The estimation of the initial oil in place is a crucial topic in the period of exploration, appraisal, and development of the reservoir. In the current work, two conventional methods were used to determine the Initial Oil in Place. These two methods are a volumetric method and a reservoir simulation method. Moreover, each method requires a type of data whereet al the volumetric method depends on geological, core, well log and petrophysical properties data while the reservoir simulation method also needs capillary pressure versus water saturation, fluid production and static pressure data for all active wells at the Mishrif reservoir. The petrophysical properties for the studied reservoir is calculated using neural network technique

The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting

Ad-Hoc Networks are a generation of networks that are truly wireless, and can be easily constructed without any operator. There are protocols for management of these networks, in which the effectiveness and the important elements in these networks are the Quality of Service (QoS). In this work the evaluation of QoS performance of MANETs is done by comparing the results of using AODV, DSR, OLSR and TORA routing protocols using the Op-Net Modeler, then conduct an extensive set of performance experiments for these protocols with a wide variety of settings. The results show that the best protocol depends on QoS using two types of applications (+ve and –ve QoS in the FIS evaluation). QoS of the protocol varies from one prot

This work studied the facilitation of the transportation of Sharqi Baghdad heavy crude oil characterized with high viscosity 51.6 cSt at 40 °C, low API 18.8, and high asphaltenes content 7.1 wt.%, by reducing its viscosity from break down asphaltene agglomerates using different types of hydrocarbon and oxygenated polar solvents such as toluene, methanol, mix xylenes, and reformate. The best results are obtained by using methanol because it owns a high efficiency to reduce viscosity of crude oil to 21.1 cSt at 40 °C. Toluene, xylenes and reformate decreased viscosity to 25.3, 27.5 and 28,4 cSt at 40 °C, respectively. Asphaltenes content decreased to 4.2 wt. % by using toluene at 110 °C. And best improvement in API of the heavy crude o

The current investigation examines the combined impacts of ultrasonic radiation and hydrogen donors on the viscosity of heavy crude oil. The impact of exposure time, power, duty cycle, and temperature on the viscosity of Iraqi heavy crude oil with 20.32 API was studied. Also, the viscosity of the oil samples, which were mixed with a hydrogen donor (decalin) and subjected to ultrasonic treatment under optimal conditions, was examined to evaluate the combined impact of ultrasonic radiation and hydrogen donor on the viscosity of crude oil. The viscosity experienced a decrease of 52.34% at 2 min of irradiation, 360 W ultrasonic power, 0.8 duty cycle, 35 ⁰C, and 8vol% decalin. To validate the outcomes of the experiments, asphaltene content, s

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as com

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as