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Geological Model of the Tight Reservoir (Sadi Reservoir-Southern of Iraq)
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A3D geological model was constructed for Al-Sadi reservoir/ Halfaya Oil Field which is discovered in 1976 and located 35 km from Amara city, southern of Iraq towards the Iraqi/ Iranian borders.

Petrel 2014 was used to build the geological model. This model was created depending on the available information about the reservoir under study such as 2D seismic map, top and bottom of wells, geological data & well log analysis (CPI). However, the reservoir was sub-divided into 132x117x80 grid cells in the X, Y&Z directions respectively, in order to well represent the entire Al-Sadi reservoir.

Well log interpretation (CPI) and core data for the existing 6 wells were the basis of the petrophysical model (Porosity, Water saturation, & Permeability) that were distributed for all the created grids and then upscaled.

 

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Petroleum Science And Engineering
Depositional environment, seismic stratigraphy, and Sr-isotope geochronology, Bangestan reservoir, Ahwaz oilfield, SW Iran
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The Bangestan reservoir, which occurs in the Ahwaz oilfield, consists of the middle Cretaceous limestone Ilam and Sarvak Formations that were deposited in the Zagros Basin. The reservoir is divided into ten Zones (A to J) formed in the upper Albian-Santonian and contains considerable hydrocarbon accumulations. The limestones were deposited on an extensive shallow carbonate platform on a passive margin and are dominated by rudist biostrome and grainstone facies. Paleogeographical changes mean that identification of the facies is complex. Seismic stratigraphy and isotopic data are used to better understand the structural and geological setting and develop an understanding of the sedimentary environment. The results show that the rudist biostr

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Publication Date
Sun Jun 15 2025
Journal Name
Journal Of Engineering
Application of Mathematical Drilling Model on Southern Iraqi Oil Fields
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Publication Date
Wed Feb 01 2017
Journal Name
Heliyon
Environmental assessment of Al-Hammar Marsh, Southern Iraq
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Publication Date
Sun Sep 07 2014
Journal Name
Baghdad Science Journal
Composition of fish assemblage in the East Hammar marsh, southern Iraq
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The fish assemblage in the East Hammar marsh was studied during December 2009 to May 2010. The fish fauna of the marsh consisted of 17 native, 23 marine (49%) and seven alien species. The dominant species were Liza abu (14.6%), Carassius auratus (13.4%) and Thryssa mystax (11.2%). The resident species formed 44.7%, occasional species 36.1% and seasonal species 19.2% of the total number of fish species. Fish species diversity index ranged from 1.28 to 2.61, richness from 1.98 to 4.50 and evenness from 0.45 to 0.78. Salinity ranged from 1.45 to 7.74‰. The increase in the proportion of marine species (49%) in the fish assemblage due to marine waters progress from Arabian Gulf had an impact on the values of ecological indices and the

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Pollution threatens water quality in the Central Marshes of Southern Iraq
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Water pollution is an issue that can be exacerbated by drought as increased concentrations of unwanted substances are a consequence of lower water levels. Polluted water that flows into natural marshlands leads to the deposition of pollutants in the interior of the marsh. Here we present evidence that the interior of the Central Marsh (CM) in southern Iraq suffers from higher levels of pollution than areas closer to the source of water entering the marsh (the Euphrates River). A 1.7m embankment that halts the flow of the Euphrates is only infrequently breached and so the CM is effectively the terminal destination of the waters (and their associated pollutants and agricultural waste) flowing from the West of Iraq.

A range of water

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Publication Date
Fri Jul 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Determine the best model to predict the consumption of electric energy in the southern region
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Abstract:          

                Interest in the topic of prediction has increased in recent years and appeared modern methods such as Artificial Neural Networks models, if these methods are able to learn and adapt self with any model, and does not require assumptions on the nature of the time series. On the other hand, the methods currently used to predict the classic method such as Box-Jenkins may be difficult to diagnose chain and modeling because they assume strict conditions.

  

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Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Hydraulic Flow Units for Jeribe Reservoir in Jambour Oil Field Applying Flow Zone Indicator Method
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The Jeribe reservoir in the Jambour Oil Field is a complex and heterogeneous carbonate reservoir characterized by a wide range of permeability variations. Due to limited availability of core plugs in most wells, it becomes crucial to establish correlations between cored wells and apply them to uncored wells for predicting permeability. In recent years, the Flow Zone Indicator (FZI) approach has gained significant applicability for predicting hydraulic flow units (HFUs) and identifying rock types within the reservoir units.    This paper aims to develop a permeability model based on the principles of the Flow Zone Indicator. Analysis of core permeability versus core porosity plot and Reservoir Quality Index (RQI) - Normalized poros

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Publication Date
Tue Jun 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Application of Neural Network in the Identification of the Cumulative Production from AB unit in Main pays Reservoir of South Rumaila Oil Field.
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A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
Diagnosing Water Problem for Asmari Reservoir in Abu Ghirab Oilfield Using Analytical and Numerical Approaches
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Asmari is the main productive reservoir in Abu Ghirab oilfield in the south-east part of Iraq. It has history production extends from 1976 up to now with several close periods. Recently, the reservoir suffers some problems in production, which are abstracted as water production rising with oil production declining in most wells. The water problem type of the field and wells is identified by using Chan's diagnostic plots (water oil ratio (WOR) and derivative water oil ratio (WOR') against time). The analytical results show that water problem is caused by the channeling due to high permeability zones, high water saturation zones, and faults or fracturing. The numerical approach is also used to study the water movement inside the reser

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Publication Date
Tue Dec 15 2020
Journal Name
Aip Conference Proceedings
The water bodies in the Southern East of Iraq before and after 2018
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This study is concerned with the recent changes that occurred in the last three years (2017-2019) in the marshes region in southern Iraq as a result of the changes in the global climate, the study included all the water bodies in the five governorates that are located in the southern regions of Iraq (Wasit, Maysan, Dhi-Qar, Qadisiyah and Basrah), which represent the marshes lands in Iraq. Scenes of the Landsat 8 satellite are used to create a mosaic to cover the five governorates within a time window with the slightest difference between the date of the scene capture, not to exceed 8 days. The results of calculating the changes in water areas were obtained using the classifier support vector machine, where high accuracy ratios were recorded

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