Horizontal wells have revolutionized hydrocarbon production by enhancing recovery efficiency and reducing environmental impact. This paper presents an enhanced Black Oil Model simulator, written in Visual Basic, for three-dimensional two-phase (oil and water) flow through porous media. Unlike most existing tools, this simulator is customized for horizontal well modeling and calibrated using extensive historical data from the South Rumaila Oilfield, Iraq. The simulator first achieves a strong match with historical pressure data (1954–2004) using vertical wells, with an average deviation of less than 5% from observed pressures, and is then applied to forecast the performance of hypothetical horizontal wells (2008–2011). The result

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

Background: Neonatal Septicemia (NNS) is generalized microbial symptomatic infection during the first 28 days of life.It>s the most serious complication in Neonatal Intensive Care Units (NICU) that demand urgent diagnosis and accurate treatment.Objective: To reveal the relationship of neonatal septicemia with birth weight (one of the neonatal risk factors).Patients and Methods: Blood sample was obtained from 76 neonates aged 1 hour-28 days who were diagnosed clinically (poor feeding, respiratory distress, fever, hypothermia, gastrointestinal and/or central nervous system symptoms)and bacteriologically to have neonatal septicemia.Results:One of the most important neonatal factor predisposing to infection is low birth weight, signi

Degradation of soil quality is an inevitable consequence of modifications to the characteristics of the soil that contribute to a decrease in ecosystem services. Numerous stressors, including chemical, biological, and physical ones, as well as those originating from both natural and artificial sources. The most prevalent kind of soil contamination that contaminates soil biota is agrochemicals. Soil is the most common place for xenobiotic dumping, which makes it the most probable source of other natural resources' pollution, such as surface and ground waters, based on the results of several studies. The danger to the environment posed by polluted soils is influenced by a variety of biological and physicochemical mechanisms that regul

Degradation of soil quality is an inevitable consequence of modifications to the characteristics of the soil that contribute to a decrease in ecosystem services. Numerous stressors, including chemical, biological, and physical ones, as well as those originating from both natural and artificial sources. The most prevalent kind of soil contamination that contaminates soil biota is agrochemicals. Soil is the most common place for xenobiotic dumping, which makes it the most probable source of other natural resources' pollution, such as surface and ground waters, based on the results of several studies. The danger to the environment posed by polluted soils is influenced by a variety of biological and physicochemical mechanisms that regulate the

Unconfined Compressive Strength is considered the most important parameter of rock strength properties affecting the rock failure criteria.  Various research have developed rock strength for specific lithology to estimate high-accuracy value without a core.  Previous analyses did not account for the formation's numerous lithologies and interbedded layers. The main aim of the present study is to select the suitable correlation to predict the UCS for hole depth of formation without separating the lithology. Furthermore, the second aim is to detect an adequate input parameter among set wireline to determine the UCS by using data of three wells along ten formations (Tanuma, Khasib, Mishrif, Rumaila, Ahmady, Maudud, Nahr Um