基于PCA蚂蚁追踪属性的复杂断裂检测技术及应用OA

The fault system of Bohai Oilfield A is highly complex.Constrained by the limited quality of seismic data and the interference of shallow gas cloud zones,conventional seismic attributes(e.g.,coherence and curvature)suffer from low signal-to-noise ratio,poor lateral continuity of fault interpretations and insufficient resolution.These limitations not only hinder the accurate fine-scale characterization of faults,but also restrict the potential for the efficient development of remaining oil reserves.To address these challenges and improve fault identification accuracy,a structure-oriented filtering algorithm was applied to suppress random noise,enhance the reflection continuity of non-fault intervals,and highlight the boundary features of faults,thereby boosting the fault recognition capability of the seismic dataset.On this basis,principal component analysis was employed to integrate multi-attribute fault detection information,yielding a comprehensive fused attribute for fault interpretation.Subsequent ant-tracking processing of this fused attribute generated a high-resolution fault detection data volume.Field application demonstrates that this integrated method effectively improves the precision of fault identification,overcomes the drawbacks of low signal-to-noise ratio and poor lateral continuity associated with conventional coherence and curvature techniques,and significantly enhances the fault-related information within gas cloud zones.This approach provides a technical support for subsequent well pattern optimization and the safe,efficient development of remaining oil in the oilfield.