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The current work is focused on the rock typing and flow unit classification for reservoir characterization in carbonate reservoir, a Yamama Reservoir in south of Iraq (Ratawi Field) has been selected, and the study is depending on the logs and cores data from five wells which penetrate Yamama formation. Yamama Reservoir was divided into twenty flow units and rock types, depending on the Microfacies and Electrofacies Character, the well logs pattern, Porosity-Water saturation relationship, flow zone indicator (FZI) method, capillary pressure analysis, and Porosity-Permeability relationship (R35) and cluster analysis method. Four rock types and groups have been identified in the Yamama formation depending on the FZI method, where the first group represents the bad reservoir quality (FZI-1) (Mudstone Microfacies and Foraminiferal wackestone Microfacies), the second group reflects a moderate quality of reservoir (FZI-2) (Algal wackestone-Packstone Microfacies and Bioclastic wackestone-Packstone Microfacies), the third group represents good reservoir quality (FZI-3) (Peloidal Packstone-Grainstone Microfacies), and the fourth group represents a very good reservoir quality (FZI-4) (Peloidal-oolitic Grainstone Microfacies). Capillary pressure curves and cluster analysis methods show four different rock types: a very good quality of reservoir and porous (Mega port type) (FZI-4) (Peloidal-oolitic Grainstone Microfacies) with a low irreducible Water saturation (Swi), good quality of reservoir and porous (Macro port type) (FZI-3) (Peloidal Packstone-Grainstone Microfacies), moderate quality of reservoir (Meso port type) (FZI-2) (Algal wackestone-Packstone Microfacies and Bioclastic wackestone-Packstone Microfacies), and a very fine-grained with bad reservoir quality (Micro port type) (FZI-1) (Mudstone Microfacies and Foraminiferal wackestone Microfacies) and with the higher displacement of pressure). These capillary pressure curves support the subdivision of the main reservoir unit to flow units.

Permeability is an essential parameter in reservoir characterization because it is determined hydrocarbon flow patterns and volume, for this reason, the need for accurate and inexpensive methods for predicting permeability is important. Predictive models of permeability become more attractive as a result.    A Mishrif reservoir in Iraq's southeast has been chosen, and the study is based on data from four wells that penetrate the Mishrif formation. This study discusses some methods for predicting permeability. The conventional method of developing a link between permeability and porosity is one of the strategies. The second technique uses flow units and a flow zone indicator (FZI) to predict the permeability of a rock mass using data from cores and well logs. The approach is used to predict the permeability of some uncored wells/intervals. The flow zone indicator is an efficient metric for calculating hydraulic flow units since it is based on the geological properties of the material and varied geometries pore of rock mass (HFU) and Artificial Neural Network (ANN) analysis is another way for predicting permeability. The result shows the FZI method, gave acceptable results compared with the obtained from core analysis than the other methods.

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Knowledge of permeability, which is the ability of rocks to transmit the fluid, is important for understanding the flow mechanisms in oil and gas reservoirs.Permeability is best measured in the laboratory on cored rock taken from the reservoir. Coring is expensive and time-consuming in comparison to the electronic survey techniques most commonly used to gain information about permeability.Yamama formation was chosen, to predict the permeability by using FZI method. Yamama Formation is the main lower cretaceous carbonate reservoir in southern of Iraq. This formation is made up mainly of limestone. Yamama formation was deposited on a gradually rising basin floor. The digenesis of Yamama sediments is very important due to its direct relation to the porosity and permeability.In this study permeability has been predicated by using the Flow zone indicator methods.This method attempts to identify the flow zone indicator in un-cored wells using log records. Once the flow zone indicator is calculated from the core data, a relationship between this FZI value and the well logs can be obtained.