Carbonate rocks are known for high level heterogeneity due to syn-depositional and post-depositional process. Developing rock type scheme for reservoir rocks involves considering various factors, including rock fabric, pore types, and pore throat size distribution, and it requires the integration of multi-scale data to ensure its reliability and predictability in assessing reservoir properties and performance. Comprehensive understanding of the complex variations in pore geometry, as influenced by lithofacies, depositional, and diagenetic controls, is crucial for effectively managing and exploiting hydrocarbon reservoirs. Core data analysis plays a significant role in achieving this understanding and can help define distinct zones within the reservoir, aiding in reservoir management and optimization of hydrocarbon production. Permeability prediction is indeed a key component of reservoir characterization and evaluation. This study is expected to comparison and define more detail between two permeability prediction methods there are multilinear regression (MLR) and hydraulic flow unit in bioclastic carbonate reservoir. To estimate the rock type permeability value, a hydraulic unit approach is used by flow zone indicator. Whereas, to estimate multilinear regression value used by dependent variable (core permeability) and independent variable (porosity and shale volume). Petrographically from 60 core plugs concludes that the lithofacies in the Formation A is predominantly foraminiferal grainstone – packstone lithofacies and the diagenetic environment is marine phreatic. The Formation B is dominated by skeletal wackestone – packstone and the diagenetic environment is shallow burial or mixing zone. Petrography is a crucial role in the validation and comprehensive understanding of sedimentary rocks and their diagenetic history. It provides detailed insights into the mineralogy, texture, and structures of these rocks.