The Buah formation has gained increasing interest in the search for new exploration plays in the Sultanate of Oman. Significant hydrocarbon accumulation has been discovered in the Buah recently. The Buah formation is complex in terms of lithology, pore structure, and the coexistence of oils with different gravities, including bitumen. The existence of bitumen makes it difficult to determine permeable and recoverable hydrocarbon intervals. The rock characteristics of Buah formation impose significant challenges to traditional formation evaluation methods. Because of the low porosity, error tolerance for estimation is low. Saturation determination using resistivity logs is uncertain because of low porosity and inexact Archie parameters. These include the cementation factor (m) that accounts for water phase connectivity, and the saturation exponent parameter (n), which is sensitive to rock wettability. The wettability status is unknown. However, the existence of bitumen could greatly influence the wettability and thus the saturation exponent. A formation evaluation workflow has been developed to provide accurate petrophysical parameters necessary to estimate stock-tank oil initially in place (STOIIP) and conduct static and dynamic modeling. The triple combo and elemental capture spectroscopy logs are first combined for the best total porosity estimate and lithology determination. Nuclear magnetic resonance provides a lithology-independent pore space hydrogen index, which improves the accuracy of the porosity estimate and also quantifies the bitumen-filled pore volume. Dielectric dispersion analysis provides water-filled porosity together with the water tortuosity exponent (mn) that is strongly related to the cementation factor that can be incorporated in resistivity analysis to obtain a better estimate of water saturation in uninvaded zone. Thus, the integration between nuclear logs and dielectric measurements enables the direct estimate of producible hydrocarbon. Conventional core analysis was used to categorize different rock types in this reservoir by using the reservoir quality index (RQI) approach. The results of our analyses have improved the static model, and examination against production logging and the dynamic model has revealed the best contributing rock types, the importance of fractures, and the impact of bitumen in hindering production.