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 grids and used in the training and testing of the used network. A comparison between the calculated and observed cumulative oil production has been carried out through the testing steps of the constructed ANN, an absolute average percentage error of the used network was reached to 4.044%, and this is consider to be an acceptable limit within engineering applications, in addition to that, a good behavior was reached with (FFNNW) and suitable re-entry wells location were identified according to the reservoir configuration (pressure and saturation distribution) output from SRF simulation model at the end of 2005.
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Reishi Mushroom, Ganoderma, is considered one of important wood-decaying medicinal mushrooms. This study aimed to identify three samples of this genus in Mosul city in February and April 2019. Three species of Ganoderma were collected from three various trees including Eucalyptus, Morus, and Olea (olive) in Mosul City, Northern Iraq. Their identifications and their DNA sequences were genetically identified by using PCR techniques according to detect nuclear ribosomal internal transcribed spacer (ITS) regions. Results exhibited the finding of Ganoderma resinaceum, Ganoderma applanatum, and Ganoderma sp. This study is first attempt to identify Reishi Mushroom by molecular methods in Iraq. Thus, the current study is considered new good d
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This research examines the quantitative analysis to assess the efficiency of the transport network in Sadr City, where the study area suffers from a large traffic movement for the variability of traffic flow and intensity at peak hours as a result of inside traffic and outside of it, especially in the neighborhoods of population with economic concentration. &n
... Show MoreThe Aaliji Formation in wells (BH.52, BH.90, BH.138, and BH.188) in Bai Hassan Oil Field in Low Folded Zone northern Iraq has been studied to recognize the palaeoenvironment and sequence stratigraphic development. The formation is bounded unconformably with the underlain Shiranish Formation and the overlain Jaddala Formation. The microfacies analysis and the nature of accumulation of both planktonic and benthonic foraminifera indicate the two microfacies associations; where the first one represents deep shelf environment, which is responsible for the deposition of the Planktonic Foraminiferal Lime Wackestone Microfacies and Planktonic Foraminiferal Lime Packstone Microfacies, while the second association represents the deep-sea environme
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Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed
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Knowing the distribution of the mechanical rock properties and the far field stresses for the field of interest is an important task for many applications concerning reservoir geomechanics, including wellbore instability analysis, hydraulic fracturing, sand production, reservoir compaction, and subsidence. A major challenge with determining the rock's mechanical properties is that they cannot be directly measured at the borehole. Furthermore, the recovered carbonate core samples for performing measurements are limited and they provide discrete data for specific depths.
The purpose of this study is to build 2D and 3D geomechanical models of the Khasib reservoir in the East Baghdad oil field/ Central area. TECHLOG.2015.3 softwa
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The Cassia glauca Lam. is the tree that belongs to the Fabaceae family and is native to India has many uses in indigenous systems of medicine, folk medicine, and traditional Brazilian medicine. Has many pharmacological activities such as anti-diabetic, antibacterial, antifungal, antioxidant, anti-hemolytic, anticancer, cardio-protective, and Hepato-protection. The aim of study is to Isolation, identification, and quantification of some compounds from aerial parts of Cassia glauca since no phytochemical investigation had previously been done in Iraq for this plant. The aerial parts were defatted in n. hexane for 48 hours. The defatted materials were extracted in 85% ethanol using the hot method (soxhlet), then the extract was fra
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Aspergillus fumigatus considered to be the most important species to cause respiratory infection cases in both humans and animals especially in cats in the last decades. In this study, we focused on the isolation and identification of Aspergillus fumigates by collecting 40 samples in deferent veterinary clinics and stray cats in Baghdad city, during the period (October 2021 to January 2022), all samples were cultured on Sabouraud dextrose agar and malt extract agar. The isolates identified by the laboratory methods, it’s depend on macroscopic and microscopic appearance. The results showed that (40) swaps taken from the pharynx of infected cats, included: Aspergillus fumigatus 16 (40%), Aspergillus spp. 7 (17.5%), Aspergillus niger
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