Shear wave velocity is an important feature in the seismic exploration that could be utilized in reservoir development strategy and characterization. Its vital applications in petrophysics, seismic, and geomechanics to predict rock elastic and inelastic properties are essential elements of good stability and fracturing orientation, identification of matrix mineral and gas-bearing formations. However, the shear wave velocity that is usually obtained from core analysis which is an expensive and time-consuming process and dipole sonic imager tool is not commonly available in all wells. In this study, a statistical method is presented to predict shear wave velocity from wireline log data. The model concentrated to predict shear wave velocity fr

      The current study includes preparing a geometric proposal of the main parameters that must be worked within a seismic reflection survey to prepare a three-dimensional subsurface image. This image represents the Siba oil field located in Basra, southern Iraq. The results were based on two options for selecting these approved elements to create a three-dimensional image of the Mishrif, Zubair and Yamama formations as well as the Jurassic and Permian Khuff and the pre-Khuff reservoir area. The first option is represented in the geometry in option -1 is 12 lines, 6 shots, and 216 chs. The receiver density is 66.67 receivers / km², so the shot density is the same. Total shots are 21000, which is the same number of receiv

This study deals with the application of surface-consistent  deconvolution  to the two-dimensional seismic data applied to the Block 11 area within the administrative boundaries of Najaf and Muthanna Governorates with an area of ​​4822 , the processed seismic data of line (7Gn 21) is 54 km long. The study was conducted within the Processing Department of the Oil Exploration Company. The gap surface- consistent deconvolution was applied using best results of the  parameters  applied  were: The length of the operator 240, the gap operator 24, the white noise 0.01%, the seismic sections of this type showed improvement with the decay of the existing complications and thus give a good continuity of the reflectors

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

This study aim to  identify the concept of  web based information systems since its one of the important topics that is  usually omitted by our organizations, in addition to,  designing a web based information system in order to manage the customers data of Al- Rasheed bank, as a unified information system that is specialized to the banking deals of the customers with the bank, and providing a suggested model to apply the virtual private network as a tool that is to protect the transmitted data through the web based information system.

This study is considered important because it deals with one of the vital topics nowadays, namely: how to make it possible to use a distributed informat

Missing data is one of the problems that may occur in regression models. This problem is usually handled by deletion mechanism available in statistical software. This method reduces statistical inference values because deletion affects sample size. In this paper, Expectation Maximization algorithm (EM), Multicycle-Expectation-Conditional Maximization algorithm (MC-ECM), Expectation-Conditional Maximization Either (ECME), and Recurrent Neural Networks (RNN) are used to estimate multiple regression models when explanatory variables have some missing values. Experimental dataset were generated using Visual Basic programming language with missing values of explanatory variables according to a missing mechanism at random general pattern and s

     Serious gases have been highly related to being prejudiced against human life within the environment. The evolution of a trustworthy gas sensor with an elevated response is of major importance for detecting various hazardous gases. Titanium dioxide (TiO₂) nanotubes (TNTs) are favorable candidates with considerable potential and stellar performance in gas sensor applications. In this work, we have studied the effect of voltage on preparing TiO₂ nanotubular arrays via the anodization technique for gas sensor applications. A simple electrochemical anodization approach was used to synthesize titanium dioxide nanotubes. Diverse techniques of characterization were used to evaluate TNTs. The results gained from fi