Carbonate reservoirs are increasingly becoming an important resource for hydrocarbon production because they contain the majority of remaining proven oil and gas reserves. In this context, carbonate reservoirs could represent new opportunities; however, there is still a lack of understanding of their subsurface status and characterization. Carbonate reservoirs are more difficult to evaluate than their siliciclastic counterparts because many aspects of carbonate rocks make their seismic image signature complex and difficult to interpret. Moreover, the presence of complex overburden such as shallow gas accumulation can exacerbate amplitude and phase fidelity at the reservoir, which introduces an additional imaging challenge. This makes field development of carbonate reservoirs extremely difficult because field development requires detailed delineation of characteristic karst features to avoid drilling hazards and sudden water breakthrough. In this paper, we demonstrate that a tight integration of signal processing, depth model building, and imaging, as well as near-real-time seismic interpretation feedback, is the key to success for imaging complex carbonate reservoirs with overburden challenges. Our findings show that such an integrated approach can result in a substantially better image, reduced depth uncertainty, and better delineation of karst and fractures. It can also aid in well placement and improve reservoir property modeling.