Application of Seismic Attribute Clustering Method Based on PCA-SOM in Identification of Paleochannels

Abstract Paleochannels are crucial reservoir types in continental oil and gas fields. The P oil layer in the Sanzhao Depression of the Songliao Basin is a typical example of river-controlled delta-phase deposition. The underwater diversion channel is highly developed and serves as the primary unit of the oil and gas reservoir. The stacking of the river channel creates a complex relationship. Identifying river channels has always been a challenging aspect of predicting sweet spot reservoirs due to the thinness of the sand, fault cutting, seismic acquisition and processing noise, low-resolution constraints, and other factors. This paper proposes a method for identifying river channels by combining principal component analysis (PCA) and self-organised mapping network (SOM) with seismic attribute clustering. The aim is to address the issue of difficult identification of paleochannels. The seismic attribute set, which contains multiple types of attributes, is first dimensionality reduced using principal component analysis. Then, a few principal components that can characterise the attribute set are extracted and used as inputs to the self-organising map (SOM). This reduces the dimensionality of the training sample set and simplifies the network structure. The self-organising neural network structure is designed based on the input sample features. Training is then carried out, and the network weights are updated. Attribute clustering is performed to obtain the river identification results. The effectiveness of this method for paleochannel recognition is verified by the clear morphology of the recognised paleochannels in the Songliao Basin, their conformity to geological understanding, and their matching with drilling information.