Nasryia oil field is located about 38 Km to the north-west of Nasryia city. The field was discovered in 1975 after doing seismic by Iraqi national oil company. Mishrif formation is a carbonate rock (Limestone and Dolomite) and its thickness reach to 170m. The main reservoir is the lower Mishrif (MB) layer which has medium permeability (3.5-100) md and good porosity (10-25) %. Form well logging interpretation, it has been confirmed the rock type of Mishrif formation as carbonate rock. A ten meter shale layer is separating the MA from MB layer. Environmental corrections had been applied on well logs to use the corrected one in the analysis. The combination of Neutron-Density porosity has been chosen for interpretation as it is c

IMPLICATION OF GEOMECHANICAL EVALUATION ON TIGHT RESERVOIR DEVELOPMENT / SADI RESERVOIR HALFAYA OIL FIELD

Flow unit and reservoir rock type identification in carbonates are difficult due to the intricacy of pore networks caused by facies changes and diagenetic processes. On the other hand, these classifications of rock type are necessary for understanding a reservoir and predicting its production performance in the face of any activity. The current study focuses on rock type and flow unit classification for the Mishrif reservoir in Iraq's southeast and the study is based on data from five wells that penetrate it. Integration of several methods was used to determine the flow unit based on well log interpretation and petrophysical properties. The flow units were identified using the Quality Index of Rock and the Indicator of Flow Zone. Th

الوصف Mixed ligand complexes of Cu (II), Co (II) and Zn (II) with 2-((4-(1-(4-chlorophenylimino) ethyl) phenylimino) methyl) phenol (L) and histidine (His) have been prepared and diagnosed by ¹H and13 C NMR, FT-IR and electronic spectral data, thermal gravimetric, molar conductance and metal analysis measurements. The ligand (L) shows a bidentate nature and the coordination occurs through N and O atoms of imine group and phenol group respectively whereas (His) behave as tridentate ligand, coordinating through the-NH2 group and carboxylate oxygen group and N atoms of imidazole ring. The analytical studies for three complexes have shown octahedral structure. The anticancer activity was screened against human cancer cell such Follicular

Passive optical network (PON) is a point to multipoint, bidirectional, high rate optical network for data communication. Different standards of PONs are being implemented, first of all PON was ATM PON (APON) which evolved in Broadband PON (BPON). The two major types are Ethernet PON (EPON) and Gigabit passive optical network (GPON). PON with these different standards is called xPON. To have an efficient performance for the last two standards of PON, some important issues will considered. In our work we will integrate a network with different queuing models such M/M/1 and M/M/m model. After analyzing IPACT as a DBA scheme for this integrated network, we modulate cycle time, traffic load, throughput, utilization and overall delay

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

Wireless Multimedia Sensor Networks (WMSNs) are networks of wirelessly interconnected sensor nodes equipped with multimedia devices, such as cameras and microphones. Thus a WMSN will have the capability to transmit multimedia data, such as video and audio streams, still images, and scalar data from the environment. Most applications of WMSNs require the delivery of multimedia information with a certain level of Quality of Service (QoS). This is a challenging task because multimedia applications typically produce huge volumes of data requiring high transmission rates and extensive processing; the high data transmission rate of WMSNs usually leads to congestion, which in turn reduces the Quality of Service (QoS) of multimedia applications. To