The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.
In this study, a simulation model inside a channel of rectangular section with high of (0.16 m) containing two rectangular obstruction plates were aligned variable heights normal to the direction of flow, use six model of the obstructions height of (0.059, 0.066, 0.073, 0.08 and 0.087 m) were compared with the flow behavior of the same duct without obstructions. To predict the velocity profile, pressure distribution, pressure coefficient and turbulence kinetic energy flow of air, the differential equations which describe the flow were approximated by the finite volumes method for two dimensional, by using commercial software package (FLUENT) with standard of k-ε model two dimensions turbulence flow.
... Show MoreThe risk assessment for three pipelines belonging to the Basra Oil Company (X1, X2, X3), to develop an appropriate risk mitigation plan for each pipeline to address all high risks. Corrosion risks were assessed using a 5 * 5 matrix. Now, the risk assessment for X1 showed that the POF for internal corrosion is 5, which means that its risk is high due to salinity and the presence of CO, H2S and POF for external corrosion is 1 less than the corrosion, while for Flowline X2 the probability of internal corrosion is 4 and external is 4 because there is no Cathodic protection applied due to CO2, H2S and Flowline X3 have 8 leaks due to internal corrosion so the hazard rating was very high 5 and could be due to salinity, CO2, fluid flow rate
... Show MoreConjugate heat transfer has significant implications on heat transfer characteristics, particularly in thick wall applications and small diameter pipes. In this study, a three-dimensional numerical investigation was carried out using commercial CFD software “ANSYS FLUENT” to study the influence of conjugate heat transfer of laminar flow in mini channels at constant heat flux wall conditions. Two parameters were studied and analyzed: the wall thickness and thermal conductivity and their effect on heat transfer characteristics such as temperature profile and Nusselt number. Thermal conductivity of (0.25, 10, 202, and 387) W/m2C and wall thickness of (1, 5, and 50) mm were used for a channel of (1*2) mm cross
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Research topic: (The Epistemological Foundations for Comparison of Religions by al-Amiri)
The research sought to study the topic with: a descriptive methodology by investigating the components of al-Amiri's approach to the interfaith comparison. And analytical, by showing the applied perception of an objective model in the comparison of religions to answer two questions: What are the cognitive foundations of al-Amiri? And what is his approach to establishing an objective comparison between religions?
The research started by introducing Abu al-Hassan al-Amiri, and then presented four topics: An introduction to al-Amiri's efforts in the interfaith comparison, his knowledge foundations, an applied model
... Show MoreThe research aims to verify the presence of correlation between the financial cycle and the economic cycle in the Iraqi economy. During the research period, the Iraqi economy witnessed a crisis cycle due to the permanent deviations in the state budget and the occurrence of sustainable deficits during the period of the economic blockade with the coincidence of an inflationary economic cycle due to the cheap money policy and the occurrence of Negative supply shocks due to the blockade and the madly high general level of prices, in addition to the weak financial planning of the state budget during the post-economic blockade and the entry of the economy into a stagnant economic cycle due to security and political instability, low levels of priv
... Show MoreThe need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien
... Show MorePeak ground acceleration (PGA) is one of the critical factors that affect the determination of earthquake intensity. PGA is generally utilized to describe ground motion in a particular zone and is able to efficiently predict the parameters of site ground motion for the design of engineering structures. Therefore, novel models are developed to forecast PGA in the case of the Iraqi database, which utilizes the particle swarm optimization (PSO) approach. A data set of 187 historical ground-motion recordings in Iraq’s tectonic regions was used to build the explicit proposed models. The proposed PGA models relate to different seismic parameters, including the magnitude of the earthquake (Mw), average shear-wave velocity (VS30), focal depth (FD
... Show MoreThe method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par
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