The Zubair Formation is one of the major reservoirs of high production in the Rumaila oilfield, southern Iraq. The petrophysical properties analysis of the Upper Sand Member (Main Pay) of the Zubair Formation was conducted. The study includes results analysis of four wells distributed along the South Rumaila oilfield. Using a set of open well-logs, the main pay was divided into three main pay (AB, DJ and LN) units separated by two insulating shale units (C and K). The unit DJ was subdivided into three secondary reservoir units: D, F, H and the LN unit, which is split into L, M, and N. The research also includes the statistical analysis of the petrophysical properties, the calculation of the heterogeneity of the reservoir, and th

Petrophysical properties evaluation from well log analysis has always been crucial for the identification and assessment of hydrocarbon bearing zones. East Baghdad field is located 10 km east of Baghdad city, where the southern area includes the two southern portions of the field, Khasib formation is the main reservoir of East Baghdad oil field.

In this paper, well log data of nine wells have been environmentally corrected, where the corrected data used to determine lithology, shale volume, porosity, and water saturation. Lithology identified by two methods; neutron-density and M-N matrix plots, while the shale volume estimated by single shale indicator and dual shale indicator, The porosity is calculated from the three common po

Rationale, aims and objectives: A review of studies published over the last six years gives update about this hot topic. In the middle of COVID-19 pandemic, this study findings can help understand how population may perceive vaccinations. The objectives of this study were to review the literature covering the perceptions about influenza vaccines and to determine factors influencing the acceptance of vaccination using Health Belief Model (HBM). Methods: A comprehensive literature search was performed utilizing PubMed and Google Scholar databases. Three keywords were used: Influenza vaccine, perceptions, and Middle East. Empirical studies that dealt with people/ HCW perceptions of influenza vaccine in the Middle East and writt

     The aims of visible watermarking are to prevent the illegal copying of videos and images and to avoid the theft and unauthorized advertisement of the legitimate owners' content. The visible watermark is distinguishable with the naked eye. This makes it easily removed by an attacker. This paper proposes an anti-removable visible logo by double embedding using the discrete wavelet transform (DWT) and a chaotic map. The main purpose of this method is to prevent the removal attack and restore the original video without information loss after the logo is legally removed. DWT is used for both the cover area and the logo to embed sub-bands of information from the cover area into sub-bands of the logo. The main idea of using a chaotic m

Video copyright protection is the most generally acknowledged method of preventing data piracy. This paper proposes a blind video copyright protection technique based on the Fast Walsh Hadamard Transform (FWHT), Discrete Wavelet Transform (DWT), and Arnold Map. The proposed method chooses only frames with maximum and minimum energy features to host the watermark. It also exploits the advantages of both the fast Walsh Hadamard transform (FWHT) and discrete wavelet transforms (DWT) for watermark embedding. The Arnold map encrypts watermarks before the embedding process and decrypts watermarks after extraction. The results show that the proposed method can achieve a fast embedding time, good transparency, and robustness against various

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

Support Vector Machine (SVM) is supervised machine learning technique which has become a popular technique for e-mail classifiers because its performance improves the accuracy of classification. The proposed method combines gain ratio (GR) which is feature selection method with one-class training SVM to increase the efficiency of the detection process and decrease the cost. The results show high accuracy up to 100% and less error rate with less number of feature to 5 features.