Abstract Accurate estimation of permeability is essential in reservoir characterization and in determining fluid flow in porous media to optimize the production of a field. Some of the available permeability prediction techniques — e.g., Porosity-Permeability transforms and more recently artificial intelligence and neural networks — are encouraging but still show only moderate to good match to core data. This could be due to limitation to homogenous media while the knowledge about geology and heterogeneity is indirectly related or absent. The use of geological information from core descriptions, e.g., Lithofacies, which includes diagenetic information, show a link to permeability when categorized into rock types exposed to similar depositional environments. The objective of this paper is to develop a robust combined workflow integrating geology and petrophysics and wireline logs in an extremely heterogeneous carbonate reservoir to accurately predict permeability. Permeability prediction is carried out using pattern recognition algorithm called multi-resolution graph-based clustering (MRGC). We will bench mark the prediction results with hard data from core and well test analysis. As a result, we show how much better improvements are achieved in permeability prediction when geology is integrated within the analysis. Finally, we use the predicted permeability as an input parameter in J-function and correct for uncertainties in saturation calculation produced by wireline logs using the classical Archie equation. In conclusion, a high level of confidence in hydrocarbon volumes estimation is reached when robust permeability and saturation height functions are estimated, in conjunction with important geological details that are petrophysically meaningful.