Automated Fault Detection and Extraction under Gas Chimneys Using Hybrid Discontinuity Attributes

3D-seismic data have increasingly shifted seismic interpretation work from a horizons-based to volume-based focus over the past decade. The size of identification and mapping has therefore become difficult requires faster better tools. Faults, for instance, are one most significant features subsurface geology interpreted data. Detailed fault is very important in reservoir characterization modeling. conventional manual picking time-consuming inefficient process. It becomes more challenging error-prone when dealing with poor quality under gas chimneys. Several attributes available faults discontinuity detection applied varying degrees success. We present hybrid workflow that combines semblance-based likelihood attribute ant-tracking attribute. This innovative generates optimized volumes automatic extraction. optimization conditioning processes suppress random coherent noise first, then combination generated co-rendered enhance discontinuities. result volume razor sharp discontinuities which tracked extracted automatically. Contrary several tracking techniques use local continuity like coherency attributes, our method based on directed semblance, incorporates aspects Dave Hale’s superior fault-oriented semblance algorithm. methodology complex faulted interval chimneys Malaysian basin, yet results were promising. Despite quality, led detailed information major minor approach not only improved accuracy but also significantly reduced time associated uncertainty. equally helpful detecting any objects fracture, chimneys, stratigraphic features.