Pre-Conditioning of 3D Seismic Data Using the Structural Filters for Fault Interpretation

Complex geological structures and varying data quality present a unique challenge for seismic interpretation and pitfalls related to normal fault interpretation. Therefore, seismic conditioning and structural filters are necessary to improve faults and horizon interpretation and to obtain high-quality seismic attributes. This study applied structure-oriented conditioning with filters along 3D seismic data to reduce noise and increase seismic resolution. To achieve this enhancement in the seismic data, it is essential to differentiate between the dip and azimuth of the reflection signal and that of the noise. Two methods were applied in this study to get the dip and azimuth of the reflection signal: first, by applying structural smoothing on the seismic volume to get a smoothed version of the dip, and second, by applying structural attributes. The structural smoothing filter is used in this study to minimize noise, enhance structural imaging, and generally improve the volume for seismic interpretation and the application of other volume attributes. It is concluded that the seismic section with dip-guided variance attribute showed better results in enhancing fault edges. Larger values of this filter reduce noise effectively and smear the detected edges' sharpness. The results also demonstrate the effectiveness of variance attributes for edge detection of faults compared with dip-deviation and curvature attributes. Also, this study showed that for 3D seismic data, an anti-track filter for fault identification is the most effective filter to detect faults.