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 critical parameter that defines the porosity-permeability relationships of each hydraulic flow unit. Additionally, the flow zone indicator method proves valuable in estimating permeability accurately. In this study, we demonstrate the application of the flow zone indicator method to determine hydraulic flow units within the Khasib formation. By analyzing core data and calculating the Rock Quality Index (RQI) and Flow Zone Indicator (∅Z), we differentiate the formation into four hydraulic flow units based on FZI values. Specifically, HFU 1 represents a rock of poor quality, corresponding to compact and chalky limestone. HFU 2 represents intermediate quality, corresponding to argillaceous limestone, while HFU 3 represents good quality, corresponding to porous limestone. Lastly, HFU 4 signifies an excellent reservoir rock quality characterized by vuggy limestone. By establishing a permeability equation that correlates with effective porosity for each rock type, we successfully estimate permeability. Comparing these estimated permeability values with core permeability reveals a strong agreement with a high correlation coefficient of 0.96%. Consequently, the flow zone indicator method effectively classifies the Khasib formation into four distinct hydraulic flow units and provides an accurate and reliable means of determining permeability in the reservoir. The resulting permeability equations can be applied to wells and depth intervals lacking core measurements, further emphasizing the practical utility of the FZI method.
Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of select
... Show MoreGypseous soils are spread in several regions in the world including Iraq, where it covers more than 28.6% [1] of the surface region of the country. This soil, with high gypsum content causes different problems in construction and strategic projects. As a result of water flow through the soil mass, permeability and chemical arrangement of these soils vary over time due to the solubility and leaching of gypsum. In this study the soil of 36% gypsum content, is taken from one location about 100 km (62 mi) southwest of Baghdad, where the sample is taken from depth (0.5 - 1) m below the natural ground surface and mixed with (3%, 6%, 9%) of Copolymer and Styrene-butadiene Rubber to improve t
Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.
The current study examined the impact of using PowerPoint presentation on EFL student’s attendance, achievement and engagement. To achieve the aim of this study, three null hypotheses have been posed as follows: There is no statistically significant difference between the mean score of the experimental group attendance and that of the control one; there is no statistically significant difference between the mean score of the experimental group achievement and that of the control one, and there is no statistically significant difference between the mean score of the experimental group engagement and that of the control one. To verify a hypothesis, a sample of sixty students is chosen randomly from the third year, department of English,
... Show MoreThe large number of failure in electrical power plant leads to the sudden stopping of work. In some cases, the necessary reserve materials are not available for maintenance which leads to interrupt of power generation in the electrical power plant unit. The present study, deals with the determination of availability aspects of generator in unit 5 of Al-Dourra electric power plant. In order to evaluate this generator's availability performance, a wide range of studies have been conducted to gather accurate information at the level of detail considered suitable to achieve the availability analysis aim. The Weibull Distribution is used to perform the reliability analysis via Minitab 17, and Artificial Neural Networks (ANNs) by approaching o
... Show MoreThe deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv
... Show MoreIn this work, the elemental constituents of smoker and nonsmoker
teeth samples of human were analyzed by Laser induced breakdown
spectroscopy method (LIBS). Many elements have been detected in
the healthy teeth samples, the important once are Ca, P, Mg, Fe, Pb
and Na. Many differences were found between (female and male)
teeth in Ca, P, Mg, Na and Pb contents. The concentrations of most
toxic elements were found significantly in the smoker group. The
maximum concentrations of toxic elements such as Pb, Cd and Co
were found in older male age above 60 year. Also, it was found that
the minimum concentrations of trace elements such as Ca, P and Na
exist in this age group. From these results it is clear that the