Constructing a fine 3D geomodel for complex giant reservoir is a crucial task for hydrocarbon volume assessment and guiding for optimal development. The case under study is Mishrif reservoir of Halfaya oil field, which is an Iraqi giant carbonate reservoir. Mishrif mainly consists of limestone rocks which belong to Late Cenomanian age. The average gross thickness of formation is about 400m. In this paper, a high-resolution 3D geological model has been built using Petrel software that can be utilized as input for dynamic simulation. The model is constructed based on geological, geophysical, pertophysical and engineering data from about 60 available wells to characterize the structural, stratigraphic, and properties distribution along the reservoir. Fourteen geological surfaces for all Mishrif units have been generated based on well tops data and top Mishrif structural map. The reservoir has been divided into 163 sublayers through the vertical direction and 160*383 grid cells in x-y direction with 9,988,640 total grid cells. A scale up process are performed for well log data, then, Sequential Gaussian Simulation algorithm are applied to fill 3D grid cells with properties values in areas away from wells. Pertophysical properties distribution for all reservoir zones are analyzed. The estimated initial oil in place of Mishrif through this model is close to that calculated in other previous studies.
Objectives: the study aims to assess nurses' practices toward chemotherapy-induced peripheral neuropathy (CIPN) for children at the hematology center, and to determine the effectiveness of the health education program on nurses' practices toward CIPN, and to find out the relationships between the effectiveness of Health education program and demographic characteristics of nurses.
Methodology: Use quasi-experimental design in the study (a design that divides the sample into two groups, a study group and a control group, with data collection in three stages). This study was conducted at a hematology center in Baghdad city for the period (from December 16th, 2019 to 8th May 202
... Show MoreWhile traditional energy sources such as oil, coal, and natural gas drive economic growth, they also seriously affect people’s health and the environment. Renewable energies (RE) are presently seen as an efficient choice for attaining long-term sustainability in development. They provide an adequate response to climate change and supply sufficient electricity. The current situation in Iraq results from a decades-long scarcity of reliable electricity, which has impacted various industries, including agriculture. There are diverse prospects for using renewable energy sources to address the present power crisis. The economic and environmental impacts of renewable energy systems were investigated in this study by using the solar pumpi
... Show MoreThis research provides a novel technique for using metal organic frameworks (HKUST-1) as a gas storage system for liquefied petroleum gas (LPG) in Iraqi vehicles to avoid the drawbacks of the currently employed method of LPG gas storage. A low-cost adsorbent called HKUST-1 was prepared and characterized in this research to investigate its ability for propane storage at different temperatures (25, 30, 35, and 40 oC) and pressures of (1-7) bar. HKUST-1 was made using a hydrothermal method and characterized using powder X-ray diffraction, BET surface area, scanning electron microscopic (SEM), and Fourier Transforms infrared spectroscopy (FTIR). The HKUST-1 was produced using a hydrothermal technique and possesses a high crys
... Show MoreChannel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T
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