In petroleum reservoir engineering, history matching refers to the calibration process in which a reservoir simulation model is validated through matching simulation outputs with the measurement of observed data. A traditional history matching technique is performed manually by engineering in which the most uncertain observed parameters are changed until a satisfactory match is obtained between the generated model and historical information. This study focuses on step by step and trial and error history matching of the Mishrif reservoir to constrain the appropriate simulated model. Up to 1 January 2021, Buzurgan Oilfield, which has eighty-five producers and sixteen injectors and has been under production for 45 years when it started

The simulation of passively Q-switching is four non – linear first order differential equations. The optimization of passively Q-switching simulation was carried out using the constrained Rosenbrock technique. The maximization option in this technique was utilized to the fourth equation as an objective function; the parameters, γa, γc and β as were dealt with as decision variables. A FORTRAN program was written to determine the optimum values of the decision variables through the simulation of the four coupled equations, for ruby laser Q–switched by Dy +2: CaF2.For different Dy +2:CaF2 molecules number, the values of decision variables was predicted using our written program. The relaxation time of Dy +2: CaF2, used with ruby was

This research paper attempts to explore problems facing the teaching of written expression among first-year female university students. The focal point behind conducting this research is to show the importance that writing is taking as a skill in learning the language. To achieve this goal, the researcher prepared a questionnaire consisting of 20 items. The sample, whose size is 60 participants, was selected randomly from the department of Arabic, College of Education for Women, University of Baghdad. Through the use of a set of statistical means including weighting means and percentage, the findings revealed that the students face many difficulties in learning writing. The researcher suggested some recommendations, mainly improving the

Low- and medium-carbon structural steel components face random vibration and dynamic loads (like earthquakes) in many applications. Thus a modification to improve their mechanical properties, essentially damping properties, is required. The present study focuses on improving and developing these properties, significantly dampening properties, without losing the other mechanical properties. The specimens used in the present study are structural steel ribbed bar ISO 6935 subjected to heating temperatures of (850, 950, and 1050) ˚C, and cooling schemes of annealing, normalizing, sand, and quenching was selected. The damping properties of the specimens were measured experimentally with the area under the curve for the loadi

In this research the effect of cooling rate and mold type on mechanical properties of the eutectic
and hypoeutectic (Al-Si) alloys has been studied. The alloys used in this research work were (Al- 12.6%Si
alloy) and (Al- 7%Si alloy).The two alloys have been melted and poured in two types of molds with
different cooling rates. One of them was a sand mold and the other was metal mold. Mechanical tests
(hardness, tensile test and impact test) were carried out on the specimens. Also the metallographic
examination was performed.
It has been found that the values of hardness for the alloys(Al-12.6%Si and Al-7%Si) which poured in
metal mold is greater than the values of hardness for the same alloy when it poured in a heated

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

he paper presents the results of exposure of normal concrete to high temperatures (400 and 700°C). In addition to the exposure of steel reinforcement bar Ø 12 mm, where two types of steel reinforcement burning situations were performed. Directly exposed to high temperatures (400 and 700°C) and others were covered by concrete layer (15 mm). From the experimental results of fire exposure for 1 hour of 400 and 700°C and gradually cooled, it was found that the residual average percentage of compressive strength of concrete was 85.3 and 41.4%, while the residual average percentage of modulus of elasticity of concrete was 75 and 48%, respectively. The residual average percentage of yielding tensile stress (Ø 12 mm) after burning and cooling