A Two-Stage Convolutional Neural Network for Interactive Channel Segmentation From 3-D Seismic Data

Fluvial facies exhibit higher porosity and permeability during sedimentation, making it crucial to study reservoir characteristics and assess the potential for oil and gas exploration. Traditional methods for identifying fluvial facies often rely on specific seismic attributes, which often require manual extraction of channel feature, especially when interpreting 3-D seismic data, which is inefficient. To improve the efficiency of channel interpretation in 3-D seismic data, we proposed a two-stage convolutional neural network to implement an interactive 3-D channel interpretation method. We generated 3-D seismic data with real channel structures and used their seismic attributes as inputs to the first stage network to automatically obtain initial and rough channel results. Then, based on this result, we added manual interaction to mark errors and combined the geodesic distance to transform the manual interaction information. Finally, we input the interaction information into the second-stage network to obtain high-quality identification results. Synthetic and field data examples demonstrated that this method retains good applicability even under complex geological conditions, providing valuable insights for the fields of oil and gas exploration and geological research.