Low-Order Faults Recognition Technology Based on Dominant Azimuth RGB Fusion

低序级断层的识别对于发现剩余油有利富集区、提高油气采收率具有重要的研究意义，渤南地区北部断裂系统发育，常规叠后资料上低序级断层的反射特征不明显，识别精度低，制约着该区域的勘探开发。针对以上问题，文章基于五维地震数据，划分优势偏移距和方位扇区，结合现有地质认识，优选断层法向方位，并通过RGB属性融合技术，有效提高了低序级断层的识别精度，明晰了断层展布特征，为研究区的构造精细描述和滚动开发提供了技术支撑。The identification of low-order faults is of significant research importance for discovering favorable accumulation areas of remaining oil and improving oil and gas recovery rates. In the northern part of the Bonan region, a fault system is well developed, but the reflection characteristics of low-order faults in conventional post-stack data are not obvious, resulting in low identification accuracy, which restricts exploration and development in this area. To address these issues, this paper focuses on five-dimensional seismic data, categorizes dominant offset distances and azimuth sectors, and combines existing geological knowledge to optimize the fault-normal azimuth. By employing RGB attribute fusion technology, the identification accuracy of low-order faults has been effectively improved, clarifying the fault distribution characteristics and providing technical support for the fine structural description and rolling development of the research area.