Reservoir characterization is an important component of hydrocarbon exploration and production, which requires the integration of different disciplines for accurate subsurface modeling. This comprehensive research paper delves into the complex interplay of rock materials, rock formation techniques, and geological modeling techniques for improving reservoir quality. The research plays an important role dominated by petrophysical factors such as porosity, shale volume, water content, and permeability—as important indicators of reservoir properties, fluid behavior, and hydrocarbon potential. It examines various rock cataloging techniques, focusing on rock aggregation techniques and self-organizing maps (SOMs) to identify specific and

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

Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.

The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC

     Reservoir characterization requires reliable knowledge of certain fundamental properties of the reservoir. These properties can be defined or at least inferred by log measurements, including porosity, resistivity, volume of shale, lithology, water saturation, and permeability of oil or gas. The current research is an estimate of the reservoir characteristics of Mishrif Formation in Amara Oil Field, particularly well AM-1, in south eastern Iraq. Mishrif Formation (Cenomanin-Early Touronin) is considered as the prime reservoir in Amara Oil Field. The Formation is divided into three reservoir units (MA, MB, MC). The unit MB is divided into two secondary units (MB1, MB2) while the unit MC is also divided into two sec

Reservoir rock typing integrates geological, petrophysical, seismic, and reservoir data to identify zones with similar storage and flow capacities. Therefore, three different methods to determine the type of reservoir rocks in the Mushrif Formation of the Amara oil field. The first method represents cluster analysis, a statistical method that classifies data points based on effective porosity, clay volume, and sonic transient time from well logs or core samples. The second method is the electrical rock type, which classifies reservoir rocks based on electrical resistivity. The permeability of rock types varies due to differences in pore geometry, mineral composition, and fluid saturation. Resistivity data are usually obtained from w

Petrophysical properties of Mishrif Formation at Amara oil field is determined
from interpretation of open log data of (Am-1, 2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12
and13) wells. These properties include the total, the effected and the secondary
porosity, as well as the moveable and the residual oil saturation in the invaded and
uninvaded zones. According to petrophysical properties it is possible to divided
Mishrif Formation which has thickness of a proximately 400 m, into seven main
reservoir units (MA, MB11, MB12, MB13, MB21, MC1, MC2) . MA is divided into
four secondary reservoir units , MB11 is divided into five secondary reservoir units ,
MB12 is divided into two secondary reservoir units , MB13 is divided into