an automated channel identification method based on an improved U-Net deep learning network.Guided by seismic sedimentology,the first step involves applying Wheeler transformation to the time-domain seismic data to incorporate sedimentary cycle characteristics,which facilitates the identification of sandstone stacking relationships and yields high-quality training samples.This is followed by the integration of a cascaded dilated convolution module and a spatial attention mechanism into the U-Net architecture.This integration strengthens the network's capacity to extract multi-scale features and thus improves the delineation of narrow,thin,and superimposed channel boundaries.Finally,data augmentation methods tailored to channel characteristics are employed to automatically generate a large number of training samples for model training and testing.Field application results demonstrate that the improved U-Net significantly enhances the accuracy of boundary identification for multi-phase superimposed channels and achieves 3D characterization of single-phase channel systems.This approach offers reliable technical support for the evaluation of tight channel sandstone reservoirs and the optimization of exploration strategies.