Obtaining high-quality seismic imaging in shallow heterogeneous carbonate reservoirs with complicated structural regimes, such as the Issaran field, is difficult. Issaran field is a heavy oil shallow heterogeneous fractured carbonate reservoirs located in the Gulf of Suez of Egypt. It has many geological factors that affect image quality and pose numerous challenges. In addition, the seismic data was acquired more than 12 years ago, with narrow azimuth and short offsets. As a result, the fault zones are not sharply defined. Furthermore, the seismic data was processed about 10 years ago. The signal-to-noise ratio is relatively poor due to the random and coherent noises. A robust data conditioning workflow for noise suppression and fault discontinuity sharpening is used to improve the post-stack seismic data quality. In this context, a steering dataset was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter, and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Structural attributes were evaluated, to investigate how the newly applied data conditioning affects the clarity of fault patterns. Furthermore, multiple physical attributes were extracted, including instantaneous phase, instantaneous frequency, and RMS amplitude to better understand the reservoir stratigraphic heterogeneity. The application of DSMF is useful in removing the residual random noises. The FEF-similarity attribute revealed small-scale faults with a 50-foot vertical throw. The physical attributes proved that the Issaran carbonate facies is controlled by structure. Moreover, the RMS amplitude attribute helped in distinguishing between porous and non-porous dolomite facies. As imaging quality has significantly improved, the applied seismic data conditioning workflow is beneficial for the field development in Issaran field. It is also suggested that this data conditioning workflow be applied in other heterogeneous carbonate reservoirs with complex structures around the world.