This paper aims to calculate the petrophysical properties in the Al-Ahdab field in the middle of Iraq within the Mauddud Formation. This study was based on the information available from well logs. The interactive petrophysical software IP (V4.5)  was used to calculate the porosity, hydrocarbon saturation and shale volume, divide the formation into reservoir units and buffer units, and evaluate these units in each well. The Mauddud was divided into five units, two of them were considered good reservoirs having good petrophysical properties (high porosity, Low water saturation, and low shale volume). The other three are not reservoirs because of poor petrophysical properties.

This study deal with structural and stratigraphic analysis of the seismic reflection data for Hartha Formation at Nasiriyah field, the area of seismic data is about (1237) km2. Nasiriyah oil field is located in Dhi Qar Governorate, southern Iraq, and the oil field is located to the East of Euphrates River of about (38) km northwest of Nasiriyah city. which includes twenty-four (24) wells. In some wells there are oil evidences in Hartha Formation at Nasiriyah oil field, for this reason, Hartha Formation is studied.
Two reflectors are picked (top and bottom Hartha) they are defined by using synthetic seismograms in time domain for wells (Ns-1, and 3). Time and depth of Hartha Formation are drawn using velocity data of reflectors. The st

This paper contains studying of the Evaluation for the Petrophysical Properties of
Yamama Formation in Ratawi Field which occurs in about 70 km to the west of
Basrah city in Mesopotamia zone (Zubair subzone). The study includes a
petrophysical evaluation and (3 Dimensions) geological model for each unit
especially the three hydrocarbon units comprising the Yamama Formation in (5)
boreholes which are Rt-3, Rt-4, Rt-5, Rt-6 and Rt-7 distributed on the crest and
flanks of the Ratawi structure that are carried out in the present study. The
formation's boundaries were determined using well logs, available core intervals and
by Petrophysical data and it is found that it can be subdivided into three main
reservoir uni

The study intends to well logs interpretation to determine the petrophysical parameters of Euphrates Formations in Ajeel Oil Field. The petrophysical properties have been determined from well logging, Euphrates Formation in terms of reservoirs units, consist of two Petrophysical properties. Total porosity, effect porosity and secondary porosity have been calculated from neutron, density, and sonic logs. secondary porosity is high and it's resulted from diagenesis processes in the formation. From RHOB-NPHI and N/M cross plot, Euphrates Formation composed mainly from Limestone and dolomite with nodules of anhydrite. Dhiban Formation composed mainly of anhydrite, so it's represented the cap rocks for Euphrates Reservoir were recognized base

Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate

Flow unit and reservoir rock type identification in carbonates are difficult due to the intricacy of pore networks caused by facies changes and diagenetic processes. On the other hand, these classifications of rock type are necessary for understanding a reservoir and predicting its production performance in the face of any activity. The current study focuses on rock type and flow unit classification for the Mishrif reservoir in Iraq's southeast and the study is based on data from five wells that penetrate it. Integration of several methods was used to determine the flow unit based on well log interpretation and petrophysical properties. The flow units were identified using the Quality Index of Rock and the Indicator of Flow Zone. Th

      The Hartha Formation reservoir was evaluated to be produced in the near future in Y oil and J fields, West of the Tigris River in northern Iraq. Due to geological data analysis of the Hartha facies and diagenesis could enhance oil production with minimized risks, this research tried to deal with data to support the oil production. The Hartha reservoir is subdivided into five-rock units, Har-1, 2, 3, 4 and 5. It is of Heterogenic facies limestone, dolomite, dolomitic limestone and argillaceous dolomite. The Hartha Formation is a clean carbonate; it has an effective porosity. It consists of lime mudstone-packstone skeletal grain. This conclusion is based on the lithology, core microfacies, cutting, thin section and log interpret

This paper presents a comparative study between different oil production enhancement scenarios in the Saadi tight oil reservoir located in the Halfaya Iraqi oil field. The reservoir exhibits poor petrophysical characteristics, including medium pore size, low permeability (reaching zero in some areas), and high porosity of up to 25%. Previous stimulation techniques such as acid fracturing and matrix acidizing have yielded low oil production in this reservoir. Therefore, the feasibility of hydraulic fracturing stimulation and/or horizontal well drilling scenarios was assessed to increase the production rate. While horizontal drilling and hydraulic fracturing can improve well performance, they come with high costs, often accounting for up t

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F