The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in perme

The work presented in this paper is a case study of the southeastern Iraqi Fauqi Oil Field to evaluate both the petrophysical properties and lithology of the Jeribe–Euphrates Formations. Reservoir characterization was performed using well log data, including Gamma Ray (GR), Density (RHOB), Neutron (NPHI), Sonic (DT), Spontaneous Potential (SP), and resistivity logs. Data processing, correction, and interpretation were conducted using Interactive Petrophysics (IP) software. The Gamma Ray method was used to estimate shale volume (Vsh), while porosity was calculated using various methods, with the neutron–density combination providing reliable results. To determine water saturation (Sw), the Archie equation was applied using common

   Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr

The petrophysical analysis is very important to understand the factors controlling the reservoir quality and production wells. In the current study, the petrophysical evaluation was accomplished to hydrocarbon assessment based on well log data of four wells of Early Cretaceous carbonate reservoir Yamama Formation in Abu-Amood oil field in the southern part of Iraq. The available well logs such as sonic, density, neutron, gamma ray, SP, and resistivity logs for wells AAm-1, AAm-2, AAm-3, and AAm-5 were used to delineate the reservoir characteristics of the Yamama Formation. Lithologic and mineralogic studies were performed using porosity logs combination cross plots such as density vs. neutron cross plot and M-N mineralogy plot. Thes

well log analysis is used to determine the rock properties like porosity, water saturation, and shale volume. Archie parameters in Archie equation, which sometimes considered constants greatly affect the determination of water saturation, also these parameters may be used to indicate whether the rocks are fractured or not so they should be determined. This research involves well logging analysis for Zubair formation in Luhais field which involves the determination of Archie parameters instead of using them as constant.

The log interpretation proved that the formation is hydrocarbon reservoir, as it could be concluded from Rwa (high values) and water saturation values (low values), the lithology of Zubair from cro

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

Permeability determination in Carbonate reservoir is a complex problem, due to their capability to be tight and heterogeneous, also core samples are usually only available for few wells therefore predicting permeability with low cost and reliable accuracy is an important issue, for this reason permeability predictive models become very desirable.

   This paper will try to develop the permeability predictive model for one of  Iraqi carbonate reservoir from core and well log data using the principle of Hydraulic Flow Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good parameter to determine (HFUs).

   Histogram analysis, probability analysis and Log-Log plot of Reservoir Qua

The lower Cretaceous sandstones of Zubair and Nahr Umr formations are the main producing reservoirs in Subba oilfield in southern Iraq. Key differences in their petrophysical and depositional attributes exist affecting their reservoir characteristics. The evaluation of well logs and core porosity-permeability data show better reservoir properties in Nahr Formation. The Litho-saturation logs indicate greater thickness of oil-saturated reservoir units for Nahr Unr Formation associated with lower values of shale volume, and higher values of effective porosity. In addition, higher values of permeability for Nahr Umr Formation is suggested by applying porosity-irreducible water saturation cross plot. The reducing reservoir quality of Zub