Multilayer reservoirs are currently modeled as a single zone system by averaging the reservoir parameters associated with each reservoir zone. However, this type of modeling is rarely accurate because a single zone system does not account for the fact that each zone's pressure decreases independently. Pressure drop for each zone has an effect on the total output and would result in inter-flow and the premature depletion of one of the zones. Understanding reservoir performance requires a precise estimation of each layer's permeability and skin factor. The Multilayer Transient Analysis is a well-testing technique designed to determine formation properties in more than one layer, and its effectiveness over the past two decades has been

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an

Conversation analysis has long been the concern of many linguists who work in the field of discourse analysis. In spite of the fact that there are many researches have been done in the field of short stories but up to the researcher knowledge the investigation of the selected short stories has not been studied yet. Hence, this paper aims at answering the following questions: what are the features of children’s short stories language and the differences between short stories of four years old and those of six years old.  Hence, the devices used by the story tellers in reciting the short stories should be observed. Thus, the researcher has consulted the models presented by Johnson and Fillmore (2010) to show tenses and sentence str

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

The Iraqi market for securities in light of financial globalization faces real challenges at the local and international levels, which were reflected in their shadows on the overall economic reality, which imposed the necessity of making fundamental changes in terms of form and content, and from here stems the research problem in the ability of the Iraqi stock market to adapt to the transformations Financial imposed by financial globalization in light of the weakness of the economic structure and its position in the global economy. The research starts from the hypothesis that the Iraqi market for securities in light of financial globalization has an important and significant role in the economic field, through its role in stimula

In this work, metal oxides nanostructures, mainly, copper oxide (CuO), nickel oxide (NiO), titanium dioxide (TiO2), and multilayer structure were synthesized by dc reactive magnetron sputtering technique. The structural purity and nanoparticle size of the prepared nanostructures were determined. The individual metal oxide samples (CuO, NiO and TiO2) showed high structural purity and minimum particle sizes of 34, 44, 61 nm, respectively. As well, the multilayer structure showed high structural purity as no elements or compounds other than the three oxides were founds in the final sample while the minimum particle size was 18 nm. This reduction in nanoparticle size can be considered as an advantage for the dc reactive magnetron sputtering tec

The study using Nonparametric methods for roubust to estimate a location and scatter it is depending  minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .       

It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob